scholarly journals Research on Dynamic Path Planning of Mobile Robot Based on Improved DDPG Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Peng Li ◽  
Xiangcheng Ding ◽  
Hongfang Sun ◽  
Shiquan Zhao ◽  
Ricardo Cajo
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Success Rate ◽  
Dynamic Environment ◽  
Simulation Software ◽  
Convergence Speed ◽  
Good Effect ◽  
Dynamic Path Planning ◽  
Experience Replay ◽  
Dynamic Path

Aiming at the problems of low success rate and slow learning speed of the DDPG algorithm in path planning of a mobile robot in a dynamic environment, an improved DDPG algorithm is designed. In this article, the RAdam algorithm is used to replace the neural network optimizer in DDPG, combined with the curiosity algorithm to improve the success rate and convergence speed. Based on the improved algorithm, priority experience replay is added, and transfer learning is introduced to improve the training effect. Through the ROS robot operating system and Gazebo simulation software, a dynamic simulation environment is established, and the improved DDPG algorithm and DDPG algorithm are compared. For the dynamic path planning task of the mobile robot, the simulation results show that the convergence speed of the improved DDPG algorithm is increased by 21%, and the success rate is increased to 90% compared with the original DDPG algorithm. It has a good effect on dynamic path planning for mobile robots with continuous action space.

scholarly journals A Novel GRU-RNN Network Model for Dynamic Path Planning of Mobile Robot

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 15140-15151 ◽  
Author(s):  
Jianya Yuan ◽  
Hongjian Wang ◽  
Changjian Lin ◽  
Dawei Liu ◽  
Dan Yu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Network Model ◽  
Dynamic Path Planning ◽  
Dynamic Path

Path Planning for Mobile Robot Based on Improved Ant Colony Algorithm

2013 ◽  
Vol 385-386 ◽  
pp. 717-720 ◽  
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Probability Function ◽  
Ant Colony ◽  
Local Optimum ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Improved Ant Colony Algorithm ◽  
Improved Algorithm

This paper presents a dynamic path planning method based on improved ant colony algorithm. In order to increasing the algorithm’s convergence speed and avoiding to fall into local optimum, we propose adaptive migratory probability function and updating the pheromone. We apply the improved algorithm to path planning for mobile robot and the simulation experiment proved that improved algorithm is viable and efficient.

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