Title: Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control Show Chinese title

Title: 风险受限的非线性机器人运动规划与集成感知和控制

Abstract: Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and prediction uncertainties, either ignoring them altogether or making questionable assumptions of Gaussianity. Robots with nonlinear dynamics and complex sensing modalities operating in an uncertain environment demand more careful consideration of how uncertainties propagate across stack layers. We propose a framework to integrate perception, motion planning, and control by explicitly incorporating perception and prediction uncertainties into planning so that risks of constraint violation can be mitigated. Specifically, we use a nonlinear model predictive control based steering law coupled with a decorrelation scheme based Unscented Kalman Filter for state and environment estimation to propagate the robot state and environment uncertainties. Subsequently, we use distributionally robust risk constraints to limit the risk in the presence of these uncertainties. Finally, we present a layered autonomy stack consisting of a nonlinear steering-based distributionally robust motion planning module and a reference trajectory tracking module. Our numerical experiments with nonlinear robot models and an urban driving simulator show the effectiveness of our proposed approaches. Show Chinese abstract

Abstract: 鲁棒的自主堆栈需要感知、运动规划和控制层的紧密集成，但这些层通常未能充分考虑固有的感知和预测不确定性，要么完全忽略它们，要么做出可疑的高斯性假设。 在不确定环境中运行的具有非线性动力学和复杂传感模态的机器人，需要更仔细地考虑不确定性如何在堆栈各层之间传播。 我们提出了一种框架，通过将感知和预测不确定性显式纳入规划中，以集成感知、运动规划和控制，从而减轻约束违反的风险。 具体而言，我们使用基于非线性模型预测控制的转向定律，并结合基于去相关方案的无迹卡尔曼滤波器进行状态和环境估计，以传播机器人状态和环境的不确定性。 随后，我们使用分布鲁棒的风险约束来限制这些不确定性存在下的风险。 最后，我们展示了一个分层的自主堆栈，包括一个基于非线性转向的分布鲁棒运动规划模块和一个参考轨迹跟踪模块。 我们的数值实验使用非线性机器人模型和城市驾驶模拟器，展示了我们所提出方法的有效性。