نوع مقاله : مقاله پژوهشی (کاربردی)
عنوان مقاله English
نویسندگان English
This research focuses on developing a dynamic method for role assignment and formation control in multi-robot systems, aiming to enhance rotation and transition in fixed translational formations. In the proposed method, instead of separating the stages of task allocation and formation control, formation is considered as an integral part of the task allocation algorithm. This approach is implemented using Q-learning reinforcement learning, and multi-criteria decision-making (MCDM) theory. Robot behaviors dynamically determine their roles while simultaneously selecting their positions within the formation. To evaluate the algorithm's performance, simulations were conducted in various environments with grid sizes of 5×5, 8×8, and 20×20. The results demonstrate that the proposed method converges effectively and outperforms comparable methods. Furthermore, by employing a genetic algorithm, the key parameters of the MCDM method were optimized to enhance system efficiency. This research indicates that integrating reinforcement learning with multi-criteria decision-making, particularly in dynamic and uncertain environments, can offer an effective solution for formation control in multi-robot systems.
کلیدواژهها English
