Higher individuality for effective swarm intelligence

Abstract

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots [1]. With the simplicity in individual robot design and flexibility in the dimension of their collectivity, swarm robot systems have received considerable attention in recent research. However, lots of previous literature has focused on collective behaviors of swarm robot systems to achieve higher capability in both communication and coordination, whereas, individuality of a specific swarm robot has been seldom addressed. Traditionally, because the design of an individual swarm robot is rather simple, using many robots is the only way to tackle complicated tasks. Considering expanding demands of robotic tasks with higher complexity, higher dimensionality, and different information density, other than employing the conventional low cost-effective approach of increasing the number of swarm, this project intends to adopt higher swarm individuality to obtain higher flexibility and re-configurability of the system. The proposed swarm design has been tested with a self-developed multi-target following system, the Auto-Cart (a new supermarket service system). In order to optimize the system performance using a simple individual robot design and a minimum number of swarm, a new navigation and communication algorithm was proposed. Simulation and experiment results verified the effectiveness and efficiency of the proposed system and algorithm.