Distributed Task Allocation in Swarms of Robots

This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm’s performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA’s control parameters by means of a genetic algorithm. INTRODUCTION The initial purpose of swarm intelligence algorithms was to solve optimization problems. However, in recent years, these algorithms have shown their full potential in terms of flexibility and autonomy when it comes to design and control of complex systems that consist of a large number of autonomous agents. In more general terms, these can be referred to as systems of autonomous systems [1]. What distinguishes swarm intelligence algorithms in the broad field of soft-computing is that they exploit the decentralizing property of natural swarms in order to create autonomous, scalable, and adaptive multi-agent systems. Swarm robotics emerged as a straight-forward application domain for swarm intelligence due to resemblance of large robot teams to animal swarms [2-4]. In nature, swarming behavior has been studied in ant and bee colonies, bird flocks and fish schools, among others [5]. However, the biological plausibility of swarm intelligence algorithms, and swarm-based systems in general, is not a must; in computer science and engineering researchers are guided by efficiency, flexibility, robustness and cost as main criteria. In applications that require area coverage, swarms of mobile robots can use their ability to quickly deploy within a large area. Some of the possible applications include planetary exploration, urban search and