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Planning under uncertainty is a key requirement for physical systems due tothe noisy nature of actuators and sensors. Using a belief space approach,planning solutions tend to generate actions that result in information seekingbehavior which reduce state uncertainty. While recent work has dealt withplanning for Gaussian beliefs, for many cases, a multi-modal belief is a moreaccurate representation of the underlying belief. This is particularly true inenvironments with information symmetry that cause uncertain data associationswhich naturally lead to a multi-modal hypothesis on the state. Thus, a plannercannot simply base actions on the most-likely state. We propose an algorithmthat uses a Receding Horizon Planning approach to plan actions thatsequentially disambiguate the multi-modal belief to a uni-modal Gaussian andachieve tight localization on the true state, called a Multi-Modal MotionPlanner (M3P). By combining a Gaussian sampling-based belief space planner withM3P, and introducing a switching behavior in the planner and beliefrepresentation, we present a holistic end-to-end solution for the belief spaceplanning problem. Simulation results for a 2D ground robot navigation problemare presented that demonstrate our method's performance.

Motion Planning in Non-Gaussian Belief Spaces (M3P): The Case of a Kidnapped Robot