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This paper introduces a decentralized multi-robot system (MRS) that employs a genetic fuzzy system to optimize collaborative object transportation in unstructured environments while minimizing path length and avoiding obstacles. By conducting terrain traversability analysis based on an elevation map, the authors effectively identify non-traversable areas and create a two-dimensional traversability map. The system's performance is validated through various scenarios, demonstrating its robustness in navigating complex environments and efficiently transporting objects.
Genetic fuzzy systems can significantly enhance multi-robot coordination, achieving efficient object transport in challenging terrains while minimizing path lengths.
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.
