A Study on Sparse Hierarchical Inverse Kinematics Algorithms for Humanoid Robots
A Study on Sparse Hierarchical Inverse Kinematics Algorithms for Humanoid Robots
复制标题
仿人机器人稀疏层次逆运动学算法研究
DOI：
10.1109/lra.2019.2954820
复制
发表时间：
2020
影响因子：
5.2
通讯作者：
N. Tsagarakis
中科院分区：
文献类型：
--
作者：
E. Hoffman;Matteo Parigi Polverini;Arturo Laurenzi;N. Tsagarakis
在类人机器人平台中，基于关节速度或加速度的L2正则化的经典逆运动学算法倾向于参与所有可用自由度的运动，从而导致整个机器人结构的运动，其本质上不是稀疏的。稀疏性在运动控制中的作用最近由于各种原因在机器人领域引起了兴趣，例如，类似人类的运动，人机交互，驱动简约，但仍然缺少详尽的数学分析。为了解决这个问题，我们在这里提出并比较可能的稀疏优化方法应用于分层逆运动学的人形机器人。这是通过LASSO回归和MILP优化来解决IK问题。进一步引入稀疏回归问题的一阶公式，以减少关节速度分布上的抖动。本文介绍了所提出的方法背后的理论，并进行了比较分析的基础上模拟和真实的实验不同的人形平台。
In humanoid robotic platforms, classical inverse kinematics algorithms, based on L2-regularization of joint velocities or accelerations, tends to engage the motion of all the available degrees of freedom, resulting in movements of the whole robot structure, which are inherently not sparse. The role of sparsity in motion control has recently gained interest in the robotics community for various reasons, e.g., human-like motions, and human-robot interaction, actuation parsimony, yet an exhaustive mathematical analysis is still missing. In order to address this topic, we here propose and compare possible sparse optimization approaches applied to hierarchical inverse kinematics for humanoid robots. This is achieved through LASSO regression and MILP optimization to resolve the IK problem. A first order formulation of the sparse regression problem is further introduced to reduce chattering on the joint velocity profiles. This article presents the theory behind the proposed approaches and performs a comparison analysis based on simulated and real experiments on different humanoid platforms.