Toward Real-Time Planning for Robotic Search

This work addresses applications of search theory where a mobile search agent seeks to find an unknown number of stationary targets randomly distributed in a bounded search domain. We assume that the search mission is subject to a time or distance constraint, and that the local environmental conditions affect sensor performance. Because the environment varies by location, the effectiveness of the search sensor also varies by location. Our contribution to search theory includes new decision-theoretic approaches for generating optimal search plans in the presence of false alarms and uncertain environmental variability. We also formally define the value of environmental information for improving the effectiveness of a search mission, and we develop methods for optimal deployment of assets that can acquire environmental information in order to improve search effectiveness. Finally, we extend our research to the case of multiple cooperating search agents. For the case that inter-agent communication is severely bandwidth-limited, such as in subsea applications, we propose a method for assessing the expected value of inter-agent communication relative to joint search effectiveness. Our results lead to a method for determining when search agents should communicate. Our contributions to search theory address important applications that range from subsea mine-hunting to post-disaster search and rescue applications.