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A heuristic Dual sampling domain Reduction-based Optimal Rapidly-exploring Random Tree scheme is proposed by guiding the planning procedure of the optimal rapidly-exploring random tree (RRT*) method through learning environmental knowledge. The scheme aims to plan low fuel expenditure, easy-execution, and low collision probability paths online for...
Citations
... Lin et al. [15] developed a closed-Loop RRT method for UAV dynamic obstacle collision avoidance. Wen et al. [16] developed a heuristic dual sampling domain reduction-based RRT* method to complete the online planning of an unmanned surface vehicle (USV). However, MCDPR consists of multiple mobile bases and CDPR, presenting a unique challenge compared to the aforementioned conventional robotic systems. ...
... One of the main advantages of the RRT algorithm is that it can be easily extended to multidimensional spaces [17]. Zhang et al. [16] proposed an optimization-based map exploration strategy by extending the RRT algorithm, enabling multiple robots to actively explore and construct environment maps. In a similar vein, Lau et al. [18] introduced a temporal memory-based RRT (TM-RRT) exploration strategy designed for multi-robot systems to execute robust exploration tasks within unknown environments. ...
Mobile cable-driven parallel robots (MCDPRs) offer expanded motion capabilities and workspace compared to traditional cable-driven parallel robots (CDPRs) by incorporating mobile bases. However, additional mobile bases introduce more degree-of-freedom (DoF) and various constraints to make their motion planning a challenging problem. Despite several motion planning methods for MCDPRs being developed in the literature, they are only applicable to known environments, and autonomous navigation in unknown environments with obstacles remains a challenging issue. The ability to navigate autonomously is essential for MCDPRs, as it opens up possibilities for the robot to perform a broad range of tasks in real-world scenarios. To address this limitation, this study proposes an online motion planning method for MCDPRs based on the pipeline of rapidly exploring random tree (RRT). The presented approach explores unknown environments efficiently to produce high-quality collision-free trajectories for MCDPRs. To ensure the optimal execution of the planned trajectories, the study introduces two indicators specifically designed for the mobile bases and the end-effector. These indicators take into account various performance metrics, including trajectory quality and kinematic performance, enabling the determination of the final following trajectory that best aligns with the desired objectives of the robot. Moreover, to effectively handle unknown environments, a vision-based system utilizing an RGB-D camera is developed, allowing for precise MCDPR localization and obstacle detection, ultimately enhancing the autonomy and adaptability of the MCDPR. Finally, the extensive simulations conducted using dynamic simulation software (CoppeliaSim) and the on-board real-world experiments with a self-built MCDPR prototype demonstrate the practical applicability and effectiveness of the proposed method.
... In [16], a novel planning algorithm based on the artificial vector field method and RRT* was proposed for USV low-cost path planning. Zhang et al. [17] improved the feasibility and efficiency of the planned path by utilizing the dual sampling space strategy and the Dubins curve. Inspired by DWA, Han et al. [18] presented an extended dynamic window approach (EDWA) for the automatic docking of USVs. ...
Path planning and tracking are essential technologies for unmanned surface vessels (USVs). The kinodynamic constraints and actuator faults, however, bring difficulties in finding feasible paths and control efforts. This paper proposes a collision avoidance strategy for USV by developing the kinodynamic rapidly exploring random tree-smart (kinodynamic RRT*-smart) algorithm and the fault-tolerant control method. By utilizing the triangular inequality and the intelligent biased sampling strategy, the kinodynamic RRT*-smart shows its advantages in terms of path length, cost and running time. With consideration of kinodynamic constraints, a feasible and collision-free trajectory can be provided. Then, a radial basis function neural network-based model predictive control (RBF-MPC) method was designed that compensates for the model’s uncertainties by developing the radial basis function neural network (RBF-NN) approximator and by constructing a feedback-state training dataset in real time. Furthermore, two types of fault situation were analyzed considering the thruster failure. We established the faults’ mathematical models and investigated the fault-tolerant strategies for different fault types. The simulation studies were conducted to validate the effectiveness of the proposed strategy. The results show that the proposed planning and control methods can avoid obstacles in faulty conditions.
... Usually, a global path planner is used to plan an optimal global path to avoid known static obstacles. For static avoidance, there are many kinds of algorithms, such as the A* [5] and RRT [6]. Based on the A* algorithm, a new algorithm is proposed, in which the maximum angular velocity constraint of the USV is considered to generate a path in real-time [7]. ...
In this paper, to improve the safety of navigation in dynamic environments, a new collision avoidance method is presented for Unmanned Surface Vehicles (USVs). It is mainly focused on dynamic obstacles and safety requirements. Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is introduced and the original artificial potential field (APF) algorithm is modified to ensure safe collision avoidance of USVs. The new attractive and repulsive potential field functions are designed and a new process of dynamic collision avoidance is constructed to solve the issue. At last, theoretical simulations and field tests are launched to validate our method. The simulation results show that the proposed method can perform safe navigation well. Through our USV platform, the effectiveness of the improved APF method for collision avoidance is also verified. Meanwhile, it is fast, effective, and deterministic.
... Based on this information, the local path can be planned quickly and correctly. The common algorithms for local path planning mainly include the following: the neural network algorithm [8], artificial potential field method [9,10], dynamic window method [11], artificial bee colony algorithm [12], random tree method [13], etc. ...
