High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization
High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization
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DOI：
10.1109/iros47612.2022.9981259
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
P. Atreya;Haresh Karnan;Kavan Singh Sikand;Xuesu Xiao;Garrett Warnell;Sadegh Rabiee;P. Stone;Joydeep Biswas
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文献类型：
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作者：
P. Atreya;Haresh Karnan;Kavan Singh Sikand;Xuesu Xiao;Garrett Warnell;Sadegh Rabiee;P. Stone;Joydeep Biswas
高速机器人的精确控制需要一个能够考虑机器人与环境的动力学相互作用的控制系统。在机器人逆运动学模型的学习方面，前人的研究已经成功地捕获了复杂的运动学效应。然而，这些方法可以应用的控制问题的类型仅限于下列预先计算的运动动力学可行轨迹。在本文中，我们提出了optimo -FKD，这是一种利用学习的前向动力学（FKD）模型和非线性最小二乘优化的精确、高速机器人控制的新公式。优化- fkd可用于精确，高速控制任何由非线性最小二乘目标指定的控制任务。optimo - fkd可以实时解决路径跟踪和时间最优控制等控制目标，而无需访问预先计算的动力学可行轨迹。我们通过在十分之一的自动驾驶汽车上进行实验，实证地证明了我们方法的这些能力。我们的研究结果表明，与基线方法相比，优化- fkd可以更准确地跟踪期望的轨迹，并且可以找到更好的最优控制问题的解决方案。
Accurate control of robots at high speeds requires a control system that can take into account the kinodynamic interactions of the robot with the environment. Prior works on learning inverse kinodynamic (IKD) models of robots have shown success in capturing the complex kinodynamic effects. However, the types of control problems these approaches can be applied to are limited only to that of following pre-computed kinodynamically feasible trajectories. In this paper we present Optim-FKD, a new formulation for accurate, high-speed robot control that makes use of a learned forward kinodynamic (FKD) model and non-linear least squares optimization. Optim-FKD can be used for accurate, high speed control on any control task specifiable by a non-linear least squares objective. Optim-FKD can solve for control objectives such as path following and time-optimal control in real time, without needing access to pre-computed kinodynamically feasible trajectories. We empirically demonstrate these abilities of our approach through experiments on a scale one-tenth autonomous car. Our results show that Optim-FKD can follow desired trajectories more accurately and can find better solutions to optimal control problems than baseline approaches.