Adaptive Path Planning for Amphibious Vehicles Based on Enhanced Hybrid A-Star Algorithm

Amphibious vehicles face complex path planning challenges in dynamic aquatic environments due to water currents and slow steering responses. Traditional algorithms, including Hybrid $A^{*}$, often produce inefficient paths under these conditions. This paper introduces an Enhanced Hybrid A* algorithm tailored for amphibious vehicles, integrating fluid dynamics- based heuristics and an obstacle influence radius that accounts for vehicle speed, dimensions, and water flow characteristics. An Artificial Potential Field (APF) incorporates attractive and repulsive forces influenced by water flow direction and velocity. Path smoothness is improved using the Ramer-Douglas-Peucker (RDP) algorithm for simplification and Chordal Catmull-Rom splines to reduce lateral slipping. Simulations under no flow, 0.4 kn, and 1.0 kn east-to-west water flows demonstrate that the Enhanced Hybrid $A^{*}$ significantly enhances obstacle avoidance and path safety, increasing the average minimum distance to obstacles from 8.34 meters to 46.83 meters, with only a slight increase in computational time. These results validate the algorithm’s effectiveness in navigating complex aquatic environments, providing a robust solution for autonomous amphibious vehicle navigation.