Abstract
Human exploration and rescue in unstructured environments including hill terrain and depression terrain are fraught with danger and difficulty, making autonomous vehicles a promising alternative in these areas. In flat terrain, traditional wheeled vehicles demonstrate excellent maneuverability; however, their passability is limited in unstructured terrains due to the constraints of the chassis and drivetrain. Considering the high passability and exploration efficiency, wheel-leg vehicles have garnered increasing attention in recent years. In the automation process of wheel-leg vehicles, planning and mode decisions are crucial components. However, current path planning and mode decision algorithms are mostly designed for wheeled vehicles and cannot determine when to adopt which mode, thus limiting the full exploitation of the multimodal advantages of wheel-leg vehicles. To address this issue, this paper proposes an integrated path planning and mode decision algorithm (IPP-MD) for wheel-leg vehicles in unstructured environments, modeling the mode decision problem using a Markov Decision Process (MDP). The state space, action space, and reward function are innovatively designed to dynamically determine the most suitable mode of progression, fully utilizing the potential of wheel-leg vehicles in autonomous movement. The simulation results show that the proposed method demonstrates significant advantages in terms of fewer mode-switching occurrences compared to existing methods.