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Cooperative Task Assignment and Trajectory Planning of Unmanned Systems Via HFLC and PSO
Abstract
This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target’s degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.
... In [31], the authors introduced a new idea that polynomial interpolation function was combined with improved cuckoo search algorithm to generate time-optimal trajectory for UR robot. In [32]- [34] a hybrid approach of control parametrization and time discretization (CPTD) and PSO was proposed to solve the optimization problem of trajectory planning. However, the three literatures focused on that the time was divided equally, the control input was close to the piecewise constant value, and the kinematic model was two-dimensional. ...
... Besides, comparing with the single-phase method (in this method the task is treated as a single process), we proposed a multi-phase method (two trajectory points as a phase to generate the trajectory) which makes it easier to obtain satisfactory solution. Also, comparing with the CPTD in [32]- [34], the trajectory generated by the proposed multi-phase method is much smoother in theory. ...
... To evaluate our proposed method, we adopt two different methods to make the simulation. One is the method we proposed which has been introduced, and the other is the method in literatures [32]- [34] (CPTD). The related results of comparison are shown in Figs. ...
Multi-constraint trajectory planning for unmanned aerial vehicles (UAVs) has been widely used in military and civil fields. The existing path planning methods, such as swarm intelligence algorithm and graph-based algorithm, cannot incorporate the flying time and UAV kinematic model into evolution. To overcome such disadvantage, a method of solving trajectory planning under obstacles and multi-constraint is investigated in this paper. Firstly, the flying time is discretized as a certain number of Chebyshev points which are the optimized moments of control variable, and they can reduce the computational burden. The process of solution is divided into multi-phase, i.e., two points as a phase to generate the trajectory. Then, angular velocity is taken as control variable, and function of angular velocity is solved by cubic spline interpolation. Besides, functions of angle and position are obtained by integration. The results are substituted into the model consisted by particle swarm optimization (PSO) and the UAV kinematic model to optimize. On this basis, the angular velocity, angle and position are calculated according to the allocated moments. Finally, Monte-Carlo simulation and comparison with existed method are carried out in obstacle environment. All the results illustrate that the multi-phase method can calculate the kinematic parameters of UAV accurately and plan smooth trajectories. Meanwhile, the proposed method is easier to meet the complicated constraints than the single-phase method. In addition, the dimensionality of solution is also enriched effectively.
... The cooperative path planning of multi-UAVs is the crucial technology of UAVs cooperative 25 combat mission planning system [1][2][3][4][5]. The constraints of multi-UAVs collaborative path planning 26 not only consider the physical performance and the mission requirements of a single UAV, but also 27 the cooperative relationship among all UAVs is taken into account, which includes achieving the 28 2 target constraint at the same time and the minimum safe distance constraint between multiple UAVs 29 [6-8]. ...
... where, 1 V is the speed of the leader UAV, 1 R is the turning radius of the leader UAV, g is the 250 gravitational acceleration, and 1max n is the maximum normal acceleration of the leader UAV. We can 251 obtain the minimum transition radius of the leader UAV flight, which shall meet the following 252 requirements: The leader UAV is only considered that path optimization of multi-QUAVs formation studied in 278 this paper, and the followed UAV exists as a constraint condition. ...
... In this way, the whole optimal control problem is put into the solving framework of nonlinear 384 programming. [ 1,1] − , so the time t is converted as follows: [ 1,1], . ...
The formation of multiple quadrotor UAVs with long navigation will encounter many flight constraints, so it is necessary to change the formation to avoid these constraints. In this paper, Voronoi graph theory is used to treat UAVs formation as a rigid body. The rigid body structure can be changed by changing the formation before different constraints, after passing constraints the rigid body structure remains the formation unchanged or form into the prescribed formation. The time-varying model is established to facilitate the use of optimization. Based on Gauss pseudospectral method (GPM), the path optimization of a single quadrotor UAV is carried out. The followed UAVs in formation are treated as constraints. The constraints of maximum turning radius and formation transformation time of other UAVs are considered in the optimization process. The minimum time required for formation transformation is optimized to solve the transformation optimization, and the performance index of trajectory optimization is to minimize the energy consumed by the leader quadrotor UAV within the specified time. Finally, the simulation proves the method proposed in this paper.
... In these works, only path planning, in order to localize the targets, is studied, and task planning and tracking the detected targets are not pointed out. On the other hand, in Hafez and Kamel (2019), only the task allocation problem (but not the detection and tracking missions) is considered in cooperative missions. Moreover, cooperative rescue operations in cases of emergencies or catastrophes are considered in Wang et al. (2023). ...
In this paper, the problem of cooperative search and tracking by multiple flying vehicles is studied. An algorithm is proposed based on optimal planning by minimizing a suitably defined cost function. In order to define the cost function, three maps are proposed: uncertainty map, detection probability map, and emitter map. In the uncertainty map, the geometrical status of the vehicle with respect to the region is considered and its suitability is measured. The detection probability map is defined such that the group of vehicles can detect new emitters that appear in their sensors’ coverage. Finally, the emitter map is a tool to keep tracking the detected emitters with acceptable error while searching the region. The assumption that the vehicles are equipped with only one commonly used radar range indicator sensor makes the proposed algorithms more applicable in the real world for a cost-effective system. The efficiency of the proposed method from the search, detection, and tracking points of view is verified through simulations.
... PSO [4] is a population-based non-gradient stochastic optimization algorithm inspired by the social behavior of birds and fish, which has been wildly leveraged to resolve complex optimization problems, comprising power dispatch [6], path planning [10], localization [11], etc. ...
Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing static path planning problems. Nevertheless, such application on dynamic scenarios has been severely precluded by PSO's low computational efficiency and premature convergence downsides. To address these limitations, we proposed a Tensor Operation Form (TOF) that converts particle-wise manipulations to tensor operations, thereby enhancing computational efficiency. Harnessing the computational advantage of TOF, a variant of PSO, designated as Self-Evolving Particle Swarm Optimization (SEPSO) was developed. The SEPSO is underpinned by a novel Hierarchical Self-Evolving Framework (HSEF) that enables autonomous optimization of its own hyper-parameters to evade premature convergence. Additionally, a Priori Initialization (PI) mechanism and an Auto Truncation (AT) mechanism that substantially elevates the real-time performance of SEPSO on dynamic path planning problems were introduced. Comprehensive experiments on four widely used benchmark optimization functions have been initially conducted to corroborate the validity of SEPSO. Following this, a dynamic simulation environment that encompasses moving start/target points and dynamic/static obstacles was employed to assess the effectiveness of SEPSO on the dynamic path planning problem. Simulation results exhibit that the proposed SEPSO is capable of generating superior paths with considerably better real-time performance (67 path planning computations per second in a regular desktop computer) in contrast to alternative methods. The code of this paper can be accessed here.
... At present, scholars have conducted a large body of research on task allocation and have achieved a series of results. The related research works mainly use intelligent algorithms (ant colony algorithms [12][13][14][15], genetic algorithms [16][17][18], particle swarm algorithms [19][20][21][22], artificial neural networks [23], etc.), and contract network algorithms [24][25][26] to solve task coordination problems in a distributed architecture. Considering long-term benefits and current benefits, [27] proposes a distributed deep compression algorithm, and a distributed quick compression algorithm and adopts a distributed framework to complete task optimization. ...
With the rapid changes in the battlefield situation, the requirement of time for UAV groups to deal with complex tasks is getting higher, which puts forward higher requirements for the dynamic allocation of the UAV group. However, most of the existing methods focus on task pre-allocation, and the research on dynamic task allocation technology during task execution is not sufficient. Aiming at the high real-time requirement of the multi-UAV collaborative dynamic task allocation problem, this paper introduces the market auction mechanism to design a discrete particle swarm algorithm based on particle quality clustering by a hybrid architecture. The particle subpopulations are dynamically divided based on particle quality, which changes the topology of the algorithm. The market auction mechanism is introduced during particle initialization and task coordination to build high-quality particles. The algorithm is verified by constructing two emergencies of UAV sudden failure and a new emergency task.
