Robotic Harvesting of a Moving Swarm Represented by a Markov Process

This paper investigates motion planning for one or more robot(s) that attempt to harvest agents from a moving swarm. Generating motion paths that maximize the number of agents harvested differs from many traditional coverage problems because the agents move. This movement allows previously cleared areas to become recontaminated. We assume that the swarm agents prefer certain regions over others, and that we can represent the swarm by a Markov Process that encodes the agents’ preferred regions and their speed of motion. We exploit this model to design and simulate robotic coverage paths that maximize the number of agents harvested by a fleet of robots in a given time budget.