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In real-world applications such as rescue robots, service robots, mobile mining robots, and mine searching robots, an autonomous mobile robot needs to reach multiple targets with the shortest path. This paper proposes an Immune System algorithm (ISA) for real-time map building and path planning for multi-target applications. Once a global route is...
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... has been considerable research in this area due to its real-world applications. Multitarget motion planning is very crucial for an autonomous robot. The multi-target path planning seeks a collision-free route to visit a sequence of targets with the minimized total distance under unknown environments. Let us consider a case in Fig. 1. There are nine points that must be visited by a robot. Let us consider Spot 1 as the starting point and Spot 9 as its final destination. There are multiple paths that could be traversed to reach Spot 9 but only a few passes through Spot 2-8 also some of the spots are longer than the others. The first stage would be to determine the ...
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... illustrated in Fig. 5(b). The ISA based multi-target global path planning and VFH local navigator are simulated and tested in multi-target navigation with obstacle avoidance. The proposed ISA path planning method, as stated in Sect. 2.2, is first applied to a test scenario with populated obstacles in comparison of the test scenario identical as Fig. 15 case 4 of [13] shown in Fig. 6 in this context. The trajectories of robot motion planning are illustrated about the proposed ISA model, ACO and improved ACO, respectively, in Fig. 6. The workspace has a size of 35 × 30, which is topologically organized as a grid-based map. The starting point is (5, 3) and the target point is (23,29). ...
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... VFH-based local navigator, the built map from the initial position S (0, 0) and the final trajectory planned is illustrated in Fig. 9(c), and final map built exactly when To demonstrate its efficiency in determining the path length. Figure 10 shows a plot of iterations required to reach an ideal solution in comparison with other orders of algorithms like log-linear and exponential algorithms. The ISA is run with a varying number of points and the results show the algorithm performs better than a quadratic algorithm O(n 2 ) but worse than Log-linear algorithm O(nlog(n)) which is much better than the brute force method of solving which is an O(n!) algorithm. ...
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Exploration of unknown scenes before human entry is essential for safety and efficiency in numerous scenarios, e.g., subterranean exploration, reconnaissance, search and rescue missions. Fleets of autonomous robots are particularly suitable for this task, via concurrent exploration, multi-sensory perception and autonomous navigation. Communication...
Citations
... Recently, a variety of approaches have been proposed to achieve reliable autonomous vehicles motion planning, such as nature-inspired optimisation algorithms [6][7][8][8][9][10][11][12], graphbased models [13][14][15][16], sampling-based methods [17][18][19] and neural networks [20]. ...
The widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation, with path planning emerging as a critical aspect. However, existing road infrastructure confronts challenges due to prolonged use and insufficient maintenance. Previous research on autonomous vehicle navigation has focused on determining the trajectory with the shortest distance, while neglecting road construction information, leading to potential time and energy inefficiencies in real‐world scenarios involving infrastructure development. To address this issue, a digital twin‐embedded multi‐objective autonomous vehicle navigation is proposed under the condition of infrastructure construction. The authors propose an image processing algorithm that leverages captured images of the road construction environment to enable road extraction and modelling of the autonomous vehicle workspace. Additionally, a wavelet neural network is developed to predict real‐time traffic flow, considering its inherent characteristics. Moreover, a multi‐objective brainstorm optimisation (BSO)‐based method for path planning is introduced, which optimises total time‐cost and energy consumption objective functions. To ensure optimal trajectory planning during infrastructure construction, the algorithm incorporates a real‐time updated digital twin throughout autonomous vehicle operations. The effectiveness and robustness of the proposed model are validated through simulation and comparative studies conducted in diverse scenarios involving road construction. The results highlight the improved performance and reliability of the autonomous vehicle system when equipped with the authors’ approach, demonstrating its potential for enhancing efficiency and minimising disruptions caused by road infrastructure development.
... Graph-based models for multi-waypoint autonomous robot navigation is developed in [28,29]. In order to generate brief collision-free paths in unstructured landscapes, a hybrid framework using local navigation based on LiDAR was created [13,30]. For autonomous vehicles used in crack detection, a bat-pigeon based approach was presented for mapping and navigation [18], where a bat model used for local search was integrated with a pigeon model used for global search. ...
