Sampling-based search for a semi-cooperative target

被引:1
|
作者
Vandermeulen, Isaac [1 ]
Gross, Roderich [2 ]
Kolling, Andreas [3 ]
机构
[1] iRobot, Pasadena, CA 91101 USA
[2] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield, S Yorkshire, England
[3] Amazon Robot, North Reading, MA USA
来源
2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) | 2020年
关键词
MOVING TARGET; CONNECTIVITY; TIME; EXPLORATION;
D O I
10.1109/IROS45743.2020.9340839
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Searching for a lost teammate is an important task for multirobot systems. We present a variant of rapidly-expanding random trees (RRT) for generating search paths based on a probabilistic belief of the target teammate's position. The belief is updated using a hidden Markov model built from knowledge of the target's planned or historic behavior. For any candidate search path, this belief is used to compute a discounted reward which is a weighted sum of the connection probability at each time step. The RRT search algorithm uses randomly sampled locations to generate candidate vertices and adds candidate vertices to a planning tree based on bounds on the discounted reward. Candidate vertices are along the shortest path from an existing vertex to the sampled location, biasing the search based on the topology of the environment. This method produces high quality search paths which are not constrained to a grid and can be computed fast enough to be used in real time. Compared with two other strategies, it found the target significantly faster in the most difficult 60% of situations and was similar in the easier 40% of situations.
引用
收藏
页码:4774 / 4781
页数:8
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