Autonomous Path Planning of AUV in Large-Scale Complex Marine Environment Based on Swarm Hyper-Heuristic Algorithm

Autonomous underwater vehicles (AUVs) as an efficient underwater exploration means have been used to perform various marine missions. However, limited by the technologies of underwater acoustic communications and intelligent autonomy, the most current and advanced AUVs only perform a limited number of tasks in the small-scale area and the known underwater environment. Therefore, in this paper, a one path planning model was proposed combining the global path planning and the local path planning for the large-scale complex marine environment. More specifically, the B-spline curve was used to represent the smooth path for the requirement of kinematic constraints of AUVs. After considering the various constraints, such as the energy/time consumption, the turning radius limitation, the marine environment, and the ocean current, the path planning was abstractly modeled as a multi-objective optimization model with the time cost, the curvature cost, the map cost, and the ocean current cost. The swarm hyper-heuristic algorithm (SHH) with the online learning ability was proposed to solve this model with real-time performance and stability. The results showed that the proposed online learning SHH algorithm had obvious advantages in terms of time efficiency, stability, and optimal performance compared with the results of two traditional heuristic algorithms, both particle swarm optimization (PSO) and firefly algorithm (FFA). The time efficiency of the online learning SHH algorithm improved at least 20% compared with PSO and FFA.