Abstract
Dynamic and continuous jumping remains an open yet challenging problem in bipedal robot control. The choice of dynamic models in trajectory optimization (TO) problems plays a huge role in trajectory accuracy and computation efficiency, which normally cannot be ensured simultaneously. In this letter, we propose a novel adaptive-model optimization approach, a unified framework of Adaptive-model TO and Adaptive-frequency Model Predictive Control (MPC), to effectively realize continuous and robust jumping on HECTOR bipedal robot. The proposed Adaptive-model TO fuses adaptive-fidelity dynamics modeling of bipedal jumping motion for model fidelity necessities in different jumping phases to ensure trajectory accuracy and computation efficiency. In addition, conventional approaches have unsynchronized sampling frequencies in TO and real-time control, causing the framework to have mismatched modeling resolutions. We adapt MPC sampling frequency based on TO trajectory resolution in different phases for effective trajectory tracking. In hardware experiments, we have demonstrated robust and dynamic jumps covering a distance of up to 40 cm (57% of robot height). To verify the repeatability of this experiment, we run 53 jumping experiments and achieve 90% success rate. In continuous jumps, we demonstrate continuous bipedal jumping with terrain height perturbations (up to 5 cm) and discontinuities (up to 20 cm gap).
Abstract (translated)
动态和连续跳跃在双足机器人控制中仍然是一个开放且具有挑战性的问题。轨迹优化（TO）问题中动态模型的选择在轨迹精度和计算效率方面具有巨大的影响，通常不能同时确保。在这封信中，我们提出了一个新的人工模型优化方法，一个自适应模型TO和自适应频率模型预测控制（MPC）的统一框架，以有效地在HECTOR双足机器人上实现连续和鲁棒的跳跃。所提出的自适应模型TO将适应性跳跃运动的双足跳跃动量建模与模型精度需求在不同的跳跃阶段相结合，以确保轨迹精度和计算效率。此外，传统方法在TO和实时控制中具有异步采样频率，导致框架的建模分辨率不匹配。我们根据不同跳跃阶段的TO轨迹分辨率调整MPC采样频率，以实现有效的轨迹跟踪。在硬件实验中，我们证明了覆盖距离多达40厘米（机器人高度的57%）的稳健和动态跳跃。为了验证这一实验的重复性，我们进行了53次跳跃实验，并获得了90%的成功率。在连续跳跃中，我们展示了在地面高度扰动（最高达5厘米）和间断（最高达20厘米的缺口）条件下的连续双足跳跃。
URL
https://arxiv.org/abs/2404.11807
