Online Motion Planning based on Nonlinear Model Predictive Control with Non-Euclidean Rotation Groups
This addresses motion planning for robots with rotational states, offering a modular open-source solution, but it appears incremental as it builds on existing nonlinear model predictive control methods.
The paper tackles robot navigation by proposing an online motion planning approach using nonlinear model predictive control that explicitly handles non-Euclidean rotation groups, demonstrating effectiveness in a complex parking scenario and comparable performance for simpler robots.
This paper proposes a novel online motion planning approach to robot navigation based on nonlinear model predictive control. Common approaches rely on pure Euclidean optimization parameters. In robot navigation, however, state spaces often include rotational components which span over non-Euclidean rotation groups. The proposed approach applies nonlinear increment and difference operators in the entire optimization scheme to explicitly consider these groups. Realizations include but are not limited to quadratic form and time-optimal objectives. A complex parking scenario for the kinematic bicycle model demonstrates the effectiveness and practical relevance of the approach. In case of simpler robots (e.g. differential drive), a comparative analysis in a hierarchical planning setting reveals comparable computation times and performance. The approach is available in a modular and highly configurable open-source C++ software framework.