... Autonomous robots have found wide-ranging applications in various domains of our daily lives, including autonomous vehicles [1][2][3][4][5], medical robots [6], agriculture robots [7,8], and emergency response robots [9][10][11]. Robot path planning, a fundamental research area in robotics, has garnered considerable attention and research efforts over the past few decades [12][13][14][15][16]. The primary objective of path planning is to determine a collisionfree trajectory that enables a robot to navigate from its initial position to its target position while effectively avoiding obstacles [17,18]. ...
Recently, bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps. However, these approaches endure performance degradation
as problem complexity increases, often resulting in lengthy search times to find an optimal solution. This limitation is particularly critical for real-world applications like autonomous off-road vehicles, where high-quality path computation is essential for energy efficiency. To address these challenges, this paper proposes a new graph-based optimal path planning approach that
leverages a sort of bio-inspired algorithm, improved seagull optimization algorithm (iSOA) for rapid path planning of autonomous robots. A modified Douglas-Peucker (mDP) algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains. The resulting mDP-derived graph is then modeled using a Maklink graph theory. By applying the iSOA approach, the trajectory of an autonomous robot in the workspace is optimized. Additionally, a Bezier-curve-based smoothing approach is developed to generate
safer and smoother trajectories while adhering to curvature constraints. The proposed model is validated through simulated experiments undertaken in various real-world settings, and its
performance is compared with state-of-the-art algorithms. The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.
... Autonomous vehicle path planning plays a crucial role in Simultaneous Localization and Mapping algorithms (SLAM) and navigation modules. Its purpose is to construct a safe and collision-free trajectory, including map-based path planning and image-based path planning [1][2][3][4][5][6][7][8][9] . Map-based navigation utilizes sensor readings performed in Cartesian space with terrain costs in maps [10][11][12][13][14][15] . ...
With the growing applications of autonomous robots and vehicles in unknown environments, studies on image-based localization and navigation have attracted a great deal of attention. This study is significantly motivated by the observation that relatively little research has been published on the integration of cutting-edge path planning algorithms for robust, reliable, and effective image-based navigation. To address this gap, a biologically inspired Bat Algorithm (BA) is introduced and adopted for image-based path planning in this paper. The proposed algorithm utilizes visual features as the reference in generating a path for an autonomous vehicle, and these features are extracted from the obtained images by convolutional neural networks (CNNs). The paper proceeds as follows: first, the requirements for image-based localization and navigation are described. Second, the principles of the BA are explained in order to expound on the justifications for its successful incorporation in image-based navigation. Third, in the proposed image-based navigation system, the BA is developed and implemented as a path planning tool for global path planning. Finally, the performance of the BA is analyzed and verified through simulation and comparison studies to demonstrate its effectiveness.
... Multi-robot deployment is an essential issue in robotics field, which requires mobile robots to be deployed in the workspace to cooperatively fulfill tasks [1][2][3][4][5]. Dynamic deployment problems in combinatorial optimization consist of finding an optimal solution in some objectives, such as timing, workload, distance traveled, energy, and deadlines [6][7][8][9][10][11][12][13]. In real-world applications, such as mine detection, environmental exploration, and rescue mission, many robots need to be dynamically deployed to goals (targets) while total distance cost among robots, and between robots and goals is minimized [14][15][16][17][18][19][20]. ...
