Personalized Off-Road Path Planning Based on Internal and External Characteristics for Obstacle Avoidance

Abstract

Off-road environments with varied terrain and obstacle types present substantial challenges to the safe maneuvering of unmanned ground vehicles (UGVs). This study addresses the need for personalized path planning by introducing a multi-source off-road potential field (MOPF) method that quantifies risk and impediments in off-road settings based on internal and external characteristics. Specifically, Vehicle capability boundaries are defined by longitudinal dynamics analysis of the ego-vehicle to prevent instability due to insufficient driving force and limited adhesion conditions. A novel Non-Uniform Safety Margin Expression (NSME) is proposed to adjust the MOPF, allowing it to consider the vehicle’s state to enhance travel efficiency and minimize detours. The MOPF can be adapted according to the characteristics of the ego vehicle, drivers, and cargo. To incorporate driving styles, the Driving Style Probabilistic Roadmap (DSPRM) algorithm is developed, leading to smoother and more personalized paths. Comparative tests demonstrate that our method enables personalized path planning, achieving an average reduction of 10.29% in path length and 30.83% in path slope compared to traditional planning methods, while maintaining a safe distance from obstacles.