A novel foraging algorithm for swarm robotics based on virtual pheromones and neural network

Abstract Swarm robotics is an emerging interdisciplinary field that has many potential real-world applications. Swarm robotics aims to produce robust, scalable, and flexible self-organizing behaviors through local interactions from a large number of simple robots. In this paper, a novel pheromone model of swarm foraging behavior is developed based on a neural network. The output of a single neuron corresponds to the density of a pheromone, which diffuses to neighboring neurons through their local connections. A neural network is updated based on the proposed evaporation model. Neural networks can often mimic the dynamics and features of pheromones. Therefore, in this work, we develop an optimization method to determine the key parameters of cooperative foraging based on mathematical modelling. The differential equation variables represent the number of foraging robots assigned different tasks. The solutions of the differential equations represent the dynamics of the foraging behavior. The key parameters that affect task allocation are determined to make optimal decision rules. Simulation experiments are conducted under different foraging scenarios. The experimental results demonstrate the effectiveness of the proposed pheromone model.