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Full-Text Articles in Systems Science
Improved Pbs Algorithm For Multi-Agent Path Planning Based On Conflict Guidance And Punishment Mechanism, Jinbao Zhang, Jianlin Mao, Chengze Qian, Guimi Sun, Kaixin Tong
Improved Pbs Algorithm For Multi-Agent Path Planning Based On Conflict Guidance And Punishment Mechanism, Jinbao Zhang, Jianlin Mao, Chengze Qian, Guimi Sun, Kaixin Tong
Journal of System Simulation
Abstract: To address the bottleneck in which the priority-based search (priority-based search, PBS) algorithm for multi-agent path planning easily falls into conflict loops and generates invalid node expansions in complex scenarios, an improved algorithm based on conflict guidance and a punishment mechanism (improved PBS multi-agent path finding algorithm based on conflict guidance and punishment mechanism, CGP-PBS) was proposed. A conflict-guided node expansion mechanism was constructed; in high-level search, it comprehensively evaluated path cost and the number of conflicts, preferentially expanded child nodes with high potential for conflict resolution, and delayed the expansion of high-conflict nodes, thereby effectively compressing the search …
Multi-Agent Cbs Path Planning Algorithm Based On Minimum Planning Margin First, Longxiao Liang, Jianlin Mao, Niya Wang, Chengyuan Fang, Wenna Zhou
Multi-Agent Cbs Path Planning Algorithm Based On Minimum Planning Margin First, Longxiao Liang, Jianlin Mao, Niya Wang, Chengyuan Fang, Wenna Zhou
Journal of System Simulation
To address the problems of chain effect and insufficient solving efficiency in the conflict tree (CT) expansion of the traditional conflict-based search (CBS) framework, a minimum-margin-first CBS algorithm based on planning margin was proposed. The calculation of planning margin was introduced into the underlying A* search, and the robots with the minimum margin were prioritized in the high-level conflict resolution, to suppress the chain expansion of the CT while ensuring path optimality.Simulation experiments show that the proposed algorithm significantly reduces the amount of CT node expansion and the number of root node conflicts and effectively improves the solving efficiency, …
Auv Path Planning Integrating Local-Global Strategies In Unknown Environments, Wenlong Meng, Yanbo Pu, Ya Gong
Auv Path Planning Integrating Local-Global Strategies In Unknown Environments, Wenlong Meng, Yanbo Pu, Ya Gong
Journal of System Simulation
Abstract: Existing path planning algorithms often struggle to efficiently explore and generate high-quality trajectories. To address this issue, this paper proposes a path planning algorithm that integrates local-global strategies. By employing the rolling window technique, the global one-time path planning problem is transformed into an iterative process of multiple local planning stages. During the global exploration phase, the rolling window is used to determine high-level path branches and to identify branch waypoints, thereby refining the calculation of local paths. In the local exploration phase, an improved RRT-Connect algorithm is proposed, which combines adaptive circular sampling with dynamic step length to …
Robot Path Planning By Reinforcement Learning Based On Sac3q-Hdm, Dequan Li, Wan Xiong
Robot Path Planning By Reinforcement Learning Based On Sac3q-Hdm, Dequan Li, Wan Xiong
Journal of System Simulation
Abstract: To address the issues of overestimated and underestimated biases, low sample utilization rate, and the inability to balance exploration and exploitation in reinforcement learning for path planning, an improved SAC method was proposed. The size balance of entropy was explored and utilized through adaptive temperature coefficient adjustment; on the basis of the SAC framework, a triple Critic architecture was introduced to dynamically weight and fuse the minimum and average values through Qvalue uncertainty, balancing overestimated and underestimated biases. A mixed dynamic sampling experience replay buffer was designed; experience data was partitioned based on reward thresholds; sampling ratios were dynamically …
Research On Uav Path Planning Method Based On Collision Free Trajectory, Jun Xie, Qi Zhang, Yanyun Peng, Haonan Shi, Dongyang Li, Xi Liu
