Global-local hierarchical path planning scheme for unmanned surface vehicles under dynamically unforeseen environments

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Unmanned surface vehicles (USVs) should be allowed to respond to dynamic ocean environments and self-adjust their paths safely and efficiently. In this paper, considering the challenges posed by time-varying, partially unknown complex environments, a novel hierarchical motion planning framework is elaborately designed for a USV, which includes a global trajectory optimization and a local reactive collision avoidance strategy. By encapsulating the intricate nature of ocean environment, a global optimization path planning problem is developed to systematically strengthen the model's adaptability to the complex engineering problem. Incorporating adaptive elite selection and fuzzy probability set, an adaptive-elite GA with fuzzy inference (AEGAfi) is devised to fully exploit the underlying optimization problem, providing high-quality global paths. By applying virtual sensory vector onto the USV's sensing module, the COLREG-compliant local-reaction is achieved by governing feasible actions of USVs under dynamically unforeseen environments. Seamlessly bridged by the transition Clothoid path, the linkage between global optimization and dynamic-avoidance is strengthened by softening the replanning time restriction and maintaining path continuity. Eventually, the motion planning framework merits autonomous global-planning and local-reaction in an organically modular manner. Comprehensive simulations and comparisons in various ocean scenarios demonstrate the effectiveness and superiority of the proposed framework.