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Online Motion Planning based on Nonlinear Model Predictive Control with Non-Euclidean Rotation Groups

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Document pages: 8 pages

Abstract: This paper proposes a novel online motion planning approach to robotnavigation based on nonlinear model predictive control. Common approaches relyon pure Euclidean optimization parameters. In robot navigation, however, statespaces often include rotational components which span over non-Euclideanrotation groups. The proposed approach applies nonlinear increment anddifference operators in the entire optimization scheme to explicitly considerthese groups. Realizations include but are not limited to quadratic form andtime-optimal objectives. A complex parking scenario for the kinematic bicyclemodel demonstrates the effectiveness and practical relevance of the approach.In case of simpler robots (e.g. differential drive), a comparative analysis ina hierarchical planning setting reveals comparable computation times andperformance. The approach is available in a modular and highly configurableopen-source C++ software framework.

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