Behaviour-based pattern formation in a swarm of anonymous robots

The ability to form patterns is useful to maximize the sensor coverage of a team of robots. Current pattern formation algorithms for multi-robot systems require the robots to be able to uniquely identify each other. This increases the sensory and computational requirements of the individual robots, and reduces the scalability, ro- bustness, and exibility of the pattern formation algorithm. The research presented in this thesis focuses on the development of a novel pattern formation algorithm called the Dynamic Neighbour Selection (DNS) algorithm. The DNS algorithm does not require robots to be uniquely identified to each other, thus improving the scal- ability, robustness, and exibility of the technique. The algorithm was developed in simulation, and demonstrated on a team of vision-enabled Bupimo robots. The Bupimo robots were developed as part of the research reported in this thesis. They are a low-cost, vision enabled, mobile robotic platform intended for use in swarm robotics research and education. Experiments conducted using the DNS algorithm were performed using a computer simulation and in real world trials. The exper- iments conducted via simulation compared the performance of the DNS algorithm to an other similar algorithm when forming a number of patterns. The results of these experiments demonstrate that the DNS algorithm was able to assume the de- sired formation while the robots traversed a shorter distance when compared to the alternative algorithm. The real robot trials had three outcomes. First, they demon- strated the functionality of the Bupimo robots, secondly they were used to develop an effective robot-robot collision avoidance technique, and lastly they demonstrated the performance of the DNS algorithm on real robots