... However, the task allocation scheme has weak practicability. Considering the coupling between task allocation and path planning, it is feasible to adopt the integrated solving methods for USCMP [8,9]. By taking the path length of UAVs as a variable of the task allocation cost function, the flight paths meeting the restrictions on mobility of UAVs and the task allocation scheme can be generated simultaneously [10]. ...
Cooperative search-attack is an important application of unmanned aerial vehicle (UAV) swarm in military field. The coupling between path planning and task allocation, the heterogeneity of UAVs, and the dynamic nature of task environment greatly increase the complexity and difficulty of the UAV swarm cooperative search-attack mission planning problem. Inspired by the collaborative hunting behavior of wolf pack, a distributed self-organizing method for UAV swarm search-attack mission planning is proposed. First, to solve the multi-target search problem in unknown environments, a wolf scouting behavior-inspired cooperative search algorithm for UAV swarm is designed. Second, a distributed self-organizing task allocation algorithm for UAV swarm cooperative attacking of targets is proposed by analyzing the flexible labor division behavior of wolves. By abstracting the UAV as a simple artificial wolf agent, the flexible motion planning and group task coordinating for UAV swarm can be realized by self-organizing. The effectiveness of the proposed method is verified by a set of simulation experiments, the stability and scalability are evaluated, and the integrated solution for the coupled path planning and task allocation problems for the UAV swarm cooperative search-attack task can be well performed.
... The cooperative path planning of multi-UAVs is the crucial technology of UAVs' cooperative combat mission planning system [1][2][3][4][5]. The problem of constraints of multi-UAVs collaborative path planning needs to concern both the physical performance of a single UAV and the cooperative relationship among all UAVs. ...
The formation of multi-quadrotor UAVs (QUAVs) in long flights will encounter many constraints and obstacles in the flight process, so it is necessary to change the formation shape to avoid these constraints. When the flight path of QUAVs is individually optimized, multiple individual dynamic problems will be faced, making the solution complicated and spending a long time. In this paper, as a whole instead of individuals considering the QUAVs, the formation is regarded as a rigid body using the Voronoi graph theory method in the flight process. The constraints of the followed QUAVs are transformed into the constraints of leader QUAV. Therefore, the formation is transformed for different constraints by changing the virtual rigid body structure or shape. A time-varying model is established to facilitate the use of optimization. The energy consumption of the leader UAV within the specified time is minimized as the trajectory optimization objective. The optimization improvement of the end heading and height constraints is proposed. A trajectory optimization method for the leader QUAV based on Gauss pseudospectral method is proposed, transforming the original optimal control problem into the nonlinear programming one. The simulation results demonstrate and verify the proposed method.
... Yuan et al. [13] improved the robustness and convergence speed of the algorithm by constructing a geometric space structure to guide the particle swarm motion. Hafez et al. [14] proposed a method that combines control parameterization and time discretization with particle swarm optimization to solve the trajectory optimization problem. Song et al. [15] proposed a new multimodal delayed particle swarm optimization algorithm that reduces the occurrence of local convergence of the trajectory planning and improves the trajectory planning performance. ...
Aiming at addressing the problems of short battery life, low payload and unmeasured load ratio of logistics Unmanned Aerial Vehicles (UAVs), the Radial Basis Function (RBF) neural network was trained with the flight data of logistics UAV from the Internet of Things to predict the flight status of logistics UAVs. Under the condition that there are few available input samples and the convergence of RBF neural network is not accurate, a dynamic adjustment method of RBF neural network structure based on information entropy is proposed. This method calculates the information entropy of hidden layer neurons and output layer neurons, and quantifies the output information of hidden layer neurons and the interaction information between hidden layer neurons and output layer neurons. The structural design and optimization of RBF neural network were solved by increasing the hidden layer neurons or disconnecting unnecessary connections, according to the connection strength between neurons. The steepest descent learning algorithm was used to correct the parameters of the network structure to ensure the convergence accuracy of the RBF neural network. By predicting the regression values of the flight status of logistics UAVs, it is demonstrated that the information entropy-based RBF neural network proposed in this paper has good approximation ability for the prediction of nonlinear systems.
... 1) The effect of Agent on Task (A AT − − → T ) : Each agent in HACS might differ in terms of dynamics, and/or sensing and computation capabilities, which leads to its different performance in different tasks. For example, some agents are skilled at acquiring task information, whereas others may only be capable of pick-and-place tasks [80]. These different (heterogeneous) agents are required by the complexity and diversity of task and motion planning to collaborate with each other to complete complicated tasks. ...
The human-agent collaboration (HAC) is a prospective research topic whose great applications and future scenarios have attracted vast attention. In a broad sense, the HAC system (HACS) can be broken down into six elements: "Man," "Agents," "Goal," "Network," "Environment," and "Tasks." By merging these elements and building a relation graph, this article proposes a systematic analysis framework for HACS, and attempts to make a comprehensive analysis of these elements and their relationships. We coin the abbreviation "MAGNET" to name the framework by stringing together the initials of the above six terms. The framework provides novel insights into analyzing various HAC patterns and integrates different types of HACSs in a unifying way. The presentation of the HACS framework is divided into two parts. This article, part I, presents the systematic analysis framework. Part II proposes a normalized two-stage top-level design procedure for designing an HACS from the perspective of MAGNET.
... Therefore, hybrid control algorithms can be seen as a promising field. An example of hybrid algorithm is the work presented in Hafez and Kamel (2019) , where a combination of hierarchical fuzzy logic controller (HFLC) and PSO is proposed to solve the problems of task assignment and trajectory re-planning of multiple UAVs; 3. Platoon formation : It is an important approach especially for transportation systems. Nevertheless, very few works deal with the UGV platooning ( Klan čar, Matko, & Blaži č, 2009; 2011 ); 4. Towards more real applications : New promising applications can be considered for formation control of UGVs. ...
Recently, multiple unmanned vehicles have attracted a great deal of attention as viable solutions to a wide variety of civilian and military applications. Among many topics in the field of multiple unmanned systems, formation control and coordination is of great importance. This paper presents a comprehensive literature review on the strategies and methodologies applied for formation control of multiple unmanned ground vehicles in both normal and faulty situations. First, the basic definitions of formation control and coordination are provided as well as their classification. Second, a comprehensive literature review of formation control strategies is introduced. Moreover, an overview on fault detection and diagnosis and fault-tolerant cooperative control of UGVs is presented. Finally, open problems, challenges, and future directions are highlighted.
Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing static path planning problems. Nevertheless, such application on dynamic scenarios has been severely precluded by PSO’s low computational efficiency and premature convergence downsides. To address these limitations, we proposed a Tensor Operation Form (TOF) that converts particle-wise manipulations to tensor operations, thereby enhancing computational efficiency. Harnessing the computational advantage of TOF, a variant of PSO, designated as Self-Evolving Particle Swarm Optimization (SEPSO) was developed. The SEPSO is underpinned by a novel Hierarchical Self-Evolving Framework (HSEF) that enables autonomous optimization of its own hyper-parameters to evade premature convergence. Additionally, a Priori Initialization (PI) mechanism and an Auto Truncation (AT) mechanism that substantially elevates the real-time performance of SEPSO on dynamic path planning problems were introduced. Comprehensive experiments on four widely used benchmark optimization functions have been initially conducted to corroborate the validity of SEPSO. Following this, a dynamic simulation environment that encompasses moving start/target points and dynamic/static obstacles was employed to assess the effectiveness of SEPSO on the dynamic path planning problem. Simulation results exhibit that the proposed SEPSO is capable of generating superior paths with considerably better real-time performance (67 path planning computations per second in a regular desktop computer) in contrast to alternative methods. The code and video of this paper can be accessed here [Code and Video: https://github.com/XinJingHao/Real-time-Path-planning-with-SEPSO ].
