Probabilistic Fusion–Based Motion Planning for a Hexapod Rover on Unstructured Terrain in Microgravity

In response to the challenges posed by the microgravity environment and unstructured terrain on small celestial bodies, this paper proposes a novel probabilistic fusion–based motion planning (PFMP) framework for the locomotion of a self-developed Hexapod Rover. The PFMP method first acquires terrain information through a nonmechanical feedback mechanism termed “probabilistic fusion prediction” (PFP). This approach utilizes a preestablished foot–ground contact probability model as input and uses Kalman filtering to predict contact states, thereby estimating the local terrain slope. Subsequently, based on the prediction results, PFMP incorporates low reaction motion planning (LRMP), which determines optimal foothold positions and optimizes the foot trajectory by minimizing motion reactions while adhering to foot–ground friction constraints. This ensures stable adhesion of the robot to complex and irregular surfaces. The effectiveness of the proposed PFMP framework is validated through simulation experiments. The study contributes to advancing motion planning strategies for multilegged robots operating in microgravity and highly unstructured extraterrestrial terrain, supporting future exploration missions on small celestial bodies.