学習した順キネモダニクスと非線形最小二乗最適化による高速高精度ロボット制御
High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization
学習した順方向キネモダニクスモデルと非線形最小二乗最適化を組み合わせ、事前計算された軌道なしで高速かつ高精度なロボット制御を実現するOptim-FKDを提案し、小型自律車両で有効性を示した。
著者: Pranav Atreya, Haresh Karnan, Kavan Singh Sikand, Xuesu Xiao, Sadegh Rabiee, Joydeep Biswas
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