In this paper, a task assignment method is proposed to deal with the multi-agent pursuit-evade game for heterogeneous unnamed aerial vehicles via reinforcement learning. The mathematical model based on the local position error dynamics is established to describe the interactions among the vehicles in the pursuit-evade game, subject to high nonlinearities and parameter uncertainties involved in the vehicle model. The execution costs and the corresponding optimal control policies of the agent pursuing each target are calculated, and the policy with minimum execution cost is determined as the objective of the multi-agent pursuit-evade game. Min-Max strategy is introduced to estimate and counteract the interaction effects in the mathematical model, and the reinforcement learning-based algorithm is proposed to obtain the optimal solution to the assignment problem based on the Hamilton–Jacobi–Bellman equation without the interaction effects. Simulation results are given to show the effectiveness of the proposed task assignment method.
The occupancy guidance of Unmanned Aerial Vehicle (UAV) is one of the inevitable stages in the future air combat. Considering both constraints of missile launch condition and UAV flight performance, this paper established a multi-objective optimization model of occupancy guidance for UAV autonomous pursuit of enemy aircraft. The distance, angle and speed of occupancy guidance state are used as optimization variables to construct the advantage evaluation function. Based on the traditional heuristic algorithm, the Gradient Descent-Truncated Symbiotic Organizations Search (GDT-SOS) is designed to achieve rapid convergence of fitness values. By both numerical simulation and field test, the experiments demonstrated that UAV can autonomously achieve continuous, rapid and advantageous occupancy guidance under different dynamic initial conditions, which verifies that GDT-SOS has more effective than the comparison algorithm.
Evolutionary search has been widely implemented for the adjustment of controllers' parameters. Nevertheless, the structure of controllers, which has a more important role in control systems, has been seldom studied. To this end, an evolutionary design method of controllers is proposed to optimize both structures and parameters simultaneously in this paper. A controller is made up of a combination of some basic controller components and relevant parameters. The design of controllers can be transformed into an optimization problem involving the structure (represented by discrete vectors) and parameters (represented by real numbers). A generalized structure encoding/decoding scheme is developed. Guided by the performance indices, intelligent algorithms for both combinatorial and numerical optimization are employed to iteratively and cooperatively evolve the controller structure and parameters, respectively. In order to effectively reduce some redundant or infeasible solutions, a set of generation rules for the controller structure are put forward, which also ensures the feasibility of the structure. Furthermore, this method is applied to a magnetic levitation ball system with nonlinear dynamics and external disturbance. Both simulation and experiment results demonstrate the effectiveness and practicability of the proposed method.
In the yester years, drones or unmanned aerial vehicle (UAV) has stayed an attention to consider on with regard to associating innovation, security concerns, rules, and guidelines all inclusive. Wonderful progressions and utilizations in photogrammetry and remote sensing applications in multi-cloud region are justified. Moving right along, classification, hardware design, path planning, and challenges are also dealt with. Coverage path planning entitles figuring out the route that covers every location on an aspired area of interest. Exploration and analyzation of the already existing studies of literature in terms of various approaches being employed in coverage path planning problems are the key aim of this paper. Multi-cloud online processing of UAV captured data is a striking selection to real-time data processing, as it accommodates for the observation and examination of data from an assortment of sources, this makes the accessibility of data between devices much more seamless and easier to plan. The path planning techniques on UAVs categorized into three solid methods of classifications them being the following, representative, cooperative, and non-cooperative methods. What’s more, various open examination issues dependent on UAVs path planning and multi-cloud security are investigated to give profound bits of knowledge.
Many real-world optimization problems involve multiple conflicting objectives. Such problems are called multiobjective optimization problems (MOPs). Typically, MOPs have a set of so-called Pareto optimal solutions rather than one unique optimal solution. To assist the decision maker (DM) in finding his/her most preferred solution, we propose an interactive multiobjective evolutionary algorithm (MOEA) called iDMOEA-εC, which utilizes the DM’s preferences to compress the objective space directly and progressively for identifying the DM’s preferred region. The proposed algorithm employs a state-of-the-art decomposition-based MOEA called DMOEA-εC as the search engine to search for solutions. DMOEA-εC decomposes an MOP into a series of scalar constrained subproblems using a set of evenly distributed upper bound vectors to approximate the entire Pareto front. To guide the population toward only the DM’s preferred part on the Pareto front, an adaptive adjustment mechanism of the upper bound vectors and two-level feasibility rules are proposed and integrated into DMOEA-εC to control the spread of the population. To ease the DM’s burden, only a small set of representative solutions is presented in each interaction to the DM, who is expected to specify a preferred one from the set. Furthermore, the proposed algorithm includes a two-stage selection procedure, allowing to elicit the DM’s preferences as accurately as possible. To evaluate the performance of the proposed algorithm, it was compared with other interactive MOEAs in a series of experiments. The experimental results demonstrated the superiority of iDMOEA-εC over its competitors.
Unmanned aerial systems provide many applications with the ability to perform flying tasks autonomously, and hence have received significant research and commercial attention in the past decade. One of the most popular unmanned aerial platforms for such tasks is the small-scale rotorcraft with multiple rotors, commonly known as multicopters. In order for these platforms to perform fully autonomous missions and tasks, they require a sophisticated low-level flight control system that is integrated with advanced task and motion planning modules, which combine together to form the complete unmanned aerial system (UAS). In this paper, the planning module of unmanned multicopter systems is discussed in detail, and a comprehensive survey on techniques for both motion and task planning reported in literature and by the Unmanned Systems Research Group at the National University of Singapore is presented.
In multi-agent systems (MAS), the coalition formation (CF) is an important problem focusing on allocating agents to different tasks. In this paper, three specific CF problems are considered, including the single-task single-coalition formation, the multi-task single-coalition formation, and the multi-task multi-coalition formation. The mathematical models of these three specific problems are formulated with the objective of minimizing the total cost while satisfying the ability requirement constraint. An efficient genetic algorithm with heuristic initialization and repair strategy (GAHIR) is proposed to solve the CF problem. Multiple initialization and repair methods, which utilize the prior knowledge of the specific problems, are proposed to improve the solution quality. Then, these methods are tested to prove their effectiveness. Finally, a comparison experiment about the proposed algorithm against several advanced algorithms is constructed. The results of statistical analysis by the Wilcoxon rank-sum test demonstrate that the proposed GAHIR can obtain better coalition schemes than its competitors in solving the CF problems. Furthermore, GAHIR has faster convergence speed in most instances.
This article investigates the task planning problem where one vehicle needs to visit a set of target locations while respecting the precedence constraints that specify the sequence orders to visit the targets. The objective is to minimize the vehicle's total travel distance to visit all the targets while satisfying all the precedence constraints. We show that the optimization problem is NP-hard, and consequently, to measure the proximity of a suboptimal solution from the optimal, a lower bound on the optimal solution is constructed based on the graph theory. Then, inspired by the existing topological sorting techniques, a new topological sorting strategy is proposed; in addition, facilitated by the sorting, we propose several heuristic algorithms to solve the task planning problem. The numerical experiments show that the designed algorithms can quickly lead to satisfying solutions and have better performance in comparison with popular genetic algorithms.
