Adaptive obstacle avoidance algorithm for wave gliders in dynamic marine environments based on improved DAPF with multi-model prediction

Current obstacle avoidance algorithms for wave gliders (WGs) often neglect inherent steering constraints and employ fixed-parameter artificial potential field (APF), which limits adaptability. Additionally, existing algorithms typically assume that the speeds of obstacles are comparable to that of the WG, which is inconsistent with the real marine environment. To address these limitations, this paper proposes a fusion obstacle avoidance algorithm combining an improved dynamic prediction (IDP) collision model with a dynamic APF (DAPF), specifically designed for scenarios involving a single dynamic obstacle (DO). A multi-model hybrid prediction approach based on interactive multiple model (IMM) is used by the IDP for DO prediction, enabling robust adaptation to DO motion states. The DAPF introduces a speed-adaptive repulsion gain coefficient and yaw attraction field constraints through a dynamic elliptical repulsion field mechanism. Compared with improved APF and environmental improved APF (EAPF), simulation results show that IDP-DAPF can increase the minimum obstacle avoidance distance for high-speed obstacles by 36.2 % and reduce the navigation efficiency index by 40.01 %. Sea trials further validate the effectiveness of the proposed algorithm in real marine environments.