Paper Info
Title
Multi-Robot Collaborative Reasoning for Unique Person Recognition in Complex Environments
Abstract
The discovery of unique or suspicious people is essential for active surveillance of security or patrol robots, and multi-robot collaboration and dynamic reasoning can further enhance their adaptability in large-scale environments. This paper proposes a hierarchical probabilistic reasoning framework for a multi-robot system to actively identify the unique person with distinct motion patterns in large-scale and dynamic environments. Linear and angular velocities are considered typical motion patterns, which are extracted by using heterogeneous sensors to detect and track people. First, single robot reasoning is performed, each robot judges the uniqueness of people by comparing their motion patterns based on local observations. Meanwhile, multi-robot reasoning is also performed, by fusing the perceptual information from each individual robot to form a global observation and then make another judgment based on it. Finally, each robot can decide which result should be adopted by comparing the beliefs of local and global judgments. Experimental results show that the method is feasible in various environments.
Year
DOI
Venue
2020
10.1109/ICARCV50220.2020.9305425
2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Keywords
DocType
ISSN
multirobot collaborative reasoning,unique person recognition,complex environments,unique people,suspicious people,active surveillance,security,multirobot collaboration,dynamic reasoning,large-scale environments,hierarchical probabilistic reasoning framework,multirobot system,distinct motion patterns,dynamic environments,typical motion patterns,single robot reasoning,robot judges,multirobot reasoning,individual robot
Conference
2474-2953
ISBN
Citations 
PageRank 
978-1-7281-7710-6
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Chule Yang101.69
Yufeng Yue285.73
Mingxing Wen325.44
Yuanzhe Wang4107.65
Baosong Deng500.68