Teams of cooperative Unmanned Aerial Vehicles (UAVs) require intelligent and flexible control strategies to allow the accomplishment of a multitude of challenging group tasks. In this paper, we introduce a solution for the problem of tactic switching between the formation flight tactic and the dynamic encirclement tactic for a team of í µí± cooperative UAVs using a predictive decentralize control approach. Decentralized Model Predictive Control (MPC) is used to generate tactics for a team of í µí± UAVs in simulation and real-world validation. A high-level Linear Model Predictive Control (LMPC) policy is used to control the UAV team during the execution of a desired formation, while a combination of decentralized LMPC and Feedback Linearization (FL) is applied to the UAV team to accomplish dynamic encirclement. The decision of switching from one tactic to the other is derived by a fuzzy logic controller, which, in its turn, is derived by a Reinforcement Learning (RL) approach. The main contributions of this paper are: (i) solution of the problem of tactic switching for a team of cooperative UAVs using LMPC and a fuzzy controller derived via RL; (ii) simulations demonstrating the efficiency of the method; and (iii) implementation of the solution to on-board real-time controllers on Qball-X4 quadrotors.
This paper investigates forest fires fighting application using team(s) of unmanned aerial vehicles (UAVs), in view of UAVs having great advantages in performing such tasks. However, important challenges in fire fighting missions in general are to perform the task with high performance in minimum time. In this paper, it is assumed that the fire spots are already detected and their coordinates will be sent to the fire fighting UAVs teams. Once the fire fighting team(s) receive relevant information, the team begins to solve the task assignment problem using the auction-based algorithm. The objective of the algorithm is to assign each UAV to each fire spot according to their relative distances, to minimize the distance traveled between each UAV's initial position and its assigned fire spot. Then, each UAV will optimally plan its path to its assigned fire spot by using particle swarm optimization (PSO) algorithm. The proposed algorithm calculates the optimal control inputs while taking into consideration the control inputs constraints while avoiding potential UAVs collisions during motion.
For the purpose of remote command and situation awareness, multiple unmanned aerial vehicles (UAVs) cooperative surveillance with a ground station via multihop communications is presented in this paper. Considering limited communication capacities, a reliable UAV-to-UAV communication relay chain is dynamically established for connectivity maintenance and real-time surveillance information transmission. Firstly, a multiple UAVs cooperative surveillance framework is constructed with history detection information and surveillance payoff estimation. Secondly, four attributes are proposed to characterize differences among UAV alternatives in communication network containing a ground station, and a novel multiple relay UAVs selection scheme based on fuzzy optimum selection is developed to achieve tradeoff between surveillance mission and connectivity maintenance. Furthermore, satisfied with collision avoidance, limited communication and UAV kinematic constraints, the optimal UAV motion plan is obtained by decentralized receding horizon control, which is solved by particle swarm optimization with elite mechanism. Simulations demonstrate the effectiveness of the proposed methods in multi UAVs cooperative surveillance.
This paper investigates fault-tolerant cooperative control (FTCC) of multiple wheeled mobile robots (WMRs) in the presence of severe actuator faults. Initially, a team of robots is moving in pre-defined formation configuration. When actuator faults occur in one or more robots, and the faulty robot(s) cannot complete the mission, the rest of robots start reconfiguring the formation to compensate the fault effect on the whole mission. First, the new formation reconfiguration is generated by solving an optimal assignment problem where each healthy robot should be assigned to a unique place. Then, the new formation can be reconfigured by recasting the reconfiguration problem as an optimization problem, while the objective is to minimize the time to achieve the new formation reconfiguration within the constraints of the robots' dynamics and collision avoidance. A hybrid approach of control parametrization and time discretization (CPTD) and particle swarm optimization (PSO) is proposed to solve the optimization problem. The results of the numerical simulations demonstrate the effectiveness of the proposed algorithm.
In this paper, we investigate the problem of fault tolerant for a group of multiple autonomous cooperative unmanned aerial vehicles (UAVs) during execution their mission in a desired geometrical pattern. A decentralized linear model predictive control (LMPC) is designed and implemented on a team of cooperative UAVs to achieve the desired formation in the absence of faults (normal/fault-free cases). When faults occur in one or more of the UAVs, a fault-tolerant cooperative control (FTCC) strategy is designed via fuzzy logic such that each UAV in the team will take its own decision in a decentralized manner, to reconfigure the formation of the whole team ensuring the execution of the required mission. The proposed controller respects the general formation constraints known as Reynolds rules of flocking during simulations. Our main contribution in this paper lays in the use of fuzzy logic control by the cooperative UAVs in taking the decision to solve the problem of formation reconfiguration for an autonomous team of UAVs in the presences of faults.
This paper introduces an approach to solve the task assignment problem for a large number of tasks and robots in an efficient time. This method reduces the size of the state space explored by partitioning the tasks to the number of robotic agents. The proposed method is divided into three stages: first, the tasks are partitioned to the number of robots, then robots are being assigned to the clusters optimally, and finally a task assignment algorithm is executed individually at each cluster. Two methods are adopted to solve the task assignment at each cluster, a genetic algorithm, and an imitation learning algorithm. To verify the performance of the proposed approach, several numerical simulations are performed. Our empirical evaluation shows that clustering leads to great savings in runtime (up to a factor of 50) while maintaining the quality of the solution.
Fuzzy logic is used in a variety of applications because of its universal approximator attribute and nonlinear characteristics. But, it takes a lot of trial and error to come up with a set of membership functions and rule-base that will effectively work for a specific application. This process could be simplified by using a heuristic search algorithm like Genetic Algorithm (GA). In this paper, genetic fuzzy is applied to the task assignment for cooperating Unmanned Aerial Vehicles (UAVs) classified as the Polygon Visiting Multiple Traveling Salesman Problem (PVMTSP). The PVMTSP finds a lot of applications including UAV swarm routing. We propose a method of genetic fuzzy clustering that would be specific to PVMTSP problems and hence more efficient compared to k-means and c-means clustering. We developed two different algorithms using genetic fuzzy. One evaluates the distance covered by each UAV to cluster the search-space and the other uses a cost function that approximates the distance covered thus resulting in a reduced computational time. We compare these two approaches to each other as well as to an already benchmarked fuzzy clustering algorithm which is the current state-of-the-art. We also discuss how well our algorithm scales for increasing number of targets. The results are compared for small and large polygon sizes.
This paper presents a solution strategy for achieving cooperative timing among teams of vehicles. The initial paths are constructed based on Voronoi diagram and based on that, the smoothing paths are designed for the Waypoint Path Planner (WPP). Then it considers the cooperative timing problem and proposes a particle swam optimization algorithm for simultaneous attack. Simulation results show that the approach is of high efficiency in multi-UAVs cooperative timing problem.
Two teams of Unmanned Aerial Vehicles (UAVs) are used in the encirclement of two targets at the same time. Encirclement is defined as the situation in which a target is isolated and surrounded by a group of UAVs. It is a tactic that can be employed by a team of UAVs to neutralize a target by restricting its movement due to a containment motion near the target while maintaining a formation around it. In this paper, the problem of choosing the correct target to create a dynamic circular formation is considered and a Decentralized Model Predictive Control (DMPC) policy is formulated. From simulation results the derived Model Predictive Control (MPC) policy is effective for the case of two teams of UAVs encircling two stationary targets, and two teams of UAVs encircling two moving targets. The contributions of this paper are the application of MPC to the problem of encirclement, the explicit objective of a dynamic circular formation around the target, and the ability of each team to choose its correct target.
The task assignment problem of multiple heterogeneous unmanned aerial vehicles (UAVs), concerned with cooperative decision making and control, is studied in this paper. The heterogeneous vehicles have different operational capabilities and kinematic constraints, and carry limited resources (e.g., weapons) onboard. They are designated to perform multiple consecutive tasks cooperatively on multiple ground targets. The problem becomes much more complicated because of these terms of heterogeneity. In order to tackle the challenge, we modify the former genetic algorithm with multi-type genes to stochastically search a best solution. Genes of chromosomes are different, and they are assorted into several types according to the tasks that must be performed on targets. Different types of genes are processed specifically in the improved genetic operators including initialization, crossover, and mutation. We also present a mirror representation of vehicles to deal with the limited resource constraint. Feasible chromosomes that vehicles could perform tasks using their limited resources under the assignment are created and evolved by genetic operators. The effect of the proposed algorithm is demonstrated in numerical simulations. The results show that it effectively provides good feasible solutions and finds an optimal one.