Research On Uav Path Planning Method Based On Collision Free Trajectory, Jun Xie, Qi Zhang, Yanyun Peng, Haonan Shi, Dongyang Li, Xi Liu
Journal of System Simulation
Abstract: In view of the problems of poor quality, long time consumption, and low efficiency of the autonomous path planning method for unmanned aerial vehicles, a path planning method for unmanned aerial vehicles based on a collision-free trajectory was proposed. Under the premise of uncertainty, the time-related virtual points and collision threshold were set; the obstacle was modeled as a rectangle; the interest points around the rectangle were defined. The uncertainty optimization model between the unmanned aerial vehicles and the obstacle was established, so as to obtain the allowable edge of the collision-free trajectory of the unmanned aerial vehicles. The …
An Adaptive Robot Path Planning Based On Improved Rea* Algorithm, Ling Zhu, Jing Li, Zhaohui Zhang
An Adaptive Robot Path Planning Based On Improved Rea* Algorithm, Ling Zhu, Jing Li, Zhaohui Zhang
Journal of System Simulation
Abstract: In order to improve the computational efficiency and path smoothness in a robot's global path planning, an adaptive robot path planning strategy based on an improved unilateral rectangle expansion A*(REA*) algorithm was proposed. The robot's operational safety was ensured by setting a buffer around obstacles. A passable interval formed by unilateral rectangle expansion was used as the operation unit, and bidirectional alternating search was combined to enhance the path planning efficiency. Inspired by potential field theory, the evaluation function was optimized by introducing a vector form to achieve fast adaptive obstacle avoidance. A new path planning strategy was proposed …
Strike Strategy Planning Method Of Unmanned Ground Vehicles Based On Improved Ppo Algorithm, Bingkun Wang, Yue Wang, Mei Yang, Pengnian Zhang, Bohao Fan, Jie Tang
Strike Strategy Planning Method Of Unmanned Ground Vehicles Based On Improved Ppo Algorithm, Bingkun Wang, Yue Wang, Mei Yang, Pengnian Zhang, Bohao Fan, Jie Tang
Journal of System Simulation
Abstract: An improved PPO algorithm based on the hybrid action space and gated recurrent unit (GRU) is proposed to address the limitations of predefined strike rules in maximizing the hitting accuracy of unmanned ground vehicles and the difficult coupling and optimization of continuous motion planning and discrete strike decision-making. The environmental model and target model are built for the process of unmanned ground vehicles' strike missions, coupled with a three-layer model for unmanned ground vehicles that fuses kinematic constraints, situational awareness, and dynamic decision-making. Two distinct policy networks are employed, including the continuous motion planning network for path planning, and …
Improved Pid Search Algorithm For Uav Path Planning In Mountainous Environments, Yi Peng, Yunkui Lei, Qingqing Yang, Hui Li, Jianming Wang
Improved Pid Search Algorithm For Uav Path Planning In Mountainous Environments, Yi Peng, Yunkui Lei, Qingqing Yang, Hui Li, Jianming Wang
Journal of System Simulation
Abstract: To address the challenges of UAV path planning in mountainous environments, including high computational complexity and suboptimal optimization performance, and the disadvantages of the PIDbased search algorithm, such as low optimization accuracy and slow convergence rate, this paper proposed an improved PID search algorithm (IPSA). The method introduced a good point set to ensure a more uniform population distribution, thereby enhancing population diversity and global search capability. The Q-learning algorithm was employed to adapt PID parameter adjustments, incorporating an exploration rate factor to further improve the algorithm's exploration and computational capabilities. A lens imaging opposition-based learning mechanism was also …
A Usv Path Planning Algorithm Under Special Environment Based On Td3-Rrt, Jitong Chen, Jiajia Zhou, Di Wu, Hailong Jiang
A Usv Path Planning Algorithm Under Special Environment Based On Td3-Rrt, Jitong Chen, Jiajia Zhou, Di Wu, Hailong Jiang
Journal of System Simulation