The quality of unmanned surface vehicle (USV) local path planning directly affects its safety and autonomy performance. The USV local path planning might easily be trapped into local optima. The swarm intelligence optimization algorithm is a novel and effective method to solve the path-planning problem. Aiming to address this problem, a hybrid bacterial foraging optimization algorithm with a simulated annealing mechanism is proposed. The proposed algorithm preserves a three-layer nested structure, and a simulated annealing mechanism is incorporated into the outermost nested dispersal operator. The proposed algorithm can effectively escape the local optima. Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) rules and dynamic obstacles are considered as the constraints for the proposed algorithm to design different obstacle avoidance strategies for USVs. The coastal port is selected as the working environment of the USV in the visual test platform. The experimental results show the USV can successfully avoid the various obstacles in the coastal port, and efficiently plan collision-free paths.
... Some methods can also combine path search with trajectory generation, such as domain reduction-based RRT* [22] and Hybrid A* [23]. In this paper, the proposed method belongs to the numerical optimization method . ...
Efficient trajectory generation in complex dynamic environments remains an open problem in the operation of an unmanned surface vehicle (USV). The perception of a USV is usually interfered by the swing of the hull and the ambient weather, making it challenging to plan optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for a coupled USV-UAV system is proposed to ensure that a USV can execute a safe and smooth path as it autonomously advances through multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. An initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.
... Therefore, it is worthwhile studying the collision avoidance algorithms of USVs with a high response speed and the capacity for autonomous decision-making as this will deeply promote the development and application of USVs. Wen et al. [7] proposed a heuristic dual-sampling domain-reduction-based optimal rapidly exploring random tree scheme to plan for low fuel expenditure, simple execution, and low-collision-probability paths online for USVs under constraints. Zhu et al. [8] proposed an approach to optimize the planned path by comparing the estimated distance with the actual distance between the current waypoint and the target waypoint and used the minimum binary heap to optimize the priority queue of D* Lite, thus significantly reducing the path search time. ...
Although broad reinforcement learning (BRL) provides a more intelligent autonomous decision-making method for the collision avoidance problem of unmanned surface vehicles (USVs), the algorithm still has the problem of over-estimation and has difficulty converging quickly due to the sparse reward problem in a large area of sea. To overcome the dilemma, we propose a double broad reinforcement learning based on hindsight experience replay (DBRL-HER) for the collision avoidance system of USVs to improve the efficiency and accuracy of decision-making. The algorithm decouples the two steps of target action selection and target Q value calculation to form the double broad reinforcement learning method and then adopts hindsight experience replay to allow the agent to learn from the experience of failure in order to greatly improve the sample utilization efficiency. Through training in a grid environment, the collision avoidance success rate of the proposed algorithm was found to be 31.9 percentage points higher than that in the deep Q network (DQN) and 24.4 percentage points higher than that in BRL. A Unity 3D simulation platform with high fidelity was also designed to simulate the movement of USVs. An experiment on the platform fully verified the effectiveness of the proposed algorithm.
... Based on the traditional RRT algorithm, an improved RRT* algorithm is generated which can quickly plan the shortest path in line with the driving of a driverless vehicle by combining it with heuristic double sampling [6]. In mobile robot path planning, MOD-RRT* can select the best node in the shortest time and generate the optimal path [7]. ...
In a complex environment, although the artificial potential field (APF) method of improving the repulsion function solves the defect of local minimum, the planned path has an oscillation phenomenon which cannot meet the vehicle motion. In order to improve the efficiency of path planning and solve the oscillation phenomenon existing in the improved artificial potential field method planning path. This paper proposes to combine the improved artificial potential field method with the rapidly exploring random tree (RRT) algorithm to plan the path. First, the improved artificial potential field method is combined with the RRT algorithm, and the obstacle avoidance method of the RRT algorithm is used to solve the path oscillation; The vehicle kinematics model is then established, and under the premise of ensuring the safety of the vehicle, a model predictive control (MPC) trajectory tracking controller with constraints is designed to verify whether the path planned by the combination of the two algorithms is optimal and conforms to the vehicle motion. Finally, the feasibility of the method is verified in simulation. The simulation results show that the method can effectively solve the problem of path oscillation and can plan the optimal path according to different environments and vehicle motion.
In a large-scale oceanic environment with spatially variable ocean currents, it is vital for unmanned surface vehicles (USVs) to navigate with a safe and energy-efficient path. In this article, a stream-based VF-RRT* (SVF-RRT*) is proposed for path planning in ocean current fields defined by the stream function. Firstly, SVF-RRT* quantifies the extent to which a feasible path goes against currents with a parameter termed
upstream coefficient
. Secondly, a heuristic interval is constructed based on the stream function and adjusted by the coefficient. Then, SVF-RRT* employs biased sampling and tree pruning to facilitate convergence. Sampling nodes possessing a stream value outside the interval are rejected with an adaptive probability. Meanwhile, nodes in the exploration tree outside the interval or with a cost higher than the path are discarded. Furthermore, SVF-RRT* utilizes the upstream coefficient to update the extended direction of the tree. Finally, simulation results show that SVF-RRT* accelerates the convergence of upstream cost and generates a smoother path for USVs.
