Abstract This paper presents a planning framework for jumping over obstacles with quadruped robots. The framework accomplishes planning via a structured predictive control strategy that combines the use of heterogeneous simplified models over different prediction time scales. A receding multi-horizon predictive controller coordinates the approach before the jump using a kinematic point-mass model. Consideration of the optimal value function over different planning horizons enables the system to select an appropriate number of steps to take before jumping. The jumping motion is then tailored to the sensed obstacle by solving a nonlinear trajectory optimization problem. The solution of this problem online is enabled by exploiting the analyticity of the flow map for a planar bounding template model under polynomial inputs. By planning with this combination of models, MIT Cheetah 2 is shown to autonomously jump over obstacles up to 40 cm in height during high-speed bounding. Untethered results showcase the ability of the method to automatically adapt to obstacles of different heights and placements in a single trial.

中文翻译：

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用 MIT Cheetah 2 跳过障碍物

摘要 本文提出了一种四足机器人跳过障碍物的规划框架。该框架通过结构化预测控制策略完成规划，该策略结合了不同预测时间尺度上异构简化模型的使用。后退多水平预测控制器使用运动学点质量模型在跳跃前协调进场。考虑不同规划范围内的最优值函数使系统能够选择适当数量的步骤，然后再跳转。然后通过解决非线性轨迹优化问题，根据感知到的障碍物调整跳跃运动。通过利用多项式输入下平面边界模板模型的流程图的分析性，可以在线解决此问题。通过使用这种模型组合进行规划，MIT Cheetah 2 被证明可以在高速跳跃过程中自主跳过高达 40 厘米的障碍物。不受限制的结果展示了该方法在一次试验中自动适应不同高度和位置的障碍物的能力。