A welding manipulator path planning method combining reinforcement learning and intelligent optimisation algorithm

2019 ◽  
Vol 33 (3) ◽  
pp. 261
Author(s):  
Xianyun Duan ◽  
Junhua Zhang ◽  
Lianglun Cheng ◽  
Tao Wang ◽  
Wenya Xia ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Planning Method ◽  
Optimisation Algorithm ◽  
Intelligent Optimisation

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

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.

scholarly journals Multi-UAV Collaborative Path Planning Method Based on Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Tingzhong Wang ◽  
Binbin Zhang ◽  
Mengyan Zhang ◽  
Sen Zhang
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Support System ◽  
Empirical Model ◽  
Path Length ◽  
Planning Method ◽  
Simulation Data ◽  
Time Requirements ◽  
Multi Uav

Aiming at the problem that traditional heuristic algorithm is difficult to extract the empirical model in time from large sample terrain data, a multi-UAV collaborative path planning method based on attention reinforcement learning is proposed. The method draws on a combined consideration of influencing factors, such as survival probability, path length, and load balancing and endurance constraints, and works as a support system for multimachine collaborative optimizing. The attention neural network is used to generate the cooperative reconnaissance strategy of the UAV, and a large amount of simulation data is tested to optimize the attention network using the REINFORCE algorithm. Experimental results show that the proposed method is effective in solving the multi-UAV path planning issue with high real-time requirements, and the solving time is less than the traditional algorithms.

scholarly journals Reinforcement Learning Path Planning Method with Error Estimation

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 247
Author(s):  
Feihu Zhang ◽  
Can Wang ◽  
Chensheng Cheng ◽  
Dianyu Yang ◽  
Guang Pan
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Error Estimation ◽  
Autonomous Driving ◽  
Planning Method ◽  
Robot Kinematics ◽  
Learning Path ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Accumulated Error

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.

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