This paper presents a stochastic/robust nonlinear model predictive control (NMPC) to enhance the robustness of legged locomotion against contact uncertainties. We integrate the contact uncertainties into the covariance propagation of stochastic/robust NMPC framework by leveraging the guard saltation matrix and an extended Kalman filter-like covariance update. We achieve fast stochastic/robust NMPC computation by utilizing the zero-order stochastic/robust NMPC algorithm with additional improvements in computational efficiency concerning the feedback gains. We conducted numerical experiments and demonstrate that the proposed method can accurately forecast future state covariance and generate trajectories that satisfies constraints even in the presence of the contact uncertainties. Hardware experiments on the perceptive locomotion of a wheeled-legged robot were also carried out, validating the feasibility of the proposed method in a real-world system with limited on-board computation.

翻译：本文提出一种随机/鲁棒非线性模型预测控制（NMPC）方法，以增强腿式运动在接触不确定性下的鲁棒性。我们通过引入守卫星跃变矩阵及扩展卡尔曼滤波类协方差更新策略，将接触不确定性融入随机/鲁棒NMPC框架的协方差传播过程。借助零阶随机/鲁棒NMPC算法，并通过改进反馈增益计算效率，实现了快速随机/鲁棒NMPC计算。数值实验表明，所提方法能够在存在接触不确定性的条件下准确预测未来状态协方差，并生成满足约束条件的运动轨迹。进一步在轮腿式机器人的感知运动硬件实验中验证了该方法在有限机载计算资源下的实际可行性。