用遗传模糊系统让多机器人在复杂地形中协作运物，路径更短且避障高效。
Genetic Fuzzy System-Based Multi-Robot Coordination for Planetary Missions
- 基于遗传算法优化模糊推理系统，实现分布式协同决策。
- 在复杂地形中路径长度减少37%，成功避开障碍物与不可通行区域。
- 适合火星等行星探测中的多机器人自主协作任务。
本文提出一种去中心化的多机器人系统（MRS）协同方法，采用遗传模糊系统在非结构化环境中执行物体运输任务，以最小化整体路径长度并避免障碍。基于高程图进行地形可穿越性分析，根据坡度识别不可通行区域，将原始地图转换为二维可穿越性地图。训练阶段，通过遗传算法优化模糊推理系统（FIS），针对局部极小值、靠近障碍的目标点及拥挤环境等多种场景进行调优。训练后的FIS模型在转换后的可穿越性地图上进行测试，并在多个场景下验证其有效性。
原文摘要 · Abstract (English)
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.
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