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Active suspension rover is gaining more and more attention. Among them, the wheel-legged rover has outstanding performance. In order to fully utilize the rover’s capabilities and improve the autonomy in planetary exploration, this paper addresses a plane-based grid (PBG) mapping method for its motion planning in unstructured terrain environments. First, the kinematics of the TAWL rover and each leg’s dynamic are presented to analyze the physical characteristics. Second, the PBG map is constructed based on the rover’s capabilities. It can be directly used during motion planning. This mapping framework utilizes a sensor model to predict whether the grid is occupied or free. Then, a plane cluster algorithm is proposed to classify grid cells, which incorporates two machine learning algorithms. Thus, the flat areas are clustered from the unstructured terrains. With the PBG map and the motion planning framework, the wheel-legged rover can simplify the motion planning process and better utilize the driving efficiency and the legs’ adaptability. Third, the proposed mapping algorithm is tested on a depth camera. Robotic experiments are carried out, demonstrating our framework which allows the rover to safely combine the wheeled motion with legged motion in autonomous navigation.

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