[1]赵昀,陈庆伟,胡维礼,等.基于状态敏感度的移动机器人路径规划[J].南京理工大学学报(自然科学版),2012,36(01):7-11.
 ZHAO Yun,CHEN Qing-wei,HU Wei-li.Mobile Robot Path Planning Based on State Sensitivity[J].Journal of Nanjing University of Science and Technology,2012,36(01):7-11.
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基于状态敏感度的移动机器人路径规划
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
36卷
期数:
2012年01期
页码:
7-11
栏目:
出版日期:
2012-02-29

文章信息/Info

Title:
Mobile Robot Path Planning Based on State Sensitivity
作者:
赵昀; 陈庆伟; 胡维礼;
南京理工大学自动化学院;
Author(s):
ZHAO YunCHEN Qing-weiHU Wei-li
School of Automation,NUST,Nanjing 210094,China
关键词:
移动机器人 路径规划 强化学习 状态敏感度
Keywords:
mobile robots path planning reinforcement learning state sensitivity
分类号:
TP242
摘要:
针对未知环境下的移动机器人系统,研究了使机器人能同时躲避静态和动态障碍物、且快速抵达目标的路径规划问题。首先通过定义一种新的状态敏感度测度,度量状态与目标之间的关联程度,指导机器人对环境的自主探索方向和力度,进而利用强化学习获得机器人的最优行动策略。通过引入状态敏感度测度,提高算法的学习速度、学习性能。最后通过对环境未知、且具有动态障碍物的路径规划任务的实例仿真,验证了所提方法的有效性。
Abstract:
Aiming at the mobile robot system in an unknown environment,the path planning problem to avoid both static and dynamic barriers and to reach a target quickly is investigated here.A new state sensitivity is defined to measure the relative degree between the state and the objective.It guides a robot to explore the environment with right direction and strength automatically.A reinforcement learning algorithm is adopted to learn the best action policy of a robot.By introducing state sensitivity,the speed and performance of learning algorithm are improved.Simulation results from a path planning task with an unknown environment and dynamic barriers verify the efficiency of the proposed algorithm.

参考文献/References:

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备注/Memo

备注/Memo:
国家自然科学基金(60975075);江苏省自然科学基金(BK2008404)
更新日期/Last Update: 2012-10-12