Probabilistic search optimization and mission assignment for heterogeneous autonomous agents

This paper presents an algorithmic framework for conducting search and identification missions using multiple heterogeneous agents. Dynamic objects of type ldquoneutralrdquo or ldquotargetrdquo move through a discretized environment. Probabilistic representation of the current level of situational awareness - knowledge or belief of object locations and identities - is updated with imperfect observations. Optimization of search is formulated as a mixed-integer program to maximize the expected number of targets found and solved efficiently in a receding horizon approach. The search effort is conducted in tandem with object identification and target interception tasks, and a method for assignment of these missions among agents is developed. The proposed framework is demonstrated in simulation studies, and an implementation of its decision support capabilities in a recent field experiment is reported.