Online planning and execution of acrobatic maneuvers pose significant challenges in legged locomotion. Their underlying combinatorial nature, along with the current hardware's limitations constitute the main obstacles in unlocking the true potential of legged-robots. This letter tries to expose the intricacies of these optimal control problems in a tangible way, directly applicable to the creation of more efficient online trajectory optimisation frameworks. By analysing the fundamental principles that shape the behaviour of the system, the dynamics themselves can be exploited to surpass its hardware limitations. More specifically, a trajectory optimisation formulation is proposed that exploits the system's high-order nonlinearities, such as the nonholonomy of the angular momentum, and phase-space symmetries in order to produce feasible high-acceleration maneuvers. By leveraging the full-centroidal dynamics of the quadruped ANYmal C and directly optimising its footholds and contact forces, the framework is capable of producing efficient motion plans with low computational overhead. The feasibility of the produced trajectories is ensured by taking into account the configuration-dependent inertial properties of the robot during the planning process, while its robustness is increased by supplying the full analytic derivatives & hessians to the solver. Finally, a significant portion of the discussion is centred around the deployment of the proposed framework on the ANYmal C platform, while its true capabilities are demonstrated through real-world experiments, with the successful execution of high-acceleration motion scenarios like the squat-jump.

翻译：在线规划与执行杂技动作对腿足式运动提出了重大挑战。其潜在的组合特性以及当前硬件的局限性，构成了释放腿足机器人真正潜力的主要障碍。本文旨在以具体的方式揭示这些最优控制问题的复杂性，使其能直接应用于构建更高效的在线轨迹优化框架。通过分析塑造系统行为的基本原理，可利用动力学本身来突破硬件限制。具体而言，提出了一种轨迹优化公式，该公式利用系统的高阶非线性特性（如角动量非完整性）和相空间对称性，以生成可行的高加速机动。通过利用四足机器人ANYmal C的全质心动力学，并直接优化其落脚点与接触力，该框架能够以较低的计算开销生成高效的运动规划。通过在规划过程中考虑机器人依赖于构型的惯性特性，确保了所生成轨迹的可行性；同时，通过向求解器提供全解析导数与海森矩阵，增强了其鲁棒性。最后，讨论重点围绕所提框架在ANYmal C平台上的部署展开，并通过真实世界实验（如成功执行蹲跳等高加速运动场景）展示了其实际能力。