Net Energy Income Optimization in a Homogeneous Swarm Robotics Foraging System

This paper presents an optimization technique inspired from the collective foraging observed in natural insects, swarm robotics is a new approach to coordinate the behaviors of large number of relatively simple robots in decentralized manner. In such robotic systems, an individual robot have only local perception and very limited capabilities in terms of sensing, computation, and communication can adapt its own behavior so that a desired collective behavior emerges from the local interactions among robots and between robots and the environment. Swarm robotics has been the focus of increased attention recently because of the beneficial features demonstrated in such systems, such as higher swarm efficiency, robustness against the failures of individual robots, flexibility to adapt to changes in the environment, and scalability over a wide range of swarm sizes. In this paper we present an optimization technique to regulate the net energy income of an individual robot performing collective foraging tasks. Through the interactions between robots, a desired division of labour can be achieved at the swarm level. Robot swarm also demonstrates the ability to optimize energy efficiency and its potential robustness in different environments.