运动规划
强化学习
平滑的
运动(物理)
路径(计算)
计算机科学
平滑度
人工智能
适应性
平面图(考古学)
机器人
运动控制
控制工程
模拟
工程类
计算机视觉
数学
历史
数学分析
生态学
考古
生物
程序设计语言
作者
Dong Zhang,Renjie Ju,Zhengcai Cao
出处
期刊:Robotica
[Cambridge University Press]
日期:2024-02-12
卷期号:42 (4): 947-961
标识
DOI:10.1017/s0263574723001613
摘要
Abstract Snake robots can move flexibly due to their special bodies and gaits. However, it is difficult to plan their motion in multi-obstacle environments due to their complex models. To solve this problem, this work investigates a reinforcement learning-based motion planning method. To plan feasible paths, together with a modified deep Q-learning algorithm, a Floyd-moving average algorithm is proposed to ensure smoothness and adaptability of paths for snake robots’ passing. An improved path integral algorithm is used to work out gait parameters to control snake robots to move along the planned paths. To speed up the training of parameters, a strategy combining serial training, parallel training, and experience replaying modules is designed. Moreover, we have designed a motion planning framework consists of path planning, path smoothing, and motion planning. Various simulations are conducted to validate the effectiveness of the proposed algorithms.
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