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.

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.

Abstract: The present invention provides a swarm of robots and a related method of operating the swarm. The robots are programmed to start at a nest and to select a dispersal direction from a uniform random distribution. The robots travel along the dispersal direction until transitioning to a search mode upon reaching a search site, where the robot performs a correlated random walk with fixed step size and direction and using a standard deviation to determine how correlated the direction of the next step of the robot is with the direction of the previous step. If no resource is found within predetermined time t independently determined by each of said robots, the robot returns to the nest and repeats the above steps.