Evolving control for distributed micro air vehicles
نویسندگان
چکیده
The general idea of distributed robotics (or multirobot systems) is that teams of robots, deployed to achieve a common goal, can outperform individual robots in terms of e ciency and quality and, in some cases, can perform tasks that a single robot cannot. Consider, for example, Micro Air Vehicles (MAVs), each of which has an extremely small payload capacity. Though individual MAVs may have limited capabilities, teams of MAVs, possibly carrying di erent payloads, can be deployed as a group to perform complex tasks. Groups of robots provide an added level of robustness, fault tolerance, and exibility over individuals, as the failure of one robot does not result in the failure of the mission, as long as the remaining robots can redistribute and share the tasks of the failed robot. Examples of tasks appropriate for robot teams are large area surveillance, environmental monitoring, large object transportation, planetary exploration, and hazardous waste cleanup. In this paper, we focus on the task of large area surveillance. Given an area to be surveilled and a team of MAVs with appropriate sensors, the task is to dynamically distribute the MAVs appropriately in the surveillance area for maximum coverage based on features present on the ground, and to adjust this distribution over time as changes in the team or on the ground occur. We have developed a system that will learn rule sets for controlling the individual MAVs in a distributed surveillance team. Since each rule set governs an individual MAV, control of the overall behavior of the entire team is distributed; there is no single entity controlling the actions of the entire team. Currently, all members of the MAV team utilize the same rule set; specialization of individual MAVs through the evolution of unique rule sets is a logical extension to this work.
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