ROBUST Path Strategy Evaluator

A swarm of robots deployed in dynamic, hostile environments may encounter situations that can prevent them from achieving optimality or completing certain tasks. To resolve these situations, the robots must have an adaptive software system that can proactively cope with changes. This adaptive system should emulate the intelligence of human reasoning and common sense but must not assume that the robots can communicate, be tightly coupled, or be constantly at a close range. This paper presents a path strategy evaluator (PSE) that learns an optimal path by considering not just the distance, but also how to minimize damages to each robot and enhance the likelihood that the swarm will succeed in its mission, all with minimal impositions on the functionality of the robots. Our evaluation shows that this PSE is able to learn a dynamic environment and its effect on the robots' critical components and output an optimal path for the robots.