This paper proposes a fuzzy logic based autonomous navigation controller for UAVs (unmanned aerial vehicles). Three fuzzy logic modules are developed under the main navigation system for the control of the altitude, the speed, and the heading, through which the global position (latitude–longitude) of the air vehicle is controlled. A SID (Standard Instrument Departure) and TACAN (Tactical Air Navigation) approach is used and the performance of the fuzzy based controllers is evaluated with time based diagrams under MATLAB’s standard configuration and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of detailed six-degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. The Aerosonde UAV model is used in the simulations in order to demonstrate the performance and the potential of the controllers. Additionally, FlightGear Flight Simulator and GMS aircraft instruments are deployed in order to get visual outputs that aid the designer in the evaluation of the controllers. Despite the simple design procedure, the simulated test flights indicate the capability of the approach in achieving the desired performance.
This paper addresses the problems of autonomous task allocation and trajectory planning for a fleet of UAVs. Two methods are compared for solving the optimization that combines task assignment, sub-jected to UAV capability constraints, and path plan-ning, subjected to dynamics, avoidance and timing constraints. Both sub-problems are non-convex and the two are strongly-coupled. The first method ex-presses the entire problem as a single mixed-integer linear program (MILP) that can be solved using available software. This method is guaranteed to find the globally-optimal solution to the problem, but is computationally intensive. The second method employs an approximation for rapid computation of the cost of many different trajectories. This enables the assignment and trajectory problems to be decou-pled and partially distributed, offering much faster computation. The paper presents several examples to compare the performance and computational re-sults from these two algorithms.
This paper proposes an innovative data-fusion/ data-mining game theoretic situation awareness and impact assessment approach for cyber network defense. Alerts generated by Intrusion Detection Sensors (IDSs) or Intrusion Prevention Sensors (IPSs) are fed into the data refinement (Level 0) and object assessment (L1) data fusion components. High-level situation/threat assessment (L2/L3) data fusion based on Markov game model and Hierarchical Entity Aggregation (HEA) are proposed to refine the primitive prediction generated by adaptive feature/pattern recognition and capture new unknown features. A Markov (Stochastic) game method is used to estimate the belief of each possible cyber attack pattern. Game theory captures the nature of cyber conflicts: determination of the attacking-force strategies is tightly coupled to determination of the defense-force strategies and vice versa. Also, Markov game theory deals with uncertainty and incompleteness of available information. A software tool is developed to demonstrate the performance of the high level information fusion for cyber network defense situation and a simulation example shows the enhanced understating of cyber-network defense.
We consider an autonomous vehicle-target assignment problem where a group of vehicles are expected to optimally assign themselves to a set of targets. We introduce a game-theoretical formulation of the problem in which the vehicles are viewed as self-interested decision makers. Thus, we seek the optimization of a global utility function through autonomous vehicles that are capable of making individually rational decisions to opti-mize their own utility functions. The first important aspect of the problem is to choose the utility functions of the vehicles in such a way that the objectives of the vehicles are localized to each vehicle yet aligned with a global utility function. The second important aspect of the problem is to equip the vehicles with an appropriate negotiation mechanism by which each vehicle pursues the optimization of its own utility function. We present several design procedures and accompanying caveats for vehicle utility design. We present two new negotiation mechanisms, namely, "generalized regret monitoring with fading memory and inertia" and "selective spatial adaptive play," and provide accom-panying proofs of their convergence. Finally, we present simulations that illustrate how vehicle negotiations can consistently lead to near-optimal assignments provided that the utilities of the vehicles are designed appropriately.
This paper investigates the problem of decentralized task assignment for a fleet of UAVs. Centralized task assignment for a fleet of UAVs is often not practical due to communication limits, robustness issues, and scalability, and using a distributed approach can mitigate many of these problems. One recently proposed decentralized approach is to replicate the central assignment algorithm on each UAV. The success of this implicit coordination strongly depends on the assumption that all UAVs have the same situational awareness. Examples are presented in this paper to show that this consensus in the information is both necessary and potentially time consuming. This paper extends the basic implicit coordination approach to achieve better performance with imperfect data synchronization. The resulting robust decentralized task assignment method assumes some degree of data synchronization, but adds a second planning step based on sharing the planning data. The approach is analogous to closing a synchronization loop on the planning process to reduce the sensitivity to exogenous disturbances. Further simulations are presented to show the advantages of this method in reducing the conflicts in the assignments, resulting in improved performance compared to implicit coordination. These results also clearly demonstrate the effect of communication at the different stages of the planning algorithm on the overall mission performance.
This paper poses the cooperative perimeter-surveillance problem and offers a decentralized solution that accounts for perimeter growth (expanding or contracting) and insertion/deletion of team members. By identifying and sharing the critical coordination information and by exploiting the known communication topology, only a small communication range is required for accurate performance. Simulation and hardware results are presented that demonstrate the applicability of the solution.
The emerging area of intelligent unmanned aerial vehicle (UAV) research has shown rapid development in recent years and offers a great number of research challenges for arti- ficial intelligence. For both military and civil applications, there is a desire to develop more sophisticated UAV platforms where the emphasis is placed on development of intelligent capabilities. Imagine a mission scenario where a UAV is supplied with a D model of a region containing buildings and road structures and is instructed to fly to an arbitrary number of building structures and collect video streams of each of the building's respective facades. In this article, we describe a fully operational UAV platform which can achieve such missions autonomously. We focus on the path planner integrated with the platform which can generate collision free paths autonomously during such missions. It is based on the use of probabilistic roadmaps. The path planner has been tested together with the UAV platform in an urban environment used for UAV experimentation.
Unmanned Aircraft Systems (UAS) have been increasing in popularity in personal, commercial, and military applications. The increase of the use of UAS poses a significant risk to general air travel, and will burden an already overburdened Air Traffic Control (ATC) network if the Air Traffic Management (ATM) system does not undergo a revolutionary change. Already there have been many near misses reported in the news with personal hobbyist UAS flying in controlled airspace near airports almost colliding with manned aircraft. The expected increase in the use of UAS over the upcoming years will exacerbate this problem, leading to a catastrophic incident involving substantial damage to property or loss of life. ATC professionals are already overwhelmed with the air traffic that exists today with only manned aircraft. With UAS expected to perform many tasks in the near future, the number of UAS will greatly outnumber the manned aircraft and overwhelm the ATC network in short order to the point where the current system will be rendered extremely dangerous, if not useless. This paper seeks to explore the possibility of using the artificial intelligence concept of fuzzy logic to automate the ATC system in order to handle the increased traffic due to UAS safely and efficiently. Automation would involve an algorithm to perform arbitration between aircraft based on signal input to ATC ground stations from aircraft, as well as signal output from the ATC ground stations to the aircraft. Fuzzy logic would be used to assign weights to the many different variables involved in ATM to find the best solution, which keeps aircraft on schedule while avoiding other aircraft, whether they are manned or unmanned. The fuzzy logic approach would find the weighted values for the available variables by running a simulation of air traffic patterns assigning different weights per simulation run, over many different runs of the simulation, until the best values are found that keep aircraft on schedule and maintain the required separation of aircraft. © 2018 Institute of Advanced Engineering and Science. All rights reserved.
