F3DMP: Foresighted 3D Motion Planning of Mobile Robots in Wild Environments

In wild environments, motion planning for mobile robots faces the challenge of local optimal path traps due to limited sensor perception range and lack of spatial awareness. Existing approaches that avoid local optimum by designing heuristic functions or high-quality global paths in wild environ- ments are time-consuming and unstable. This work proposes F3DMP, which consists of two parts to alleviate the local optimum solution and better utilize distant terrain information. First, the entire planning framework is adapted to the three- dimensional space so that the planning result conforms to the geometric characteristics of the terrain. Second, a time allocation function based on offline reinforcement learning is proposed. This function can anticipate potential challenges or opportunities based on semantic information for the image and proactively determine a time allocation. Our planner is integrated into a complete mobile robot system and deployed to a real robot. Experiments in simulation and the real world demonstrate that our method can improve the success rate by 28% and the trajectory smoothness by 27% compared with traditional methods.