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Robust sampling-based motion planning for autonomous tracked vehicles in deformable high slip terrain

This paper presents an optimal global planner for autonomous tracked vehicles navigating in off-road terrain with uncertain slip, which affects the vehicle as a process noise. This paper incorporates two fields of study: slip estimation and motion planning. For slip estimation, an experimental result from [9] is used to model the effect of the slip on the vehicle in various soil types. For motion planning, a robust incremental sampling based motion planning algorithm (CC-RRT*) is combined with the LQG-MP algorithm. CC-RRT* yields the optimal and probabilistically feasible trajectory by using a chance constrained approach under the RRT* framework. LQG-MP provides the capability of considering the role of compensator in the motion planning phase and bounds the degree of uncertainty to appropriate size. In simulation, the planner successfully finds the optimal and robust solution. In addition, the planner is compared with an RRT* algorithm with dilated obstacles to show that it avoids being overly conservative.