Distributed area partitioning for multi-robot coverage

Hocaoğlu, Burak
This thesis focuses on the distributed coverage area partitioning problem in a robotic swarm deployed over a known, two-dimensional environment. The goal is to split a region of interest into a number of subregions such that the amount of effort required to cover each subregion is approximately equal with respect to an objective function. Towards this end, the problem is defined as a distributed optimization problem, where the decision varibles are the locations of robots. Then, the proposed solution approach is described as a control mechanism driving each robot to better positions. At the end, robot positions will converge to a configuration where the total effort to process their subregion is minimal. Specifically, first, Lloyds Algorithm is deployed on the base approach to workload partitioning by converging to a Centroidal Voronoi Tesselation (CVT). Lloyds Algorithm provides a basic idea for convex problems where the deployment region has convex boundaries, but fails to provide reliability when used in a non-convex region. At this point, heuristics are added to reinforce the capabilities of Lloyds Algorithm to capture the dynamics missed when using CVT and to enable stable convergence in non-convex problems, converting the behaviour to converging to a Geodesic Voronoi Tesselation (GVT). Also, the model as the objective function and the control mechanism is revised for equitable workload partititoning. After that, various analyses are conducted with different centrality metrics whose results give the actual target locations that agents need to arrive, while heuristics refine how each agent arrives. These centrality metrics add new dynamics to how local workloads of each agent is defined, as well as optimizing them. This work provides qualitative and quantitative results of the proposed approaches. The experiments are done in non-convex two-dimensional environments where Lloyds Algorithm fails in its base form.


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Citation Formats
B. Hocaoğlu, “Distributed area partitioning for multi-robot coverage,” M.S. - Master of Science, Middle East Technical University, 2022.