Randomized Model Predictive Navigation

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Department of Mechanical Engineering, University of Delaware
A new methodology for implementing nonlinear receding horizon optimization is presented, with direct application to robot navigation in cluttered environments. The methodology combines elements from statistical learning theory with nonlinear receding horizon schemes that use control Lyapunov functions as terminal costs, while relaxing the conditions on the time derivatives of the latter, based on a new result for stability of nonlinear systems with switching dynamics. As the theoretical analysis indicates, and numerical results verify, the proposed receding horizon scheme can utilize terminal costs that are not control Lyapunov functions. The resulting strategy is shown to outperform traditional potential field-based techniques, even when additional optimization objectives are imposed, and allows for trade-offs between performance and computational complexity.
Robot navigation, Lyapunov functions