Rapid thermal processing (RTP) is very important for semiconductor manufacturing. The RTP is actually a spatially distributed dynamical system (SDDS) with multiple control sources. Due to its highly nonlinear and spatiotemporal dynamics, it would be difficult to maintain the temperature uniformity inside the RTP. In this study, a hierarchical intelligent methodology is proposed for the spatiotemporal control of RTP. The control scheme consists of three different levels. At the lower level, the decomposition strategy is taken to divide the SDDS into several relatively simple subsystems; each of them is handled by one control source. At the mid-level, three dimensional fuzzy logic controllers (3D-FLCs) are designed, which have inherent capability to process spatiotemporal dynamics, to maintain the temperature uniformity of each subsystem. At the upper level, a local coordination strategy will be designed to adjust 3D-FLCs to overcome interactions between the adjacent subsystems. The proposed scheme is applied to the temperature uniformity control of a rapid thermal chemical vapor deposition system (RTCVD), and better temperature uniformity can be achieved.
This paper addresses a flatness-based controller and a Model Predictive Control (MPC) trajectory generation for a quadrotor camera helicopter. Applications like aerial videography can highly benefit from an automation of the pilot's tasks, enabling the camera operator to solely focus on camera motion control. The coupled nonlinear system dynamics of a quadrotor pose difficulties precisely controlling several channels simultaneously for agile maneuvering using conventional controllers. A flatness-based approach is employed to obtain linear input-output dynamics, even for large attitude angles. The associated state feedback equations are explicitly derived. The resulting linear system dynamics are controlled using a cascaded proportional control structure. Feasible reference trajectories are generated using a linear MPC, which translates operator commands for camera motions — e.g. relative to a point-of-interest — into quadrotor trajectories complying with operational constraints. Flatness-based controller and MPC trajectory generation show tracking errors below 1% in simulation tests. Accurate and smooth positioning is achieved in first indoor test flights. The gained results motivate adoptions of the proposed control approach to other UAV applications with similar demands for pilot automation and accuracy.
This paper presents possible application of hierarchical fuzzy logic systems to control vehicles in computer games. The main idea is presented in two ways, a current research referring to paper [14] and concerning the new architecture of a fuzzy logic system as Hierarchical Fuzzy Logic Systems (HFLS), and a brief look at the application of higher order fuzzy sets to these systems. ”Hierarchical” means that fuzzy sets produced as output of one of fuzzy controllers are then processed as an input of another fuzzy controller. The use of such a controller significantly enhances the possibilities of computational intelligence methods in single-player games, i.e. where the ”enemy” is controlled by agents simulating some real behaviour. The original proposal takes into account type-1 fuzzy sets which are not able to model uncertainties. The proposal presented in this paper models a type-2 hierarchical fuzzy logic system with uncertainties support, built with fuzzy controllers (in the sense of Mamdani). The advantages and disadvantages of HFLS in comparison to classical fuzzy systems with preliminary discussion about type-2 hierarchical fuzzy logic system are enumerated and commented on.
In addition to the terminal state constraint and control action energy constraints, the constraints of safe distance to avoid colliding and dependable distance to guarantee normal communication between vehicles were also considered. On the premise of satisfying all above constraint requirements, a new hybrid genetic algorithm was proposed. It is the incorporation of the control parameterization and time discretization (CPTD) method into the genetic algorithm (GA). The hybrid genetic algorithm was used to transform the problem of time-optimal control for formation reconfiguration into discrete optimization problem with a free terminal state constraint by CPTD, and the global optima of the problem of time-optimal control of formation reconfiguration was obtained using the improved GA, with new genetic arithmetic operators. The example results show the superiority of the hybrid algorithm, which is applicable to the problem of time-optimal control for formation reconfiguration.
This chapter is concerned with dynamically determining appropriate flight patterns for a set of autonomous UAVs in an urban environment, with multiple mission goals. The UAVs are tasked with searching the urban region for targets of interest and tracking those targets that have been detected. It is assumed that there are limited communication capabilities between the UAVs and that there exist possible line of sight constraints between the UAVs and the targets. Each UAV (i) operates its own dynamic feedback loop, in a receding-horizon framework, incorporating local information (from UAV i perspective) as well as remote information (from the perspective of the “neighbor” UAVs) to determine the task to perform and the optimal flight path of UAV i over the planning horizon. This results in a decentralized and more realistic model of the real- world situation. As the coupled task assignment and flight route optimization formulation is NP-hard, a hybrid heuristic for continuous global optimization is developed to solve for the flight plan and tasking over the planning horizon. Experiments are considered as communication range between UAVs varies.
This chapter is concerned with dynamically determining appropriate flight patterns for a set of autonomous UAVs in an urban environment, with multiple mission goals. The UAVs are tasked with searching the urban region for targets of interest and tracking those targets that have been detected. It is assumed that there are limited communication capabilities between the UAVs and that there exist possible line of sight constraints between the UAVs and the targets. Each UAV (i) operates its own dynamic feedback loop, in a receding-horizon framework, incorporating local information (from UAV i perspective) as well as remote information (from the perspective of the “neighbor” UAVs) to determine the task to perform and the optimal flight path of UAV i over the planning horizon. This results in a decentralized and more realistic model of the real- world situation. As the coupled task assignment and flight route optimization formulation is NP-hard, a hybrid heuristic for continuous global optimization is developed to solve for the flight plan and tasking over the planning horizon. Experiments are considered as communication range between UAVs varies.
This chapter attempts to bring closure to the Handbook of UAVs. This, by itself, is an extremely challenging and difficult task; it should not be, but it is! After all, the reason is so simple that it was almost entirely ignored and overlooked during the course of preparing the handbook. Unmanned aviation has witnessed exponential growth, worldwide, amidst controversy and difficulties, with new potential applications regularly surfacing. And although several federal agencies and multiple governments, with perhaps competitive agendas, are involved in accomplishing the common goal of integration of unmanned aviation into civilian airspace, everything seems to move so fast, that when one considers that a specific subject is completed, new changes and modifications are in effect. However, it looks like that 2013 will be “the critical year” for a new jumpstart in unmanned aviation, with efforts converging and roadmaps being in place towards the ultimate goal of integration of unmanned aviation into civilian and commercial airspace. This goal remains intact and it has not changed over the years. Therefore, this chapter aims at, simply, summarizing the state of the art, the potential of unmanned aviation, referring to challenges that still need to be resolved and overcome, commenting on economic impact, and highlighting anticipated future developments. At the same time, this epilogue may also be viewed as justification for enhancing the handbook publishing a second edition. Let's think the unthinkable, let's do the undoable, let's prepare to grapple with the ineffable itself, and see if we may not eff it after all. Douglas Adams (Dirk Gently's Holistic Detective Agency, 1987)
This paper presents the development and flight-testing of an obstacle avoidance system that can provide a rotary-wing unmanned aerial vehicle (UAV) the autonomous obstacle field navigation capability in uncertain environment. The system is composed of a sensor, an obstacle map generation algorithm from sensor measurements, an online path planning algorithm, and an adaptive vehicle controller. The novel approach of path planning presented in the paper is the integration of a newly developed receding horizon (RH) trajectory optimization scheme with a global path searching algorithm. The developed RH trajectory optimization scheme solves the local nonlinear trajectory optimization problem using approximated vehicle dynamics, maneuverability constraints, and terrain constraints within the finite range of the sensor. The global path searching by dynamic programming algorithm finds the shortest path to the destination to provide the initial guess to the RH trajectory optimization. The spline-based direct solver, Nonlinear Trajectory Generation (NTG), solves the RH trajectory optimization in real time and updates the solution continuously. The developed system is implemented within the Georgia Tech UAV Simulation Tool (GUST) and on the onboard computer of the Georgia Tech UAV test bed. Simulations and flight tests carried out for the benchmark scenarios and with sensor-in-the-loop flight tests demonstrated the viability of the developed system for autonomous obstacle field navigation capability of a UAV.
