论文标题
基于非线性模型预测控制的在线运动计划与非欧几里得旋转组
Online Motion Planning based on Nonlinear Model Predictive Control with Non-Euclidean Rotation Groups
论文作者
论文摘要
本文提出了一种基于非线性模型预测控制的机器人导航的新型在线运动计划方法。通用方法依赖于纯欧几里得优化参数。但是，在机器人导航中，状态空间通常包括跨越非欧几里得旋转组的旋转组件。所提出的方法在整个优化方案中应用非线性增量和差异运算符，以明确考虑这些组。实现包括但不限于二次形式和时间优势。运动学自行车模型的复杂停车场景证明了该方法的有效性和实际相关性。在更简单的机器人（例如差分驱动器）的情况下，层次计划设置中的比较分析显示出可比的计算时间和性能。该方法可在模块化和高度可配置的开源C ++软件框架中获得。
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.
