Hierarchical Cooperative Mission Planning of Non-Homogeneous Autonomous Search-and-Rescue Robots

Abstract

The application of autonomous robots in search-and-rescue (SAR) missions forms a challenging field of research. Cooperative search behaviour can greatly increase the efficiency with which a multi-agent system creates situational awareness of and finds victims within an unknown environment. In this research we develop an autonomous mission planning approach that exploits non-homogeneous characteristics of the robots to increase the overall search performance. Furthermore, the proposed approaches incorporate a hierarchical, cooperative control architecture: At the lower level of control, every SAR agent is locally controlled by a heuristic approach that uses fuzzy-logic control (FLC) and A* search to guide its individual actions. At the higher level of control, a model-predictive-control-based (MPC-based) system coordinates the actions of all SAR agents when two or more agents intend on searching the same area at once. When simulated in a virtual SAR environment, we show that the area coverage performance of the cooperative search approach is comparable to a purely heuristic approach designed for area coverage, while simultaneously outperforming this and another optimisation-based approach in victim detection efficiency. This shows that the hierarchical combination of the heuristic local controller and the optimisation-based supervisory controller is able to outperform either one approach individually. Furthermore, it is demonstrated that the cooperative search approach is able to efficiently resolve particular SAR-related search conflicts where a non-cooperative structure fails.