We propose a two-phase risk-averse architecture for controlling stochastic nonlinear robotic systems. We present Risk-Averse Nonlinear Steering RRT* (RANS-RRT*) as an RRT* variant that incorporates nonlinear dynamics by solving a nonlinear program (NLP) and accounts for risk by approximating the state distribution and performing a distributionally robust (DR) collision check to promote safe planning. The generated plan is used as a reference for a low-level tracking controller. We demonstrate three controllers: finite horizon linear quadratic regulator (LQR) with linearized dynamics around the reference trajectory, LQR with robustness-promoting multiplicative noise terms, and a nonlinear model predictive control law (NMPC). We demonstrate the effectiveness of our algorithm using unicycle dynamics under heavy-tailed Laplace process noise in a cluttered environment.

翻译：我们提出了控制随机非线性机器人系统的两阶段风险规避结构。我们将风险-反非线性指导RRT* (RANS-RRT*)作为RRT* 变体,通过解决非线性程序(NLP)纳入非线性动态,并通过接近国家分布和进行分布强的碰撞检查来说明风险,以促进安全规划。产生的计划被用作低级跟踪控制器的参考。我们展示了三个控制器:环绕参照轨线性动态的有限地平线线线线线性线性二次曲线调节器(LQR),以强度促进多复制性噪音术语的LQR,以及非线性模型预测控制法(NMPC),我们展示了在封闭环境中使用重尾拉贝过程噪音下的单周期性动态的算法的有效性。