Contribution à l'Optimisation d'un Comportement Collectif pour un Groupe de Robots Autonomes
This work addresses optimization challenges in multi-robot systems for tasks like exploration, though it appears incremental as it adapts existing algorithms and tools to robotics.
The thesis tackled the optimization of collective robot behavior for exploration and coordination by introducing a new version of the Butterfly Optimization Algorithm (xBOA) to improve solution diversity and convergence speed, and developing a generic simulation framework for benchmarking dynamic robotics problems, with experiments showing promising results.
This thesis studies the domain of collective robotics, and more particularly the optimization problems of multirobot systems in the context of exploration, path planning and coordination. It includes two contributions. The first one is the use of the Butterfly Optimization Algorithm (BOA) to solve the Unknown Area Exploration problem with energy constraints in dynamic environments. This algorithm was never used for solving robotics problems before, as far as we know. We proposed a new version of this algorithm called xBOA based on the crossover operator to improve the diversity of the candidate solutions and speed up the convergence of the algorithm. The second contribution is the development of a new simulation framework for benchmarking dynamic incremental problems in robotics such as exploration tasks. The framework is made in such a manner to be generic to quickly compare different metaheuristics with minimum modifications, and to adapt easily to single and multi-robot scenarios. Also, it provides researchers with tools to automate their experiments and generate visuals, which will allow them to focus on more important tasks such as modeling new algorithms. We conducted a series of experiments that showed promising results and allowed us to validate our approach and model.