A probabilistic cellular automata ant memory model for a swarm of foraging robots

Abstract

Foraging is one of the most popular tasks for multi-robot systems and it can be considered a metaphor for a broad class of problems. The complexity of the problem increases proportionally with the number of agents, due to the fact that all robots must act in cooperation to complete the task. The proposed model employs a combination of different natural swarm behavior techniques to control a team of robots. It was named robot probabilistic cellular automata ant memory (RPCAAM). Our inspiration came from the possibility to mimic the cognitive behaviors of foraging ants with those of pedestrians in a building evacuation. The proposed probabilistic homing can avoid inertial behavior near the nests, which improves team performance. Furthermore, some investigations into robot-robot and robot-obstacles conflicts were improved to make the model more adequate to real-world applications. The probabilistic model was contrasted with deterministic homing. Moreover, the proposed method was implemented in a robotics simulation environment called Webots. Simulation results indicate that foraging tasks could be implemented with low complexity in low-cost architectures.