Research on path planning of deep-sea mining vehicles integrating improved theta∗ algorithm with dynamic window method

Abstract

Due to the complex and unpredictable environment of deep-sa mining areas, effective path planning algorithms can significantly enhance the operational efficiency of deep-sea mining vehicles (DSMVs). This paper proposes an improved Theta∗ algorithm, integrated with the dynamic window approach (DWA), specifically designed for path planning in deep-sea mining operations. The algorithm incorporates constraints on the vehicle's kinematic parameters, dynamically adjusts the neighborhood node expansion method and line-of-sight algorithm, and introduces acceleration and azimuth evaluation weights to improve search efficiency and ensure smooth vehicle motion. Furthermore, a path planning controller based on this algorithm is developed. To validate the effectiveness of the proposed method, a multi-body dynamics (MBD) model of the DSMV was constructed to perform co-simulation, taking into account factors such as water resistance and the interaction between the tracks and sediment. The simulation results and experimental tests demonstrate that the improved path planning algorithm satisfies the kinematic requirements for deep-sea mining operations and exhibits high precision in motion control.