Abstract: In view of USV path planning in special environments such as multiple obstacles, large-size obstacles, and narrow passages, the rapidly-exploring random tree (RRT) algorithm suffers from drawbacks such as a large sampling base, low success rate, and zigzagging planned path. To address these problems, a global path planning algorithm (TD3-RRT) was proposed based on the twin delayed deep deterministic policy gradient (TD3). The USV path search model was established by combining the RRT algorithm with deep reinforcement learning. Forward looking detection was used to sense the environment to adaptively adjust the step size. The path search direction was exported …
Auv Path Planning Based On Behavior Cloning And Improved Dqn In Partially Unknown Environments, Lijing Xing, Min Li, Xiangguang Zeng, Ping Zhang, Bei Peng
Auv Path Planning Based On Behavior Cloning And Improved Dqn In Partially Unknown Environments, Lijing Xing, Min Li, Xiangguang Zeng, Ping Zhang, Bei Peng
Journal of System Simulation
Abstract: To address the problems of large randomness and slow convergence of the DQN dynamic path planning algorithm for a single autonomous underwater vehicle (AUV) in a partially unknown environment, a path planning method combining behavior cloning with A* algorithm and DQN (BA_DQN) was proposed. Based on the known environmental information, an improved A* algorithm incorporating ocean current resistance was proposed to guide DQN, thereby reducing the randomness of the DQN algorithm. By considering the complexity of the marine environment, the sampling probability was improved again after expanding the positive experience pool to enhance the training success rate. To address …
Path Planning For Mobile Robots Based On Improved Artificial Potential Field Algorithm, Chi Zhang, Wei Wei
Path Planning For Mobile Robots Based On Improved Artificial Potential Field Algorithm, Chi Zhang, Wei Wei
Journal of System Simulation
Abstract: In view of the problems of unreachable target areas and easy local minima in traditional artificial potential field methods, an improved artificial potential field method was proposed. The improved algorithm optimized the repulsive field function by introducing obstacle angle factors and distance factors to control the repulsive force magnitude. At the same time, an additional repulsive force towards the target point was added to solve the problem of unreachable target areas in traditional algorithms. When the robot fell into a local minimum, by introducing turning towards obstacles and turning factors to accurately apply escape forces to the robot, the …
Research On Path Smoothing Processing Of Mobile Robot Based On Improved A* Algorithm, Mengyuan Chen, Guifang Qiao, Xu Zou, Jiayu Cao, Lei Tian
Research On Path Smoothing Processing Of Mobile Robot Based On Improved A* Algorithm, Mengyuan Chen, Guifang Qiao, Xu Zou, Jiayu Cao, Lei Tian
Journal of System Simulation
Abstract: Traditional bidirectional A* algorithm has many path inflection points, undergoes smoothness, and faces diagonal obstacles in path traversing. Therefore, an improved bidirectional A* algorithm was proposed. Local path constraint search was added to the forward search and backward search, respectively to solve the problem of planning paths traversing diagonal obstacles, and the effectiveness of the improved bidirectional A* algorithm to avoid traversing diagonal obstacles was verified through simulations. The path inflection points were optimized by introducing the cubic B-spline curve, and the paths before and after smoothing were tracked and controlled, respectively by using the differential-driven mobile robot. The …
Robot Path Planning Optimization Based On Fusion Of Improved Ant Colony Algorithm And A* Algorithm, Lanying Yang, Chao Li, Haifeng Zou, Jiangtao Wan, Renqiang Zhang, Hui Liu, Hong Lu
Robot Path Planning Optimization Based On Fusion Of Improved Ant Colony Algorithm And A* Algorithm, Lanying Yang, Chao Li, Haifeng Zou, Jiangtao Wan, Renqiang Zhang, Hui Liu, Hong Lu
Journal of System Simulation
