带姿态的机器人：面向敏捷足式机器人的无奇异基于四元数的模型预测控制
Robots with Attitude: Singularity-Free Quaternion-Based Model-Predictive Control for Agile Legged Robots
AI总结：
针对足式机器人大角度旋转时姿态表示易出现奇异的问题，提出基于无奇异单位四元数的MPC框架，修改iLQR算法适配几何特性，推导仅用基础数学知识，经四足与人形机器人实验验证了性能与计算效率。
摘要：
我们提出了一种适用于足式机器人的模型预测控制（MPC）框架，可避免大角度旋转过程中与欧拉角等常见三参数姿态表示相关的奇异问题。该方法采用无奇异单位四元数对机器人姿态进行参数化，并对迭代线性二次调节器（iLQR）算法进行修改以适配由此产生的几何特性。算法推导仅需基础微积分与线性代数知识，刻意规避了李群的抽象概念与符号体系。我们在四足机器人和人形机器人上开展了多项实验，验证了四元数MPC的性能与计算效率。
英文摘要：
We present a model-predictive control (MPC) framework for legged robots that avoids the singularities associated with common three-parameter attitude representations like Euler angles during large-angle rotations. Our method parameterizes the robot's attitude with singularity-free unit quaternions and makes modifications to the iterative linear-quadratic regulator (iLQR) algorithm to deal with the resulting geometry. The derivation of our algorithm requires only elementary calculus and linear algebra, deliberately avoiding the abstraction and notation of Lie groups. We demonstrate the performance and computational efficiency of quaternion MPC in several experiments on quadruped and humanoid robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
