scholarly journals Multi-robot path planning based on a deep reinforcement learning DQN algorithm

2020 ◽  
Vol 5 (3) ◽  
pp. 177-183
Author(s):  
Yang Yang ◽  
Li Juntao ◽  
Peng Lingling
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Robot Path Planning ◽  
Robot Path ◽  
Multi Robot

scholarly journals Applications and Challenges of Deep Reinforcement Learning in Multi-robot Path Planning

2021 ◽  
Vol 5 (6) ◽  
pp. 25-29
Author(s):  
Tianyun Qiu ◽  
Yaxuan Cheng
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Research Area ◽  
Training Methods ◽  
Multi Agent Systems ◽  
Robot Path Planning ◽  
Practical Applications ◽  
Multi Agent ◽  
Robot Path ◽  
Multi Robot

With the rapid advancement of deep reinforcement learning (DRL) in multi-agent systems, a variety of practical application challenges and solutions in the direction of multi-agent deep reinforcement learning (MADRL) are surfacing. Path planning in a collision-free environment is essential for many robots to do tasks quickly and efficiently, and path planning for multiple robots using deep reinforcement learning is a new research area in the field of robotics and artificial intelligence. In this paper, we sort out the training methods for multi-robot path planning, as well as summarize the practical applications in the field of DRL-based multi-robot path planning based on the methods; finally, we suggest possible research directions for researchers.

Research and Implementation of Multi-robot Path Planning Based on Genetic Algorithm

2021 ◽  
Author(s):  
Mengqing Fan ◽  
Jiawang He ◽  
Susheng Ding ◽  
Yuanhao Ding ◽  
Meng Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Robot Path Planning ◽  
Robot Path ◽  
Multi Robot

scholarly journals Multi-robot path planning using an improved self-adaptive particle swarm optimization

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142093615
Author(s):  
Biwei Tang ◽  
Kui Xiang ◽  
Muye Pang ◽  
Zhu Zhanxia
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Evolutionary Game ◽  
Particle Swarm ◽  
Computation Time ◽  
Robot Path Planning ◽  
Swarm Optimization ◽  
Robot Path ◽  
Self Adaptive ◽  
Multi Robot

Path planning is of great significance in motion planning and cooperative navigation of multiple robots. Nevertheless, because of its high complexity and nondeterministic polynomial time hard nature, efficiently tackling with the issue of multi-robot path planning remains greatly challenging. To this end, enhancing a coevolution mechanism and an improved particle swarm optimization (PSO) algorithm, this article presents a coevolution-based particle swarm optimization method to cope with the multi-robot path planning issue. Attempting to well adjust the global and local search abilities and address the stagnation issue of particle swarm optimization, the proposed particle swarm optimization enhances a widely used standard particle swarm optimization algorithm with the evolutionary game theory, in which a novel self-adaptive strategy is proposed to update the three main control parameters of particles. Since the convergence of particle swarm optimization significantly influences its optimization efficiency, the convergence of the proposed particle swarm optimization is analytically investigated and a parameter selection rule, sufficiently guaranteeing the convergence of this particle swarm optimization, is provided in this article. The performance of the proposed planning method is verified through different scenarios both in single-robot and in multi-robot path planning problems. The numerical simulation results reveal that, compared to its contenders, the proposed method is highly promising with respect to the path optimality. Also, the computation time of the proposed method is comparable with those of its peers.

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