Communication Optimization for Efficient Dynamic Task Allocation in Swarm Robotics

Abstract

The interest in solving high complexity problems has been growing in recent years, intensifying the use of swarm robotics. Cooperation is a central idea to the usage of swarm robotics because it enables the solution of complex problems with a coordinated execution of basic tasks, which together lead to the achievement of the swarm common goal. This coordination is only possible with an efficient task allocation. Inspired by the strategy of the particle swarm optimization algorithm, we propose a novel algorithm called the Clustered Dynamic Task Allocation (CDTA). This algorithm performs task allocation to swarm robots in a fully distributed manner. It performs a guided search of the solution spaces using the concept of adaptive speed. However, this process requires an intense exchange of information between robots, which hinders the efficiency of the task allocation process for large swarms. This paper proposes the use of a clustered communication topology between the swarm robots, aiming to optimize the underlying communication processes, and thus enabling efficient task allocation for large robotic swarms. The results obtained with the cluster-based topology are compared to those obtained with the full mesh-based topology.