Multi-Robot Collaborative Reasoning for Unique Person Recognition in Complex Environments

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.