基于Q-learning的搜救机器人自主路径规划  

Q-learning Based Autonomous Path Planning for Search and Rescue Robots

作　　者：褚晶 邓旭辉 岳颀 CHU Jing;DENG Xuhui;YUE Qi(School of Automation,Xi’an University of Posts and Telecommunications,Xi’an 710121,China)

机构地区：[1]西安邮电大学自动化学院,西安710121

出　　处：《南京航空航天大学学报》2024年第2期364-374,共11页Journal of Nanjing University of Aeronautics & Astronautics

基　　金：国家自然科学基金(61703336);陕西省自然科学基金(2023-JC-QN-0727)。

摘　　要：当人为和自然灾害突然发生时,在极端情况下快速部署搜救机器人是拯救生命的关键。为了完成救援任务,搜救机器人需要在连续动态未知环境中,自主进行路径规划以到达救援目标位置。本文提出了一种搜救机器人传感器配置方案,应用基于Q⁃table和神经网络的Q⁃learning算法,实现搜救机器人的自主控制,解决了在未知环境中如何避开静态和动态障碍物的路径规划问题。如何平衡训练过程的探索与利用是强化学习的挑战之一,本文在贪婪搜索和Boltzmann搜索的基础上,提出了对搜索策略进行动态选择的混合优化方法。并用MATLAB进行了仿真,结果表明所提出的方法是可行有效的。采用该传感器配置的搜救机器人能够有效地响应环境变化,到达目标位置的同时成功避开静态、动态障碍物。When man⁃made or natural disasters occur suddenly,the rapid deployment of search and rescue(SAR)robots is crucial for saving lives.To accomplish rescue tasks,SAR robots need to autonomously plan paths in continuously dynamic and unknown environments to reach the rescue target locations.This paper proposes a sensor configuration scheme for SAR robots,applying a Q-learning algorithm based on Q-table and neural networks to achieve autonomous control of SAR robots.It addresses the challenge of path planning in unknown environments,specifically how to avoid static and dynamic obstacles.Balancing the exploration and exploitation during the training process is one of the challenges in reinforcement learning.This paper introduces a mixed optimization method for dynamically selecting search strategies,building upon greedy search and Boltzmann search.Simulations are conducted using MATLAB,and the results indicate that the proposed method is feasible and effective.SAR robots equipped with the sensor configuration can effectively respond to environmental changes,reaching target locations while successfully avoiding both static and dynamic obstacles.

关 键 词：搜救机器人 路径规划 传感器配置 Q⁃learning 神经网络 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]