Abstract: To improve slow search efficiency and achieve real-time obstacle avoidance in traditional ant colony algorithms, an adaptive ant colony algorithm was proposed. A guidance direction mechanism was introduced to shorten the time of node selection. The A* algorithm's path-finding mechanism was introduced into the heuristic function to reduce the length and number of circles of the optimal path solution. The route planned by the traditional A* algorithm was used as the initial iteration data of the ant colony algorithm in global path planning, so as to solve the problem of slow initial convergence of the ant colony algorithm. The …
Low-Energy Multi-Robot Path Planning Algorithm Under Hca* Framework, Ning Wang, Jianlin Mao, Dayan Li, Chengyuan Fang, Chengze Qian
Low-Energy Multi-Robot Path Planning Algorithm Under Hca* Framework, Ning Wang, Jianlin Mao, Dayan Li, Chengyuan Fang, Chengze Qian
Journal of System Simulation
Abstract: To address the energy optimization problem in multi-robot path planning, this paper proposed a multi-robot path planning algorithm based on the energy-guided hierarchical cooperative A* (E-HCA*) algorithm. To address the issue of robot oscillations caused by mutual avoidance at bottlenecks and narrow passages in multi-robot systems, a node expansion method with path length as a secondary feature was introduced, and a greedy suppression strategy under the cooperative A* framework was proposed. A differential-drive robot energy consumption model was established, and an energy-guided heuristic function was constructed by integrating energy metrics into the underlying A* algorithm to guide low-energy path …
Path Planning For Mobile Robots Based On Improved Rrt-Connect And Dwa Fusion, Yi Luo, Jia Deng
Path Planning For Mobile Robots Based On Improved Rrt-Connect And Dwa Fusion, Yi Luo, Jia Deng
Journal of System Simulation
Abstract: To improve the efficiency and quality of dynamic path planning for mobile robots in complex environments, this paper proposed a path planning algorithm that combined an improved RRT-connect with the DWA. Two expanding random trees were introduced for alternating expansion, and a dynamically restricted sampling area was set to reduce the randomness of the sampling process while ensuring the probability completeness of the algorithm. A target bias adaptive step size strategy was employed to enhance the target orientation of the random tree expansion process. A greedy strategy was adopted to prune redundant nodes in the path and smooth the …
Optimization Method For Multi Agricultural Machinery Collaborative Operation Based On Genetic Algorithm And A* Algorithm, Yiran Yu, Huicheng Lai, Guxue Gao, Guo Zhang, Wangyinan Peng, Longfei Yang, Junhao Huang
Optimization Method For Multi Agricultural Machinery Collaborative Operation Based On Genetic Algorithm And A* Algorithm, Yiran Yu, Huicheng Lai, Guxue Gao, Guo Zhang, Wangyinan Peng, Longfei Yang, Junhao Huang
Journal of System Simulation
Abstract: To address the uneven task distribution among multiple agricultural machines (referred to as farm machinery) and the high time cost due to numerous turning points at intersections, this paper proposes a task planning method that combines a pre-heat multi grouped genetic algorithm (PHMGA) with the turn A* algorithm (tA*). PHMGA allocates tasks to each piece of farm machinery based on the known environment, ensuring balanced workload through a cost objective function that considers travel, operation, and turning distances. It also designs various operators and strategies to search for nearoptimal solutions. The tA* algorithm is used to select paths …
Robot Path Planning Based On Improved A-Ddqn Algorithm, Peilong Ni, Pengjun Mao, Ning Wang, Mengjie Yang
Robot Path Planning Based On Improved A-Ddqn Algorithm, Peilong Ni, Pengjun Mao, Ning Wang, Mengjie Yang
Journal of System Simulation
Abstract: An improved A-DDQN algorithm is proposed to address the challenges of reward sparsity and the inability to distinguish sample importance in traditional DQN algorithms during robot path planning. Building on the original DQN, an enhancement is made by incorporating the Double-DQN approach, which updates the predictive Q-value network based on actions selected by the Q network, rather than directly using the predicted Q-values for action selection, thereby mitigating overestimation issues. Secondly, the concept of artificial potential field (APF) is introduced to design specific rewards for each step of the robot's movement, guiding the robot and addressing the problem of …
