New Bio-Inspired Coordination Strategies for Multi-Agent Systems Applied to Foraging Tasks

Multiple agent systems can be applied to foraging tasks, thus solving this problem in a cooperative approach. The major processes performed by a forager agent are searching and homing. A new coordination searching strategy inspired on Tabu Search is reported here by modifying a previous probabilistic cellular automata ant memory model. Moreover, new homing strategies based on ants behavior and cellular automata are investigated. Combining bio-inspired searching and homing strategies, we propose a new coordination model for foraging in robotics called Hybrid Cellular Automata Ant Queue model, or HCAAQ for short. The model is able to adapt the current system dynamics if either the number of robots or the environment structure change. Experimental simulations were conducted to evaluate different versions of memory policies, resulting in a new homing strategy based on ants communication by inverted pheromone. Besides, simulations confirm that the new homing strategy proposed herein distributes agents equitably between the nests, accelerating the task performance. As a result, using the new team coordination it is possible to avoid lines forming near the nests, specially when the robot number has increased, thus outperforming previous models. The proposed method was implemented in a robotics simulation environment called Webots to better investigate the application of the multi-agent system. Simulation results indicate that the HCAAQ proposed herein could be implemented in multi-robot systems.