Abstract
Can a multi-robot system (MRS) be made to find and track a target that can move faster than any of its component robots? Such tasks have long been considered impossible due to the assumption that the target will always be able to outrun the individual robots. The work done in this thesis shows that this task is in fact, achievable and at its root, boils down to the exploration-exploitation dilemma—the choice a system must make between gathering more information about the environment or making use of the information currently available. To accomplish the task of tracking a fast-moving non-evasive target, a fully decentralised search and track strategy based on the Particle Swarm Optimisation (PSO) algorithm is used. This strategy is complemented by an adaptive inter-agent repulsion behaviour, used to promote exploration, as well as an adjustable k-nearest neighbour communications network, used to tune the system’s exploration-exploitation balance. To achieve the more challenging task of tracking an evasive target, the individual agents of the swarming MRS are endowed with a short-term memory, thereby promoting higher levels of exploitation. The two developed strategies are then validated through physical tests using a decentralised swarm of miniature ground robots. Through both virtual and physical experimentation, an optimum level of connectivity to maximise the MRS’s tracking performance is revealed. The origin of this optimum level of connectivity is further traced back to an optimum balance in the amount of exploratory and exploitative actions carried out by the system. The effect of various environmental factors and mission parameters on this optimum, such as the swarm density, number of agents used, and the movement profile of the targets, are also studied. The results presented in this thesis further emphasises the importance of attaining the correct exploration-exploitation balance when developing a swarm strategy. This optimum changes according to the task set for the system and the results presented shed some light on how to tune a swarm’s exploration-exploitation dynamics to find this optimum, potentially paving the way for better swarming algorithms to be developed.