Fuzzy logic based robot path planning in unknown environment

This paper proposes a new method, minimum risk approach, to address the local path planning to escape from local minimum during goal-oriented robot navigation in unknown environments. This approach is theoretically proved to guarantee global convergence even in the long-wall, unstructured, cluttered, maze-like, and modified environments. The approach adopts a strategy of multi-behavior coordination, in which a novel path-searching behavior is developed to recommend the regional direction with minimum risk. The paper provides a fuzzy logic framework to implement the behavior design and coordination. It is verified by the simulated and real world tests.