Probabilistic search optimization and mission assignment for heterogeneous autonomous agents

• Published in: 2009 IEEE International Conference on Robotics and Automation pp. 939 - 945
• Main Authors: Chung, T.H., Kress, M., Royset, J.O.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2009
• Subjects: Autonomous agents; Feedback; Inspection; Object detection; Operations research; Optimization methods; Robotics and automation; Target tracking; USA Councils; Vehicle dynamics
• ISBN: 1424427886; 9781424427888
• ISSN: 1050-4729
• DOI: 10.1109/ROBOT.2009.5152215

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.