Affiliation:
1. Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, Massachusetts 02139
Abstract
The inertial parameters of manipulator rigid-body loads and links have been automatically estimated as a result of gen eral movement. The Newton-Euler equations have been recast to relate linearly the measured joint forces or torques via acceleration-dependent coefficients to the inertial parame ters, which have then been estimated by least squares. Load estimation was implemented on a PUMA 600 robot equipped with an R TI FS-B wrist force-torque sensor and on the MIT Serial Link Direct Drive Arm equipped with a Barry Wright Company Astek wrist force-torque sensor. Good estimates were obtained for load mass and center of mass, and the forces and torques due to movement of the load could be pre dicted accurately. The load moments of inertia were more difficult to estimate. Link estimation was implemented on the MIT Serial Link Direct Drive Arm. A good match was ob tained between joint torques predicted from the estimated parameters and the joint torques estimated from motor cur rents. The match actually proved superior to predicted torques based on link inertial parameters derived by CAD modeling. Restrictions on the identifiability of link inertial parameters due to restricted sensing and movement near the base have been addressed. Implications of estimation accu racy for manipulator dynamics and control have been consid ered.
Subject
Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modeling and Simulation,Software
Cited by
295 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献