"Introduction to Optimum Design " is the most widely used textbook in engineering optimization and optimum design courses. It is intended for use in a first course on engineering design and optimization at the undergraduate or graduate level within engineering departments of all disciplines, but primarily within mechanical, aerospace and civil engineering. The basic approach of the text is to describe an organized approach to engineering design optimization in a rigorous yet simplified manner, illustrate various concepts and procedures with simple examples, and demonstrate their applicability to engineering design problems. Formulation of a design problem as an optimization problem is emphasized and illustrated throughout the text. Excel and MATLAB are featured throughout as learning and teaching aids. The 3rd edition has been reorganized and enhanced with new material, making the book even more appealing to instructors regardless of the level they teach the course. Examples include moving the introductory chapter on Excel and MATLAB closer to the front of the book and adding an early chapter on practical design examples for the more introductory course, and including a final chapter on advanced topics for the purely graduate level course. Basic concepts of optimality conditions and numerical methods are described with simple and practical examples, making the material highly teachable and learnable.Applications of the methods for structural, mechanical, aerospace and industrial engineering problems.Introduction to MATLAB Optimization Toolbox.Optimum design with Excel Solver has been expanded into a full chapter.Practical design examples introduce students to usage of optimization methods early in the book. New material on several advanced optimum design topics serves the needs of instructors teaching more advanced courses.
This paper proposes an approach for fault tolerant control of quadrotor UAVs in formation flight. The fault tolerance is achieved by changing the reference trajectories of the entire formation so that to allow the damaged UAV to follow the healthy ones. For this purpose, the virtual structure formation framework is adopted and differential flatness is employed to find the relation between the applied control inputs and the reference trajectories. Simulation results for three UAVs in formation are given to demonstrate the effectiveness of the proposed approach.
Dynamic encirclement is a tactic which can be employed by a group of UAVs to neutralize a target by restricting its movement, or provide constant surveillance of a target. The aim of the UAVs in the formation is to move into a position close to the target and establish a moving formation around the target. In this paper, the problem of creating a dynamic circular formation around a moving target is considered, and a Decentralized Model Predictive Control (DMPC) policy is formulated. Using theoretical results, a stabilizing control policy is derived, and the policy is validated through simulation results. Furthermore, we examine the effects of communications between the UAVs and the use of a model target on the performance of the UAVs. The contributions of this paper are the extension of the dynamic encirclement tactic to the case of a group of UAVs and a moving target, the consideration of a target model and communications, and the application of theoretical stability analysis to the problem.
Great potentials of robotic networks have been found in numerous applications such as environmental monitoring, battlefield surveillance, target search and rescue, oil and gas exploration, etc. A networked multi-robot system allows cooperative actions among robots and can achieve much beyond the summed capabilities of each individual robot. However, it also poses new research and technical challenges including novel methods for multi-agent data fusion, topology control and cooperative path planning, etc. In this paper, we review recent developments in cooperative control of robotic networks with focus on search and exploration. We shall first present a general formulation of the search and exploration problem, and then divide the overall search strategy into different modules based on their functions. Methods and algorithms are illustrated and compared following the classification of the modules. Moreover, a 3D simulator developed in our laboratory is introduced and its application is demonstrated by experiments. Finally, challenges and future research in this area are provided.
The initial state of an Unmanned Aerial Vehicle (UAV) system and the relative state of the system, the continuous inputs of each flight unit are piecewise linear by a Control Parameterization and Time Discretization (CPTD) method. The approximation piecewise linearization control inputs are used to substitute for the continuous inputs. In this way, the multi-UAV formation reconfiguration problem can be formulated as an optimal control problem with dynamical and algebraic constraints. With strict constraints and mutual interference, the multi-UAV formation reconfiguration in 3-D space is a complicated problem. The recent boom of bio-inspired algorithms has attracted many researchers to the field of applying such intelligent approaches to complicated optimization problems in multi-UAVs. In this paper, a Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) is proposed to solve the multi-UAV formation reconfiguration problem, which is modeled as a parameter optimization problem. This new approach combines the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), which can find the time-optimal solutions simultaneously. The proposed HPSOGA will also be compared with basic PSO algorithm and the series of experimental results will show that our HPSOGA outperforms PSO in solving multi-UAV formation reconfiguration problem under complicated environments.
A study on multivehicle trajectory planning for cooperative reconnaissance problems is presented. Specifically, this work develops understanding and insights into how vehicles cooperate in reconnaissance type missions in which target information is maximized. The performance metric used to guide the cooperation study is the amount of information, defined using the Fisher information matrix, that the sensing vehicles gather over their planned trajectory. A receding horizon optimal control formulation is developed and solved for trajectories that yield maximum information. High-risk zones and vehicle/terminal constraints are also are considered. Trends include the following: 1) vehicles with nonsymmetric sensors tend to triangulate as they get close to the target; 2) vehicles tend to move toward stationary targets as quickly as possible; 3) the addition of a third vehicle exhibits at least 50% less performance improvement than the addition of the second vehicle, and even less for nonsymmetric sensors.; 4) optimization for multiple vehicles and targets is a strong function of target to target distances and sensor uncertainty symmetry; and 5) short planning horizons are preferable for moving targets.
Aerospace researchers used genetic algorithms to solve the problems of cooperative task assignment and path planning of multiple unmanned aerial vehicles (UAV) that are used to carry out special military operations. The researchers used one of the mission scenarios of the Suppression of Enemy Air Defense (SEAD) military operations. It was demonstrated that cooperative task assignment and path planning of multiple UAVs plays a key role in the SEAD military operations. They also focused on primary terrain information, used by the SEAD team to carry out cooperative military operations, such as location of targets, obstacles, and dangerous areas. The investigations also revealed that the timing constrains for simultaneous attacks and multiple tasks with specific time delays were applied for carrying more advanced military operations. Genetic algorithms was used to solve the problem, as it not restricted to continuity, differentiability, or unimodality of searching for the optimal solution.
This paper addresses the development of a hierarchical distributed control system for unmanned aerospace vehicles, specifically wide area search munitions. The hierarchy has three levels of decomposition. The top level performs task assignment using a market based bidding procedure. The middle subteam level coordinates cooperative tasks. The lower level executes the tasks, and performs trajectory optimization. The tasks, or functions, include cooperative search, cooperative classification, cooperative attack, and cooperative battle damage assessment. The control system is decentralized with no explicit leader, is redundant, and thus fault tolerant, and has on-line optimal decision capability that allows operation in a dynamic unstructured environment. Simulations are performed for a team of up to eight vehicles that show superior performance over that achievable when the vehicles operate independently.
Natural disasters such as forest fires, earthquakes, tsunamis, floods, hurricanes, and cyclones happen unexpectedly and bring out the worst influence on people. Unmanned Aerial Vehicles (UAVs) could be used under these disasters for surveillance, search and rescue. In order to have good performances in mission areas, effective algorithms are required in mission re-tasking and path re-planning to handle unanticipated events or any environmental disturbances. In this paper, a new algorithm is proposed to deal with path re-planning for multi-mission of multi-UAV under environments with unexpected events. A group of UAVs are considered to perform joint missions. Each UAV plans its own initial, optimal or sub-optimal path using Voronoi graph and Dijkstra algorithm. Our algorithm is then employed to assign a distinct task to each UAV and to re-plan its path based on new multi-mission requirement corresponding to some unexpected events. In addition to a theoretical analysis of the algorithm, the paper has also provided relevant simulation results which have shown that the algorithm can be used effectively for multiple cooperating UAVs’ path re-planning under uncertain and dynamic disaster environments.
