System and methods for multiple-place swarm foraging with dynamic depots

Abstract

Teams of robots can be organized to collectively complete complex real-world tasks, for example collective foraging in which robots search for, pick up, and drop off targets in a collection zone. A dynamic multiple-place foraging algorithm (MPFAdynamic) is a scalable, flexible, and efficient algorithm for robot swarms to collect objects in unmapped environments. It achieves scalability through a decentralized architecture in which robots search without central control, and then return to mobile depots which provide collection and communication points. Mobile depots move closer to clusters of targets as robots discover them, which reduces robot transport time as well as collisions among robots. Flexibility is achieved by incorporating individual robot behaviors in which robots move and communicate in ways that mimic the foraging behaviors of ants. The MPFAdynamic algorithm demonstrates that dispersed agents that dynamically adapt to local information in their environment provide more flexible and scalable swarms.

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