Chae H. An

Estimation of inertial parameters of manipulator loads and links

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.

Kinematic stability issues in force control of manipulators

Robots in force control mode often become unstable during contact with stiff environments, due mainly to the high gain nature of wrist force-sensor feedback. Three methods are presented for achieving stable force control compliant coverings or soft sensors, sell-tuning of force gains after estimation of environmental impedance, and reliance on fast open-loop joint torque control and using tip force sensor feedback in a slow loop to maintain accuracy. The latter method is proposed as the best one. Dynamic stability is analysed with a simple model of the robot and its environment. The analyses are verified by single-link experiments on the MIT Serial Link Direct Drive Arm.Robots in force control mode often become unstable during contact with stiff environments, due mainly to the high gain nature of wrist force-sensor feedback. To achieve stable force control, we propose an essential reliance on fast open-loop joint torque control, and relegate tip force sensor feedback to a slow loop to maintain accuracy. Dynamic stability is analyzed with a simple model of the robot and its environment. The analyses are verified by single-link experiments on the MIT Serial Link Direct Drive Arm.

Estimation of inertial parameters of rigid body links of manipulators

A method of estimating the mass, the location of center of mass, and the moments of inertia of each rigid body link of a robot during general manipulator movement is presented. The algorithm is derived from the Newton-Euler equations, and uses measurements of the joint torques as well as the measurement and calculation of the kinematics of the manipulator while it is moving. The identification equations are linear in the desired unknown parameters, and a modified least squares algorithm is used to obtain estimates of these parameters. Some of the parameters, however, are not identifiable due to the restricted motion of proximal links and the lack of full force/torque sensing. The algorithm was implemented on the MIT Serial Link Direct Drive Arm. A good match was obtained between joint torques predicted from the estimated parameters and the joint torques computed from motor currents.

Experimental evaluation of feedforward and computed torque control

Trajectory tracking errors resulting from the application of various controllers have been experimentally determined on the MIT Serial Link Direct Drive Arm. The controllers range from simple analog PD (proportional-derivative) control applied independently at each joint to feedforward and computed torque methods incorporating full dynamics. It was found that trajectory tracking errors decreased as more dynamic compensation terms were incorporated. There was no significant difference in trajectory tracking performance between the feedforward controller using independent digital servos and the full computed torque controller. Implementing the model-based controller highlights the need for accurate control of joint torque, accurate joint position and velocity sensing, and adequate sampling rates. >

The Role of Dynamic Models in Cartesian Force Control of Manipulators

Dynamic models are as important in Cartesian force control as they are in position control. A variety of Cartesian force control schemes are examined, comprising some that do incorporate a dynamic model into the control loop (resolved acceleration force control, operational space method, and impedance control) and some that do not (hybrid control and stiffness control). Stability analyses and experimental imple mentations are presented that demonstrate not only that using a dynamic model leads to more accurate control, but also that not using a model can in certain cases make force control unstable. Experiments on the MIT Serial Link Direct Drive Arm show that resolved acceleration force control is stable and accurate in producing force steps and sinusoidal force responses and in complying to sinusoidal position dis turbances.

Rigid body load identification for manipulators

A method for estimating the mass, the center of mass, and the moments of inertia of a rigid body load during general manipulator movement is presented. The algorithm is derived from the Newton-Euler equations and incorporates measurements of the force and torque from a wrist force/torque sensor and of the arm kinematics. The identification equations are linear in the desired unknown parameters, which are estimated by least squares. We have implemented this identification procedure on a PUMA 600 robot equipped with an RTI 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.

Experimental determination of the effect of feedforward control on trajectory tracking errors

Trajectory tracking errors resulting from the application of various controllers have been experimentally determined on the MIT Serial Link Direct Drive Arm. The controllers range from simple PD control applied independently at each joint to feedforward control incorporating full dynamics followed by a separate PD control loop. It was found that trajectory tracking errors decreased as more feedforward terms were incorporated.

Model-based control of a direct drive arm. I. Building models

Work on building robot models to be used in designing model-based controllers is described. Various algorithms are presented estimating the kinematic, link-inertial, and load-inertial parameters. It is shown experimentally that accurate estimates can be obtained automatically using sensor data taken during movement. The algorithms have been implemented on the MIT Serial Link Direct Drive Arm.<<ETX>>

Design and control of an air-bearing supported three degree-of-freedom fine positioner

The authors discuss design considerations and control issues of a fine-motion device which provides very high performance, precision, and speed (12-g acceleration, 0.2 mu m resolution). Coupled with a standard industrial robot and external sensing, improvements in precision up to two orders of magnitude can be achieved. Software enables it to be operated from a serial port of an IBM PC or PS/2. The moving element of the positioning head can be commanded to move over a +or-1 mm range in x and y, and rotated up to +or-1.75 degrees in theta about the z axis. The device provides variable compliance with compliance set by loop gain in a digital controller. It incorporates a novel three-degree-of-freedom motor, a pair of internal position sensors to enable controlled motion in the plane, and an air bearing to support the moving elements.<<ETX>>

Model-based control of a direct drive arm. II. Control

For pt.I see ibid., p.1374-9 (1988). Work on model-based control with the MIT Serial Link Direct Drive Arm is described. It is shown that model-based control leads to performance superior to control not based on carefully constructed models. Trajectory control, trajectory learning, and force control are treated. A new type of trajectory learning is considered in which a robot fine-tunes one particular trajectory through repetition. Various experiments with the direct drive arm are reported to validate the importance of model-based control.<<ETX>>