This paper presents a new robust approach to the task assignment of unmanned aerial vehicles (UAVs) operating in uncertain dynamic environments for which the optimization data, such as target cost and target–UAV distances, are time varying and uncertain. The impact of this uncertainty in the data is mitigated by tightly integrating two approaches for improving the robustness of the assignment algorithm. One approach is to design task assignment plans that are robust to the uncertainty in the data, which reduces the sensitivity to errors in the situational awareness (SA), but can be overly conservative for long duration plans. A second approach is to replan as the SA is updated, which results in the best plan given the current information, but can lead to a churning type of instability if the updates are performed too rapidly. The strategy proposed in this paper combines robust planning with the techniques developed to eliminate churning. This combination results in the robust filter-embedded task assignment algorithm that uses both proactive techniques that hedge against the uncertainty, and reactive approaches that limit churning behavior by the vehicles. Numerous simulations are shown to demonstrate the performance benefits of this new algorithm. Copyright © 2007 John Wiley & Sons, Ltd.
In conventional fuzzy logic controllers, the computational complexity increases with the dimensions of the system variables; the number of rules increases exponentially as the number of system variables increases. Hierarchical fuzzy logic controllers (HFLC) have been introduced to reduce the number of rules to a linear function of system variables. However, the use of hierarchical fuzzy logic controllers raises new issues in the automatic design of controllers, namely the coordination of outputs of sub-controllers at lower levels of the hierarchy. In this paper, a method is described for the automatic design of an HFLC using an evolutionary algorithm called differential evolution (DE).The aim in this paper is to develop a sufficiently versatile method that can be applied to the design of any HFLC architecture. The feasibility of the method is demonstrated by developing a two-stage HFLC for controlling a cart–pole with four state variables. The merits of the method are automatic generation of the HFLC and simplicity as the number of parameters used for encoding the problem are greatly reduced as compared to conventional methods.
Introduction Characteristics of a Constrained Problem Random Search Methods Complex Method Sequential Linear Programming Basic Approach in the Methods of Feasible Directions Zoutendijk's Method of Feasible Directions Rosen's Gradient Projection Method Generalized Reduced Gradient Method Sequential Quadratic Programming Transformation Techniques Basic Approach of the Penalty Function Method Interior Penalty Function Method Convex Programming Problem Exterior Penalty Function Method Extrapolation Techniques in the Interior Penalty Function Method Extended Interior Penalty Function Methods Penalty Function Method for Problems with Mixed Equality and Inequality Constraints Penalty Function Method for Parametric Constraints Augmented Lagrange Multiplier Method Checking the Convergence of Constrained Optimization Problems Test Problems MATLAB Solution of Constrained Optimization Problems References and Bibliography Review Questions Problems
A fuzzy logic resource allocation algorithm that enables a collection of unmanned aerial vehicles (UAVs) to automatically
cooperate will be discussed. The goal of the UAVs’ coordinated effort is to measure the atmospheric index of refraction. Once
in flight no human intervention is required. A fuzzy logic based planning algorithm determines the optimal trajectory and
points each UAV will sample, while taking into account the UAVs’ risk, risk tolerance, reliability, and mission priority for
sampling in certain regions. It also considers fuel limitations, mission cost, and related uncertainties. The real-time fuzzy
control algorithm running on each UAV renders the UAVs autonomous allowing them to change course immediately without consulting
with any commander, requests other UAVs to help, and change the points that will be sampled when observing interesting phenomena.
Simulations show the ability of the control algorithm to allow UAVs to effectively cooperate to increase the UAV team’s likelihood
of success.
In this paper the optimal timing of air-to-ground tasks is considered. A scenario is examined, where multiple unmanned air vehicles (UAVs) must perform one or more tasks on a set of geographically dispersed targets in the presence of no-fly zones. Strict inter-task timing constraints apply, which have been chosen by BAE Systems' Military Air & Information (MAI) business to reflect current UAV usage. The optimal assignment of these tasks requires cooperation amongst the vehicles in order to generate a plan that is efficient, with respect to overall mission duration and satisfies all problem constraints. A number of previous papers have compared the benefits of employing numerous optimisation algorithms to this class of problem. Recently, auction techniques have been investigated for coordination of multiple autonomous assets and this paper compares two auctioning algorithms, a meta-heuristic algorithm and a mathematical programming approach for solving such constrained task assignment problems.
We consider the problem of controlling multiple robots manipulating and transporting a payload in three dimensions via cables.
Individual robot control laws and motion plans enable the control of the payload (position and orientation) along a desired
trajectory.We address the fact that robot configurations may admit multiple payload equilibrium solutions by developing constraints
for the robot configuration that guarantee the existence of a unique payload pose. Further, we formulate individual robot
control laws that enforce these constraints and enable the design of non-trivial payload motion plans. Finally, we propose
two quality measures for motion plan design that minimize individual robot motion and maximize payload stability along the
trajectory. The methods proposed in the work are evaluated on a team of aerial robots in experimentation.
This paper presents a solution to the time-optimal control of the relative formation of multiple vehicles. This is a problem in cooperative time-optimal control with a free terminal state constraint. In this paper, a canonical formulation of the problem is first derived. Then, a numerical technique to solve this class of problem is proposed. Numerical results demonstrate the efficacy of the proposed formulation and solution to the problem of expeditiously building and controlling formations of cooperative autonomous vehicles.
A weapon system consisting of a swarm of air vehicles whose
mission is to search for, classify, attack, and perform battle damage
assessment, is considered. It is assumed that the target field
information is communicated to all the elements of the swarm as it
becomes available. A network flow optimization problem is posed whose
readily obtained solution yields the optimum resource allocation among
the air vehicles in the swarm. Hence, the periodic reapplication of the
centralized optimization algorithm yields the benefit of cooperative
feedback control
Coordination and control of multiple UAVs, AIAA Guidance, Navigation, and Control Conf
- A Richards
- J Bellingham
- M Tillerson
- J How
A. Richards, J. Bellingham, M. Tillerson and J. How, Coordination and
control of multiple UAVs, AIAA Guidance, Navigation, and Control
Conf. (2002), pp. 1-11.
A fuzzy switching median filter for highly corrupted images
- R Pushpavalli
- G Sivarajde
R. Pushpavalli and G. Sivarajde, A fuzzy switching median filter
for highly corrupted images, Int. J. Sci. Res. Publ. 3(6) (2013) 1-6.
UMIS-TIC) in MTC, Egypt. His research interests are focused on Automatic Control, with particular interest in advanced control of flying vehicles
- T Ahmed
Ahmed T. Hafez received a B.Sc. and M.Sc. degrees
in Electrical Engineering in 1999 and 2007, respectively, from the Military Technical College,
Cairo, Egypt. He received his Ph.D. degree in Electric Engineering in 2014 from Electrical and Computer Engineering at Queens University, Kingston,
ON, Canada. In January 2015, he joined the
Department of Electrical Engineering, MTC, Egypt,
as an Assistant Professor. He is also working in the
Unmanned Integrated Systems Technology and
Innovation Center (UMIS-TIC) in MTC, Egypt. His
research interests are focused on Automatic Control, with particular interest in advanced control of flying vehicles. In his doctoral studies, his
research interests have expanded to include autonomous systems, optimal
control, Model Predictive Control and unmanned aerial vehicles.
respectively, from the Military Technical College (MTC), Egypt. He received his Ph
- Mohamed A Kamel
- B Sc
- M Sc
Mohamed A. Kamel received his B.Sc. and M.Sc. in
Mechanical Engineering in 2002 and 2009, respectively, from the Military Technical College
(MTC), Egypt. He received his Ph.D. in Mechanical
Engineering from the Department of Mechanical,
Industrial, and Aerospace Engineering (MIAE),
Concordia University, in July 2016. In November
2016, he joined the Department of Mechanical
Engineering, MTC, Egypt, as an Assistant Professor.
He is also working in the Unmanned Integrated
Systems Technology and Innovation Center (UMIS-TIC) in MTC, Egypt. Mohamed's research interests are mainly focused on
cooperative control, fault-tolerant cooperative control, path planning and
trajectory tracking of unmanned ground vehicles, robotics, optimization,
and dynamic systems modeling and control.