Robust Trajectory Optimization Over Uncertain Terrain With Stochastic Complementarity
Robust Trajectory Optimization Over Uncertain Terrain With Stochastic Complementarity
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DOI：
10.1109/lra.2021.3056064
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发表时间：
2021-04-01
影响因子：
5.2
通讯作者：
Zhao, Ye
中科院分区：
文献类型：
--
作者：
Drnach, Luke;Zhao, Ye
具有丰富接触行为的轨迹优化最近因在没有预先指定的地面接触序列的情况下产生不同的运动行为而受到关注。然而，这些方法依赖于机器人动力学和地形的精确模型，并且容易受到不确定性的影响。最近的工作试图处理系统模型中的不确定性，但很少有研究接触动力学中的不确定性。在这封信中，我们模型的不确定性来自地形和设计相应的风险敏感的目标接触隐式轨迹优化。特别是，我们参数化的不确定性，从地形接触距离和摩擦系数使用概率分布，并提出了相应的期望剩余最小化成本的方法。我们评估我们的方法在三个简单的机器人的例子，包括腿跳跃机器人，我们基准我们的例子在模拟对一个强大的最坏情况下的解决方案。我们表明，我们的风险敏感的方法产生接触厌恶的轨迹，是强大的地形扰动。此外，我们证明了所产生的轨迹收敛到由传统的，非鲁棒的方法所产生的地形模型变得更加确定。我们的研究标志着一个完全强大的，接触隐式的方法，适合部署机器人在现实世界的地形上迈出了重要的一步。
Trajectory optimization with contact-rich behaviors has recently gained attention for generating diverse locomotion behaviors without pre-specified ground contact sequences. However, these approaches rely on precise models of robot dynamics and the terrain and are susceptible to uncertainty. Recent works have attempted to handle uncertainties in the system model, but few have investigated uncertainty in contact dynamics. In this letter, we model uncertainty stemming from the terrain and design corresponding risk-sensitive objectives for contact-implicit trajectory optimization. In particular, we parameterize uncertainties from the terrain contact distance and friction coefficients using probability distributions and propose a corresponding expected residual minimization cost approach. We evaluate our method in three simple robotic examples, including a legged hopping robot, and we benchmark one of our examples in simulation against a robust worst-case solution. We show that our risk-sensitive method produces contact-averse trajectories that are robust to terrain perturbations. Moreover, we demonstrate that the resulting trajectories converge to those generated by a traditional, non-robust method as the terrain model becomes more certain. Our study marks an important step towards a fully robust, contact-implicit approach suitable for deploying robots on real-world terrain.