Multi-Robot Hierarchical Collaborative K-Robust Path Planning For Path Interference, Kaixiang Zhang, Jianlin Mao, Niya Wang, Zhihao Xu
Multi-Robot Hierarchical Collaborative K-Robust Path Planning For Path Interference, Kaixiang Zhang, Jianlin Mao, Niya Wang, Zhihao Xu
Journal of System Simulation
Abstract: To plan collision-free paths for multiple robots in interference environments, based on the multi-robot k-robust path planning, this paper designed a multi-robot hierarchical collaborative k-robust path planning framework. In the priority optimization layer, in response to the starting predicament caused by the solution sequence, the multi-robot path solving sequence was determined based on the closure factor. In the multi-robot robust coordination layer, with the goal of improving solution efficiency, a safety interval was introduced as the basis for the design of k-robustness and collision-free avoidance. A collision-free path constraint for multiple robots in the sense of k-robustness was given. …
Robot Path Planning Based On Ant Colony Algorithm With Dual Heuristic Information, Xiaohui Zhou, Yanqiang Li, Yong Wang, Decai Zhao, Xiaoyao Yang
Robot Path Planning Based On Ant Colony Algorithm With Dual Heuristic Information, Xiaohui Zhou, Yanqiang Li, Yong Wang, Decai Zhao, Xiaoyao Yang
Journal of System Simulation
Abstract: The traditional ant colony algorithm is characterized by a slow convergence speed, numerous turning points, and a tendency to fall into local minima. These characteristics make the algorithm less effective for path planning research in mobile robotics. Therefore, this paper proposes an improved ant colony algorithm and applies it to global path planning for robots. The A* algorithm is used to quickly plan a path and increase the initial pheromone of that path, so that the improved algorithm is guided by the global path during the local search, preventing excessive ants from entering dead ends, and reducing the randomness …
A Multi-Robot Collaborative Path Planning Algorithm With Chain Working Mode, Zhigang He, Dayan Li, Niya Wang, Jianlin Mao, Ning Wang
A Multi-Robot Collaborative Path Planning Algorithm With Chain Working Mode, Zhigang He, Dayan Li, Niya Wang, Jianlin Mao, Ning Wang
Journal of System Simulation
Abstract: In order to solve the problem that the traditional MAPF algorithms can lead to a large number of repeated paths and thus non-essential energy loss in application scenarios where multiple robots have a common goal point, a multi-robot chain work mode with a tractor is proposed, which divides the robots with common target points into subgroups for multi robot collaborative path planning, and a collaborative dynamic priority SIPP with tractor (Co-DPtSIPP) algorithm is given. The polygonal Fermat point principle and other methods are used to obtain the serial connection areas of each collaborative group robot; considering the sequence of …
Uav Path Planning Based On Improved Deep Deterministic Policy Gradients, Sen Zhang, Qiangqiang Dai
Uav Path Planning Based On Improved Deep Deterministic Policy Gradients, Sen Zhang, Qiangqiang Dai
Journal of System Simulation
Abstract: Aiming at the problems of poor convergence and invalid exploration when UAVs perform path planning in complex environments, an improved deep deterministic policy gradient(DDPG) algorithm is proposed. Using a dual experience pooling mechanism to store success and failure experiences separately, the algorithm is able to use the success experience to strengthen the strategy optimization and learn from the failure experience to avoid the wrong path; an APF method is introduced to add a bootstrap term to the planning, which is combined with the exploration of noisy actions in a randomized sampling process to dynamically integrate the selected actions; multi-objective …
Mobile Robot Path Planning Based On Search-Step Optimized A* Algorithm, Die Yu, Baizhong Bao, Yan Si, Jian Duan, Xiaobin Zhan, Tielin Shi
Mobile Robot Path Planning Based On Search-Step Optimized A* Algorithm, Die Yu, Baizhong Bao, Yan Si, Jian Duan, Xiaobin Zhan, Tielin Shi
Journal of System Simulation
