To address the challenges of full coverage path planning for deep-sea mining vehicles operating in complex benthic environments with variable terrain, this article proposes an iterative obstacle avoidance algorithm constrained by strict kinematics and safety margins. Moving beyond traditional planar assumptions, the operational space is modeled as a 2.5D environment where three-dimensional topographical features are systematically mapped into two-dimensional kinematic constraint costs. The algorithm employs an online perception and decision-making state machine based on the Boustrophedon strategy, utilizing a virtual boundary mechanism and analytical circular arc smoothing to resolve turning deadlocks in constrained spaces without resorting to computationally expensive global searches. Furthermore, to counteract chassis slip and gravity-induced drift inherent in traversing uneven seabeds, a closed-loop tracking system integrating a Terrain-Adaptive Extended Kalman Filter and Variable-weight Model Predictive Control is implemented. Extensive simulations on a realistic 1000m × 1000m seabed map demonstrate the superiority of the proposed method. Compared to advanced search-based baselines including Hybrid A* and Chaos A*, the proposed algorithm reduces the computational time by more than 42%, achieving a planning time of 24.09 s. While yielding a marginal reduction in the raw coverage ratio at 68.28%, statistical distribution analysis proves that the proposed method completely eliminates path generation within the extreme hazard zone, guaranteeing zero exposure to high-risk proximities. The results validate that the algorithm successfully balances highly efficient real-time computation with rigorous operational safety for deep-sea heavy equipment.
Wei et al. (Sun,) studied this question.