面向平层多房间的内墙作业移动机器人路径规划
Mobile Robot Path Planning for Interior Wall Operation in Flat Multi-room
靳徐明 1林云汉 2张磊 3闵华松4
作者信息
- 1. 武汉科技大学计算机科学与技术学院,武汉 430065;武汉科技大学智能信息处理与实时工业系统湖北省重点实验室,武汉 430065
- 2. 武汉科技大学计算机科学与技术学院,武汉 430065;武汉科技大学智能信息处理与实时工业系统湖北省重点实验室,武汉 430065;武汉科技大学机器人与智能系统研究院,武汉 430065
- 3. 北京电子科技职业学院汽车工程学院,北京 100081
- 4. 武汉科技大学机器人与智能系统研究院,武汉 430065
- 折叠
摘要
本文针对多房间的移动机器人内墙作业的路径规划任务,提出一种两阶段路径规划方法.第 1 阶段针对沿墙作业过程中环境存在灰尘或雾气造成的传感器失效问题,以及房间多出口时路径规划不完整问题,我们提出起点自动选择沿墙路径规划方法,基于栅格地图离线生成沿墙规划路径.第 2 阶段,针对点到点路径规划过程中的动态避障问题,我们提出一种基于PSAC(prioritized experience replay soft actor critic)算法的点到点路径规划方法,在软行动者-评论家(soft actor critic,SAC)的中引入优先级经验回放策略,实现机器人的动态避障.实验部分设计了沿墙路径规划对比实验和动态避障的对比实验,验证本文所提出的方法在室内沿墙路径规划和点到点路径规划的有效性.
Abstract
This study proposes a two-stage path planning method for the path planning task of the inner wall operation of a mobile robot in multi-room.In the first stage,for the sensor failure caused by dust or fog in the environment during wall operation and incomplete path planning when there are many exits in a room,the study proposes a start-point automatically selected wall following path planning method,which is based on grid maps to generate the wall following paths offline.In the second stage,for the dynamic obstacle avoidance problem during point-to-point path planning,it proposes a point-to-point path planning method based on the prioritized experience replay soft actor critic(PSAC)algorithm,which introduces the prioritized experience playback strategy in the soft actor critic(SAC)to achieve dynamic obstacle avoidance.The comparison experiments of wall following path planning and dynamic obstacle avoidance are designed to verify the effectiveness of the proposed method in the indoor wall following path planning and point-to-point path planning.
关键词
两阶段路径规划方法/沿墙路径规划/强化学习/PSACKey words
two-stage path planning method/wall following path planning/reinforcement learning/prioritized experience replay soft actor critic(PSAC)引用本文复制引用
基金项目
国家重点研发计划(2022YFB4700400)
国家自然科学基金(62073249)
湖北省重点研发计划(2023BBB011)
出版年
2024
NETL
NSTL
万方数据