Abstract: A search-step optimized A* algorithm is proposed to address the issues with the traditional A* algorithm in robot path planning tasks, such as the high time consumption in large-scale high-resolution maps and the poor paths qualitys. Based on the cubic Hermite curve, a set of search steps (the path edges connecting the current node to its successors) is constructed, which can match the size of the robot and satisfy the dynamic constraints of the robot. More accurate cost functions are established based on the length and maximum absolute curvature value of the curve. Experimental results show that compared with …
Research On Robot Path Planning Based On Improved Harris Hawks Algorithm, Yuxin Bai, Zhenya Chen, Ruitao Shi, Weitao Su, Zhuoqiang Ma, Shangjin Yang
Research On Robot Path Planning Based On Improved Harris Hawks Algorithm, Yuxin Bai, Zhenya Chen, Ruitao Shi, Weitao Su, Zhuoqiang Ma, Shangjin Yang
Journal of System Simulation
Abstract: In order to improve the convergence accuracy of the HHO algorithm, this paper proposes a GSHHO(gold sine harris hawks optimization) algorithm based on multi-strategies. An infinite iterative chaotic map is used to initialize the population, and an elite reverse learning strategy is used to improve population quality; A convergence factor adjustment strategy is used to recalculate prey energy, balancing the global exploration and local development capabilities of the algorithm; In the development phase of Harris Eagle, the golden sine strategy was introduced to replace the original position update method and improve the local development ability of the algorithm; Experiments …
Research On Improved A* Algorithm Path Planning Based On Global Key Point Extraction, Guijuan Lin, Zihan Li, Yu Wang
Research On Improved A* Algorithm Path Planning Based On Global Key Point Extraction, Guijuan Lin, Zihan Li, Yu Wang
Journal of System Simulation
Abstract: To address the limitations of the traditional A* algorithm in large and complex scenes, including traversing a large number of nodes, long computation times, and susceptibility to U-shaped traps, this paper proposes an improved A* algorithm incorporating the jump point search (JPS) concept and image processing techniques to extract key points from the global map. The proposed method preprocesses the global map to identify corner points located one grid diagonally from obstacles, constructs a key point list, and replaces the nodes traditionally traversed by the A* algorithm with these global key points, significantly reducing computational overhead. The neighbor nodes …
Multi-Strategy Hybrid Mountain Gazelle Optimizer For Robot Path Planning, Xu Jin, Yuanbin Mo
Multi-Strategy Hybrid Mountain Gazelle Optimizer For Robot Path Planning, Xu Jin, Yuanbin Mo
Journal of System Simulation
Abstract: Aiming at the problems of local optimum and premature convergence in the design of optimization path of robot navigation system, a multi-strategy hybrid MGO(HMGO) improved algorithm based on the mountain gazelle optimizer(MGO) is proposed. The algorithm uses the quasi-reverse learning strategy to optimize the population initialization ensuring its diversity, introduces the dynamic adaptive density factor to adjust the parameters of the optimization mechanism, and integrates arithmetic optimization and sine-cosine strategies for random perturbations. Through ablation experiments, 13 benchmark test functions, and simulation experiments on the solution of two-dimensional and threedimensional space robot path planning problems, the results demonstrate that …
Improved Bidirectional A* Quadratic Path Planning Algorithm For Mobile Robots, Jiongyi Li, Qiang Li, Xinwen Zhang, Myo Htet Zin, Yongbin Cai
Improved Bidirectional A* Quadratic Path Planning Algorithm For Mobile Robots, Jiongyi Li, Qiang Li, Xinwen Zhang, Myo Htet Zin, Yongbin Cai
Journal of System Simulation
Abstract: Aiming at the problems of the traditional A* algorithm, such as the unhoped intersection between the planned path and the obstacles, the planned path has many inflection points and the search time is long, an improved bidirectional A* quadratic path planning algorithm for the indoor environments is proposed. Through the expansion of the map, the intersection between the planned path and the obstacle is solved. By new heuristic functions and bidirectional expansion methods, the search speed and accuracy of the bidirectional A* algorithm are improved. Turning cost function and adaptive weight are introduced to reduce the number of turning …
