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.

翻译：本文研究集体机器人学领域，更具体地关注探索、路径规划与协调场景下多机器人系统的优化问题。本研究包含两项贡献。第一项贡献是采用蝴蝶优化算法（BOA）解决动态环境中具有能量约束的未知区域探索问题。据我们所知，该算法此前从未被用于解决机器人学问题。我们基于交叉算子提出了该算法的新版本xBOA，以提升候选解的多样性并加速算法收敛。第二项贡献是开发了一个新型仿真框架，用于评估机器人学中如探索任务等动态增量式问题的基准测试。该框架设计具有通用性，能够以最小修改量快速比较不同元启发式算法，并易于适配单机器人及多机器人场景。此外，该框架为研究人员提供了实验自动化与可视化生成工具，使其能聚焦于新算法建模等更重要的任务。我们开展了一系列实验，实验结果展现了良好成效，并验证了所提方法与模型的有效性。