发表机构
University of Cincinnati（辛辛那提大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于遗传模糊系统的分布式多机器人协作方法，经遗传算法优化模糊推理系统，在非结构化环境中完成协作物体运输任务并通过多场景验证。
AI 中文摘要
本文提出一种多机器人系统（MRS）的分布式方法，采用遗传模糊系统执行协作物体运输任务，在非结构化环境中最小化MRS总路径长度并避障。对于给定高程图的环境，开展关于坡度的地形可通行性分析，以降低维度并识别可视为障碍物的不可通行区域，将给定地图转换为二维空间的可通行性地图。训练过程中，针对运输物体至目标位置的MRS速度生成所提出的模糊推理系统（FIS），采用遗传算法结合局部极小值、目标靠近障碍物、杂乱环境等多种场景进行优化。将训练后的FIS模型应用于转换后的可通行性地图测试环境，通过多种场景验证其有效性。
英文摘要
This paper proposes a decentralized approach for a multi-robot system (MRS) using a genetic fuzzy system to perform a collaborative object transportation task that minimizes the total path length of the MRS in unstructured environment while avoiding obstacles. For an environment given by an elevation map, terrain traversability analysis with respect to the slope is performed to reduce the dimension and identify non-traversable areas that can be considered as obstacles, and the given map is converted into a traversability map in two dimensional space. In the training process, proposed fuzzy inference systems (FISs) to generate the MRS's velocity for transporting an object to a target position are optimized by a genetic algorithm with several scenarios, such as a local minima, a target that is close to an obstacle, and a cluttered environment. The trained FIS models are applied to the testing environment, which is the converted traversability map, and validated using multiple scenarios.
Comments14 pages, 14 figures, 2021 31st AAS/AIAA Space Flight Mechanics Meeting
