论文标题
计划意外：明确优化运行中的地面不确定性动议
Planning for the Unexpected: Explicitly Optimizing Motions for Ground Uncertainty in Running
论文作者
论文摘要
我们提出了一种为减少订单，动态运行模型生成驱动计划的方法。这种方法明确地实现了对地面不确定性的鲁棒性。生成的计划不是积极稳定的固定身体轨迹：相反，该计划与还原订单模型的被动动力学相互作用，以创建出现的鲁棒性。目的是为腿部机器人制定计划，这些计划将是对环境不完善的不完善感，并使用太复杂而无法实时优化的动态。在这种腿部运动的动态模型中工作，我们将一组干扰案例与名义案例一起优化，所有情况都具有链接的输入。由于运行模型的混合动力学，输入链接是非平凡的，但是我们的解决方案是有效的并且具有分析梯度。提出的优化程序比标准轨迹优化的优化速度明显慢，但是导致步态可靠，这些步态极有效地拒绝干扰而无需进行任何重新启动。
We propose a method to generate actuation plans for a reduced order, dynamic model of bipedal running. This method explicitly enforces robustness to ground uncertainty. The plan generated is not a fixed body trajectory that is aggressively stabilized: instead, the plan interacts with the passive dynamics of the reduced order model to create emergent robustness. The goal is to create plans for legged robots that will be robust to imperfect perception of the environment, and to work with dynamics that are too complex to optimize in real-time. Working within this dynamic model of legged locomotion, we optimize a set of disturbance cases together with the nominal case, all with linked inputs. The input linking is nontrivial due to the hybrid dynamics of the running model but our solution is effective and has analytical gradients. The optimization procedure proposed is significantly slower than a standard trajectory optimization, but results in robust gaits that reject disturbances extremely effectively without any replanning required.
