Research on Dynamic Path Planning of Wheeled Robot Based on Deep Reinforcement Learning on the Slope Ground

IF 1.4 Q4 ROBOTICS
W. Peng, Xiaoqiang Li, Chunxiao Song, S. Zhai
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引用次数: 6

Abstract

The existing dynamic path planning algorithm cannot properly solve the problem of the path planning of wheeled robot on the slope ground with dynamic moving obstacles. To solve the problem of slow convergence rate in the training phase of DDQN, the dynamic path planning algorithm based on Tree-Double Deep Q Network (TDDQN) is proposed. The algorithm discards detected incomplete and over-detected paths by optimizing the tree structure, and combines the DDQN method with the tree structure method. Firstly, DDQN algorithm is used to select the best action in the current state after performing fewer actions, so as to obtain the candidate path that meets the conditions. And then, according to the obtained state, the above process is repeatedly executed to form multiple paths of the tree structure. Finally, the non-maximum suppression method is used to select the best path from the plurality of eligible candidate paths. ROS simulation and experiment verify that the wheeled robot can reach the target effectively on the slope ground with moving obstacles. The results show that compared with DDQN algorithm, TDDQN has the advantages of fast convergence and low loss function.
基于深度强化学习的轮式机器人斜坡路面动态路径规划研究
现有的动态路径规划算法不能很好地解决轮式机器人在有动态移动障碍物的斜坡地面上的路径规划问题。为解决DDQN在训练阶段收敛速度慢的问题,提出了一种基于树双深度Q网络的动态路径规划算法。该算法通过优化树形结构,丢弃检测到的不完整和过检测路径,并将DDQN方法与树形结构方法相结合。首先,采用DDQN算法,在执行较少的动作后,选择当前状态下的最佳动作,从而得到满足条件的候选路径。然后,根据得到的状态,重复执行上述过程,形成树形结构的多条路径。最后,采用非极大值抑制方法从多个符合条件的候选路径中选择最佳路径。ROS仿真和实验验证了轮式机器人在有移动障碍物的斜坡地面上能够有效地到达目标。结果表明,与DDQN算法相比,TDDQN具有收敛速度快、损失函数小的优点。
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来源期刊
CiteScore
3.70
自引率
5.60%
发文量
77
审稿时长
22 weeks
期刊介绍: Journal of Robotics publishes papers on all aspects automated mechanical devices, from their design and fabrication, to their testing and practical implementation. The journal welcomes submissions from the associated fields of materials science, electrical and computer engineering, and machine learning and artificial intelligence, that contribute towards advances in the technology and understanding of robotic systems.
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