外观
论文arXiv 2610.1297219 天前
未知负载下人形机器人的带最优姿态规划的自适应全身MPC
原文链接：Adaptive Whole-Body MPC with Optimal Posture Planning for Humanoid Robots under Unknown Payloads（arXiv 2610.12972 · 发布日期：2026-10-09）
Adaptive Whole-Body MPC with Optimal Posture Planning for Humanoid Robots under Unknown Payloads
针对人形机器人未知变负载搬运中的失衡问题，提出融合质心MPC、自适应模糊平衡与最优姿态规划的全身控制，并在仿真和硬件实验验证。
核心要点
- 未知且变化的负载会影响全身动力学，使传统控制器在举升和物料搬运中可能失稳。
- 架构结合质心MPC、自适应模糊平衡控制和基于路点的最优姿态规划。
- 姿态规划采用三重积分模型，生成满足CoM稳定、关节限位和全身可行性的坐站轨迹。
- 仿真和硬件实验显示，相比非自适应基线，框架提升平衡鲁棒性并减小跟踪退化。
验证范围与局限：验证限于摘要所述仿真和硬件实验中的下降、抓取、举升、搬运、放置及姿态转换任务。
英文摘要 (Abstract)
Humanoid robots performing lifting and material-handling tasks must maintain balance despite unknown and varying payloads. However, payload uncertainty significantly affects whole-body dynamics and may destabilize conventional controllers. We propose an adaptive whole-body control architecture that integrates three components: (i) a centroidal model predictive controller (MPC) that generates dynamically feasible reference motions, (ii) an adaptive fuzzy balance controller that compensates for payload-induced model mismatch by generating corrective torques distributed across the supporting-leg joints, and (iii) a waypoint-based optimal posture planner built on a triple-integrator model that produces smooth sit-to-stand and stand-to-sit trajectories satisfying center-of-mass (CoM) stability constraints, joint limits, and whole-body feasibility requirements. Together, these components enable stable lowering, grasping, lifting, carrying, and placing behaviors under significant payload uncertainty. Extensive simulation and hardware experiments demonstrate that the proposed framework improves balance robustness and reduces tracking degradation during posture transitions and lifting tasks compared to non-adaptive baselines.
作者：Manh Cuong Nguyen, Đức Cường Vũ, Viet Anh Vu, Đức Tuấn Phạm, Toan Nguyen, Minh Hiep Phung, Ngo Anh Vien, Minh Nhat Vu
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