Design Of Robust Behavior Tree Control Architecture For Agents In Dynamic Task Environment, Qiwei Wang, Qi Zhang, Shuo Yang, Yong Peng
Design Of Robust Behavior Tree Control Architecture For Agents In Dynamic Task Environment, Qiwei Wang, Qi Zhang, Shuo Yang, Yong Peng
Journal of System Simulation
Abstract: In recent years, the environment in which agents perform tasks has become more open and dynamic, which puts forward higher requirements for the robustness of task planning and behavior scheduling of agents. As a classic behavior control architecture, behavior tree has the characteristics of modularity, behavior parameterization, and structure of both plan representation and reaction, which can effectively support the behavior representation, decision making and scheduling of agents. Based on the hybrid behavior strategy , this paper proposes a robust behavior tree control architecture for dynamic task environment to realize the prudent decision-making and reactive control of agents. The …
Research On Path Planning Method For Autonomous Underwater Vehicles Based On Improved Informed Rrt, Bensheng Qi, Yan Li, Hongxia Miao, Jialin Chen, Chenglin Li
Research On Path Planning Method For Autonomous Underwater Vehicles Based On Improved Informed Rrt, Bensheng Qi, Yan Li, Hongxia Miao, Jialin Chen, Chenglin Li
Journal of System Simulation
Abstract: In response to the autonomous underwater vehicle (AUV) path planning problem in complex underwater environments, an improved path planning algorithm based on Informed rapidly-exploring random trees (RRT) is proposed in this study. A target-biased sampling strategy and a target-biased extension strategy are employed to address the issue of lack of goal orientation in the sampling process, ensuring that target nodes become sampling points during random sampling. During path points extension, non-target sampling points are guided in the direction of the target point, thereby enhancing the algorithm's ability to search for the target during random sampling and extension processes. A …
Path Planning Of Mobile Robot Based On The Integration Of Multi-Scale A* And Optimized Dwa Algorithm, Jianmin Xu, Lei Song, Dongdong Deng, Yaoruo Chen, Wei Yang
Path Planning Of Mobile Robot Based On The Integration Of Multi-Scale A* And Optimized Dwa Algorithm, Jianmin Xu, Lei Song, Dongdong Deng, Yaoruo Chen, Wei Yang
Journal of System Simulation
Abstract: In order to solve the problems of sharply increasing computational and time costs, as well as poor flexibility of the traditional A* algorithm and dynamic window approach (DWA) in the face of largescale complex environmental path planning, a fusion algorithm based on the A* algorithm of the multiscale map approach(MMA) and the improved DWA algorithm is proposed. A multi-scale map set is established and an obstacle proportion factor is added to the heuristic function of the A* algorithm. The A* algorithm is used to calculate the optimal path on the coarse-scale map, and the optimal path is mapped onto …
Path Planning Of Desert Robot Based On Deep Reinforcement Learning, Ming Li, Wangzhong Ye, Jiehua Yan
Path Planning Of Desert Robot Based On Deep Reinforcement Learning, Ming Li, Wangzhong Ye, Jiehua Yan
Journal of System Simulation
Abstract: Due to the complexity and variability of the desert environment, the key to the high-efficient of mobile robot is how to avoid obstacles and plan its path. To solve the problems of poor search efficiency and slow convergence of deep reinforcement learning algorithm in complex environment, an improved deep reinforcement learning path planning algorithm is proposed. The exploration factor is improved and dynamically adjusted according to the convergence degree of the algorithm, so that the exploration factor dynamically decreases with the increase of the understanding degree of the agent to the environment, thus speeding up the convergence speed of …