In real-world applications, multiple robots need to be dynamically deployed to their appropriate locations as teams while the distance cost between robots and goals is minimized, which is known to be an NP-hard problem. In this paper, a new framework of team-based multi-robot task allocation and path planning is developed for robot exploration missions through a convex optimization-based distance optimal model. A new distance optimal model is proposed to minimize the traveled distance between robots and their goals. The proposed framework fuses task decomposition , allocation, local sub-task allocation, and path planning. To begin, multiple robots are firstly divided and clustered into a variety of teams considering interrelation and dependencies of robots, and task decomposition. Secondly, the teams with various arbitrary shape enclosing intercorrelative robots are approximated and relaxed into circles, which are mathematically formulated to convex optimization problems to minimize the distance between teams, as well as between a robot and their goals. Once the robot teams are deployed into their appropriate locations, the robot locations are further refined by a graph-based Delaunay triangulation method. Thirdly, in the team, a self-organizing map-based neural network (SOMNN) paradigm is developed to complete the dynamical sub-task allocation and path planning, in which the robots are dynamically assigned to their nearby goals locally. Simulation and comparison studies demonstrate the proposed hybrid multi-robot task allocation and path planning framework is effective and efficient.
... Lei et al. [1] proposed a hybrid model to optimize the global trajectory using a graph-based search algorithm associated with an ant colony optimization (ACO) method. A hybrid fireworks algorithm with LiDAR-based local navigation was developed capable of generating short collision-free trajectories in unstructured environments [2,7]. A bat-pigeon algorithm was developed in crack detection-driven autonomous vehicle navigation and mapping, in which a local search-based bat algorithm and a global search-based pigeon-inspired optimization algorithm are effectively fused to improve the speed performance of robot path planning and mapping [3]. ...
... Lei et al. [3] proposed a hybrid model to optimize trajectory of the global path using a graph-based search algorithm associated with an ant colony optimization (ACO) method. A hybrid fireworks algorithm with LiDAR-based local navigation was developed capable of generating short collision-free trajectories in unstructured environments [4,12,13]. A bat-pigeon algorithm was developed in crack detection-driven autonomous vehicle navigation and mapping, in which a local search-based bat algorithm and a global search-based pigeon-inspired optimization algorithm are effectively fused to improve the speed performance of robot path planning and mapping [5]. ...
... In the general case, the informative planning protocol incorporates the multi-objective informative planning by using DCPP and obtained information that guide the robots to visit those places more frequently with higher information gain. This multiobjective optimization problem is stated in Eq. (12). ...
Multiple autonomous robots are deployed to fulfill tasks collaboratively in real-world applications with row-based settings as found in precision agriculture, warehouses, factory inspections, and wind farms. One batch of robots are assigned to explore, search and localize objects in large-scale row-based environments, while the other batch of robots move directly to the detected targets to retrieve the objects. In this paper, a multi-robot collaborative navigation framework with two different batches of robots is proposed to explore the environment and achieve the obtained targets, respectively. The first batch of robots act as detection robots, which are driven by a proposed informative-based directed coverage path planning (DCPP) through a multi-robot minimum spanning tree algorithm. It refines and optimizes the coverage path based on the information gained from the environment. The second batch of robot reaches the multiple targets by guidance from a hub-based multi-target routing (HMTR) scheme, which is applicable to row-based environments. The feasibility and effectiveness of the proposed methods are validated by simulation and comparison studies.
In real-world robot applications such as service robots, mining mobile robots, and rescue robots, an autonomous mobile robot is required to visit multiple waypoints that it achieves multiple-objective optimizations. Such multiple-objective optimizations include robot travelling distance minimization, time minimization, turning minimization, etc. In this paper, a particle swarm optimization (PSO) algorithm incorporated with a Generalized Voronoi diagram (GVD) method is proposed for a robot to reach multiple waypoints with minimized total distance. Firstly, a GVD is used to form a Voronoi diagram in an obstacle populated environment to construct safety-conscious routes. Secondly, the sequence of multiple waypoints is created by the PSO algorithm to minimize the total travel cost. Thirdly, while the robot attempts to visit multiple waypoints, it traverses along the edges of the GVD to form a collision-free trajectory. The regional path locally from waypoints to nearest nodes or edges needs to be created to join the trajectory. A Node Selection Algorithm (NSA) is developed in this paper to implement such a protocol to build up regional path from waypoints to nearest nodes or edges on GVD. Finally, a histogram-based local reactive navigator is adopted for moving obstacle avoidance. Simulation and comparison studies validate the effectiveness and robustness of the proposed model.
