Cooperation in a distributed hybrid potential-field/reinforcement learning multi-agents-based autonomous path planning in a dynamic time-varying unstructured environment

2012 ◽  
Author(s):  
Dalila B. Megherbi ◽  
Vikram Malayia
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Potential Field ◽  
Time Varying ◽  
Unstructured Environment ◽  
Dynamic Time ◽  
Multi Agents

scholarly journals Path Planning Method With Improved Artificial Potential Field—A Reinforcement Learning Perspective

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 135513-135523
Author(s):  
Qingfeng Yao ◽  
Zeyu Zheng ◽  
Liang Qi ◽  
Haitao Yuan ◽  
Xiwang Guo ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Potential Field ◽  
Planning Method ◽  
Artificial Potential Field

scholarly journals Path Planning for Unmanned Underwater Vehicle Based on Improved Particle Swarm Optimization Method

2018 ◽  
Vol 14 (12) ◽  
pp. 137
Author(s):  
Jianhua Xu ◽  
Hao Gu ◽  
Hongtao Liang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Potential Field ◽  
Particle Swarm ◽  
Underwater Vehicle ◽  
Time Varying ◽  
Slowly Varying Function ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Unmanned Underwater Vehicle

Path planning of Unmanned Underwater Vehicle (UUV) is of considerable significance for the underwater navigation, the objective of the path planning is to find an optimal collision-free and the shortest trajectory from the start to the destination. In this paper, a new improved particle swarm optimization (IPSO) was proposed to process the global path planning in a static underwater environment for UUV. Firstly, the path planning principle for UUV was established, in which three cost functions, path length, exclusion potential field between the UUV and obstacle, and attraction potential field between UUV and destination, were considered and developed as an optimization objective. Then, on the basis of analysis traditional particle swarm optimization (PSO), the time-varying acceleration coefficients and slowly varying function were employed to improve performance of PSO, time-varying acceleration coefficients was utilized to balance the local optimum and global optimum, and slowly varying function was introduced into the updating formula of PSO to expand search space and maintain particle diversity. Finally, numerical simulations verify that, the proposed approach can fulfill path planning problems for UUN successfully.

Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zheng Fang ◽  
Xifeng Liang
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Content Type ◽  
Artificial Potential Field Method

Purpose The results of obstacle avoidance path planning for the manipulator using artificial potential field (APF) method contain a large number of path nodes, which reduce the efficiency of manipulators. This paper aims to propose a new intelligent obstacle avoidance path planning method for picking robot to improve the efficiency of manipulators. Design/methodology/approach To improve the efficiency of the robot, this paper proposes a new intelligent obstacle avoidance path planning method for picking robot. In this method, we present a snake-tongue algorithm based on slope-type potential field and combine the snake-tongue algorithm with genetic algorithm (GA) and reinforcement learning (RL) to reduce the path length and the number of path nodes in the path planning results. Findings Simulation experiments were conducted with tomato string picking manipulator. The results showed that the path length is reduced from 4.1 to 2.979 m, the number of nodes is reduced from 31 to 3 and the working time of the robot is reduced from 87.35 to 37.12 s, after APF method combined with GA and RL. Originality/value This paper proposes a new improved method of APF, and combines it with GA and RL. The experimental results show that the new intelligent obstacle avoidance path planning method proposed in this paper is beneficial to improve the efficiency of the robotic arm. Graphical abstract Figure 1 According to principles of bionics, we propose a new path search method, snake-tongue algorithm, based on a slope-type potential field. At the same time, we use genetic algorithm to strengthen the ability of the artificial potential field method for path searching, so that it can complete the path searching in a variety of complex obstacle distribution situations with shorter path searching results. Reinforcement learning is used to reduce the number of path nodes, which is good for improving the efficiency of robot work. The use of genetic algorithm and reinforcement learning lays the foundation for intelligent control.

Sign in / Sign up

Export Citation Format

Share Document