Inverse reinforcement learning-based time-dependent A* planner for human-aware robot navigation with local vision

被引:15
|
作者
Sun Shiying [1 ]
Zhao Xiaoguang [1 ]
Li Qianzhong [1 ]
Tan Min [1 ]
机构
[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Human-aware navigation; inverse reinforcement learning; path planning; service robot;
D O I
10.1080/01691864.2020.1753569
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
In an environment where robots coexist with humans, mobile robots should be human-aware and comply with humans' behavioural norms so as to not disturb humans' personal space and activities. In this work, we propose an inverse reinforcement learning-based time-dependent A* planner for human-aware robot navigation with local vision. In this method, the planning process of time-dependent A* is regarded as a Markov decision process and the cost function of the time-dependent A* is learned using the inverse reinforcement learning via capturing humans' demonstration trajectories. With this method, a robot can plan a path that complies with humans' behaviour patterns and the robot's kinematics. When constructing feature vectors of the cost function, considering the local vision characteristics, we propose a visual coverage feature for enabling robots to learn from how humans move in a limited visual field. The effectiveness of the proposed method has been validated by experiments in real-world scenarios: using this approach robots can effectively mimic human motion patterns when avoiding pedestrians; furthermore, in a limited visual field, robots can learn to choose a path that enables them to have the larger visual coverage which shows a better navigation performance.
引用
收藏
页码:888 / 901
页数:14
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