Swarm robotics

Abstract

The field of swarm robotics has been growing fast over the last few years. Using a large swarm of simple and cheap robots has advantages in various tasks. Apart from performance gains on tasks that allow for parallel execution, simple robots can also be smaller, enabling them to reach areas that cannot be accessed by a larger, more complex robot. Their ability to cooperate means they can execute complex tasks while offering self-organised adaptation to changing environments and robustness due to redundancy. Hence, swarms have the potential to be useful for types of tasks for which traditional robots are not well suited, such as large area surveillance, distributed contaminant clean-up, and in–vitro medical applications. The scope of this dissertation is to study, design and implement swarming algorithms on a team of four Khepera III mobile robots. The robots will aim to cooperate with the other robots in order to carry out complex tasks such as moving into a suitable formation to transport a relatively large object or search a large space. Moreover, an obstacle avoidance technique was required since the robots would be navigating in an unknown environment and hence, the robots would need to rely on their limited onboard sensing capabilities to navigate safety across the terrain. This dissertation starts by reviewing different approaches to implement swarm behaviour on mobile robots as well the underlying infrastructure which is important for the sound operation of the swarm. A swarming algorithm is then explained, simulated and tested on the physical robots. The robustness of the algorithms is tested by giving different initial positions to the robots. These tests produced very promising results, since in all cases the swarm managed to carry out the task efficiently. Hence, this study concludes that the proposed algorithms are a suitable solution to implementing swarming behaviour in multi-robot systems.