Multirobot Cooperative Learning for Semiautonomous Control in Urban Search and Rescue Applications

Abstract

The use of cooperative multirobot teams in urban search and rescue (USAR) environments is a challenging yet promising research area. For multirobot teams working in USAR missions, the objective is to have the rescue robots work effectively together to coordinate task allocation and task execution between different team members in order to minimize the overall exploration time needed to search disaster scenes and to find as many victims as possible. This paper presents the development of a multirobot cooperative learning approach for a hierarchical reinforcement learning (HRL) based semiautonomous control architecture in order to enable a robot team to learn cooperatively to explore and identify victims in cluttered USAR scenes. The proposed cooperative learning approach allows effective task allocation among the multirobot team and efficient execution of the allocated tasks in order to improve the overall team performance. Human intervention is requested by the robots when it is determined that they cannot effectively execute an allocated task autonomously. Thus, the robot team is able to make cooperative decisions regarding task allocation between different team members (robots and human operators) and to share experiences on execution of the allocated tasks. Extensive results verify the effectiveness of the proposed HRL‐based methodology for multi‐robot cooperative exploration and victim identification in USAR‐like scenes.

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

“…Ground robots can be used instead of human rescuers for search and rescue purposes [3][4][5]. However, they are expensive and need invasive equipment and need human operators for moving around the disaster zone.…”

A survey of formation control and motion planning of multiple unmanned vehicles

“…Then, as the technology became more mature, the concept, developed from the multirobot systems, paved the way to the utilisation of multiple unmanned vehicle platforms in real-world applications. One of the crucial applications is the rescue missions carried out by UGV formations in disaster areas to minimise exploration time and reduce the risk of further casualties [22][23][24] . Similarly, a number of efforts have been put into the deployments including the area mapping 25;26 and border patrol and surveillance 27 .…”

Ensemble Methods for Cooperative Robotic Learning

“…Finally, Liu and Nejat present a multirobot cooperative learning approach for a hierarchical reinforcement learning based semiautonomous control architecture. They use it to enable robots to learn cooperatively to explore and identify victims in urban search and rescue applications.…”