Bidirectional Obstacle Avoidance Enhancement‐Deep Deterministic Policy Gradient: A Novel Algorithm for Mobile‐Robot Path Planning in Unknown Dynamic Environments

Real‐time path planning in unknown dynamic environments is a significant challenge for mobile robots. Many researchers have attempted to solve this problem by introducing deep reinforcement learning, which trains agents through interaction with their environments. A method called BOAE‐DDPG, which co...

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Published in:Advanced intelligent systems Vol. 6; no. 4
Main Authors: Xue, Junxiao, Zhang, Shiwen, Lu, Yafei, Yan, Xiaoran, Zheng, Yuanxun
Format: Journal Article
Language:English
Published: Weinheim John Wiley & Sons, Inc 01.04.2024
Wiley
Subjects:
Algorithms
Artificial intelligence
Attention
Behavior
cross‐attention mechanisms
deep deterministic policy gradients
Deep learning
deep reinforcement learning
dynamic psychologies
Lasers
Machine learning
mobile robot path planning
Neural networks
Obstacle avoidance
Path planning
Planning
Robot dynamics
Robots
Unknown environments
Velocity
ISSN:2640-4567, 2640-4567
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Abstract Real‐time path planning in unknown dynamic environments is a significant challenge for mobile robots. Many researchers have attempted to solve this problem by introducing deep reinforcement learning, which trains agents through interaction with their environments. A method called BOAE‐DDPG, which combines the novel bidirectional obstacle avoidance enhancement (BOAE) mechanism with the deep deterministic policy gradient (DDPG) algorithm, is proposed to enhance the learning ability of obstacle avoidance. Inspired by the analysis of the reaction advantage in dynamic psychology, the BOAE mechanism focuses on obstacle‐avoidance reactions from the state and action. The cross‐attention mechanism is incorporated to enhance the attention to valuable obstacle‐avoidance information. Meanwhile, the obstacle‐avoidance behavioral advantage is separately estimated using the modified dueling network. Based on the learning goals of the mobile robot, new assistive reward factors are incorporated into the reward function to promote learning and convergence. The proposed method is validated through several experiments conducted using the simulation platform Gazebo. The results show that the proposed method is suitable for path planning tasks in unknown environments and has an excellent obstacle‐avoidance learning capability. A novel deep reinforcement learning‐based method called bidirectional obstacle avoidance enhancement‐deep deterministic policy gradient (BOAE‐DDPG) for mobile‐robot path planning in unknown dynamic environments is proposed. The core BOAE mechanism is inspired by dynamic psychology, making BOAE‐DDPG better at learning obstacle avoidance without relying on environmental information. In addition, new assisted reward factors designed for path planning promote learning and convergence.
AbstractList Real‐time path planning in unknown dynamic environments is a significant challenge for mobile robots. Many researchers have attempted to solve this problem by introducing deep reinforcement learning, which trains agents through interaction with their environments. A method called BOAE‐DDPG, which combines the novel bidirectional obstacle avoidance enhancement (BOAE) mechanism with the deep deterministic policy gradient (DDPG) algorithm, is proposed to enhance the learning ability of obstacle avoidance. Inspired by the analysis of the reaction advantage in dynamic psychology, the BOAE mechanism focuses on obstacle‐avoidance reactions from the state and action. The cross‐attention mechanism is incorporated to enhance the attention to valuable obstacle‐avoidance information. Meanwhile, the obstacle‐avoidance behavioral advantage is separately estimated using the modified dueling network. Based on the learning goals of the mobile robot, new assistive reward factors are incorporated into the reward function to promote learning and convergence. The proposed method is validated through several experiments conducted using the simulation platform Gazebo. The results show that the proposed method is suitable for path planning tasks in unknown environments and has an excellent obstacle‐avoidance learning capability.
Real‐time path planning in unknown dynamic environments is a significant challenge for mobile robots. Many researchers have attempted to solve this problem by introducing deep reinforcement learning, which trains agents through interaction with their environments. A method called BOAE‐DDPG, which combines the novel bidirectional obstacle avoidance enhancement (BOAE) mechanism with the deep deterministic policy gradient (DDPG) algorithm, is proposed to enhance the learning ability of obstacle avoidance. Inspired by the analysis of the reaction advantage in dynamic psychology, the BOAE mechanism focuses on obstacle‐avoidance reactions from the state and action. The cross‐attention mechanism is incorporated to enhance the attention to valuable obstacle‐avoidance information. Meanwhile, the obstacle‐avoidance behavioral advantage is separately estimated using the modified dueling network. Based on the learning goals of the mobile robot, new assistive reward factors are incorporated into the reward function to promote learning and convergence. The proposed method is validated through several experiments conducted using the simulation platform Gazebo. The results show that the proposed method is suitable for path planning tasks in unknown environments and has an excellent obstacle‐avoidance learning capability. A novel deep reinforcement learning‐based method called bidirectional obstacle avoidance enhancement‐deep deterministic policy gradient (BOAE‐DDPG) for mobile‐robot path planning in unknown dynamic environments is proposed. The core BOAE mechanism is inspired by dynamic psychology, making BOAE‐DDPG better at learning obstacle avoidance without relying on environmental information. In addition, new assisted reward factors designed for path planning promote learning and convergence.
Author Yan, Xiaoran
Zheng, Yuanxun
Xue, Junxiao
Zhang, Shiwen
Lu, Yafei
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Snippet Real‐time path planning in unknown dynamic environments is a significant challenge for mobile robots. Many researchers have attempted to solve this problem by...
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SubjectTerms Algorithms
Artificial intelligence
Attention
Behavior
cross‐attention mechanisms
deep deterministic policy gradients
Deep learning
deep reinforcement learning
dynamic psychologies
Lasers
Machine learning
mobile robot path planning
Neural networks
Obstacle avoidance
Path planning
Planning
Robot dynamics
Robots
Unknown environments
Velocity
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Title Bidirectional Obstacle Avoidance Enhancement‐Deep Deterministic Policy Gradient: A Novel Algorithm for Mobile‐Robot Path Planning in Unknown Dynamic Environments
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