We present a versatile nonlinear model predictive control (NMPC) formulation for quadrupedal locomotion. Our formulation jointly optimizes a base trajectory and a set of footholds over a finite time horizon based on simplified dynamics models. We leverage second-order sensitivity analysis and a sparse Gauss-Newton (SGN) method to solve the resulting optimal control problems. We further describe our ongoing effort to verify our approach through simulation and hardware experiments. Finally, we extend our locomotion framework to deal with challenging tasks that comprise gap crossing, movement on stepping stones, and multi-robot control.

翻译：我们提出了一个多功能的非线性模型预测控制(NMPC)配方,用于四轨移动。我们的配方共同优化了基础轨迹和一套基于简化动态模型的固定时空的立足点。我们利用二阶灵敏度分析和稀疏的Gaus-Newton(SGN)方法来解决由此产生的最佳控制问题。我们进一步描述了我们目前通过模拟和硬件实验来核查我们的方法的努力。最后,我们扩展了移动框架,处理具有挑战性的任务,包括跨越差距、踏脚石移动和多机器人控制。