Methodologies for decentralized control of networked autonomous vehicles

This paper presents two autonomous methods for cooperatively controlling a number of distributed mobile platforms in order to accurately and efficiently achieve a desired common mission objective. The first method presented is based on an information-theoretic approach utilizing a decentralized data fusion (DDF) core with information measures. This technique achieves coordinated control of distributed mobile platforms by maximizing joint information gains relative to various information metrics of interest. The second method presented is based on a distributed locational optimization approach employing Voronoi tessellations for optimal placement of resources relative to a given area of interest. This technique, referred to as Simultaneous Coverage and Tracking (SCAT), provides a framework which allows the full coupling of environment coverage, target tracking and task assignment. In this work, these state-of-the-art decentralized control methodologies are uniquely combined in order to couple their individual strengths and provide a complementary capability. A heterogeneous application example consisting of ground-based Intelligence, Surveillance and Reconnaissance (ISR) with air-based Suppression of Enemy Air Defenses (SEAD) establishes a proof of concept while illustrating the complementary strengths of the two presented autonomous architectures.