Modeling robot swarms using agent-based simulation

Abstract

In the near future advances in mechanical and electrical engineering will enable the production of a wide variety of relatively low cost robotic vehicles. This thesis investigates the behavior of swarms of military robots acting autonomously. The Multi-Agent Robot Swarm Simulation (MARSS) was developed for modeling the behavior of swarms of military robots. MARSS contains state, sensing, and behavioral model building tools that allow a range of complex entities and interactions to be represented. It is a model-building tool that draws theory and ideas from agent-based simulation, discrete event simulation, traditional operations research, search theory, swarm theory, and experimental design. MARSS enables analysts to explore the effect of individual behavioral factors on swarm performance. The performance response surface can be explored using designed experiments. A model was developed in MARSS to investigate the effects of increasing behavioral complexity for a search scenario involving a swarm of Micro Air Vehicles (MAV's) searching for mobile tanks in a region. Agreement between theoretical and simulated search scenarios for simple searchers was found. The effect of increased MAV sensory and behavioral capability was demonstrated to be important. Little improvement was observed in swarm performance with these capabilities, however agent performance was adversely affected by reacting to increased knowledge in the wrong way. The utility of MARSS for conducting this type of analysis was demonstrated.