发表机构
Technical University of Munich (TUM); Tampere University; University of Naples Federico II; Mohamed Bin Zayed University of Artificial Intelligence（慕尼黑工业大学; 坦佩雷大学; 那不勒斯费德里科二世大学; 穆罕默德·本·扎耶德人工智能大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种不确定性感知的轨迹优化与耦合MPC框架，用于四足机器人非抓取运输，显著减少物体滑动并降低质心跟踪误差。
AI 中文摘要
本文提出了一种针对四足机器人的鲁棒非抓取物体运输框架。一种不确定性感知的轨迹优化方法生成物体运动，使闭环灵敏度对不确定参数最小化。得到的参考轨迹通过一个耦合的凸模型预测控制器进行跟踪，该控制器联合预测四足机器人的质心动力学和负载，随后通过一个全身QP（二次规划）强制执行地面反作用力约束。该方法通过在物体惯性参数变化下的广泛仿真和真实世界实验进行评估。其性能与固定方向和直线轨迹作为基线进行比较。结果表明，与固定方向基线相比，优化后的物体运动将滑动减少了约50%，与直线基线相比减少了30%，同时实现了更低的机器人质心跟踪误差。
英文摘要
In this paper, we present a robust nonprehensile object transportation framework for quadruped robots. An uncertainty-aware trajectory optimization method generates object motions with minimal closed-loop sensitivity to uncertain parameters. The resulting reference trajectory is tracked using a coupled convex model predictive controller that jointly predicts the CoM dynamics of the quadruped and the payload followed by a whole-body QP that enforces ground reaction constraints. The approach is evaluated through extensive simulations and real-world experiments under variations in the object's inertial parameters. Its performance is compared with fixed-orientation and straight-line trajectories as baseline. The results show that the optimized object motion reduces the sliding by approximately 50% compared with the fixed-orientation baseline and 30% compared with the straight-line baseline, while also achieving lower robot CoM tracking errors.
Comments9 pages, 8 figures
