Adaptive bi-level path optimization for deep-sea mining vehicle in non-uniform grids considering ocean currents and dynamic obstacles

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The navigation safety of Deep-sea Mining Vehicle (DSMV) is significantly threatened by unstructured seabed terrain and dynamic obstacles. To address this challenge, this paper proposes an adaptive bi-level path optimization (ABLPO) method that adaptively adjusts cost function weights for optimal global path planning and effective local obstacle avoidance in dynamic environments. Initially, a non-uniform grid-based traversability map is established using sonar data integrated with a seabed current model. Dense grids in high-risk areas enhance safety through detailed calculations, while sparse grids in low-risk areas improve computational efficiency. Based on this non-uniform grid map, the ABLPO method is implemented for bi-level path planning: the upper level utilizes the adaptive-weight A∗ (AWA∗) algorithm for global path planning noted for its computational efficiency, while the lower level employs the adaptive dynamic window approach (ADWA) for local obstacle avoidance, providing efficient navigation in dynamic environments. Throughout the planning process, ABLPO adaptively adjusts the weights for path distance, traversability, and energy consumption to determine the optimal path. Simulation results demonstrate that ABLPO, with its adaptive weight adjustment, notably outperforms traditional methods in terms of convergence time, path distance, energy consumption, and traversability. The experiments further confirm the feasibility of the ABLPO method.