In control of industrial manipulators, the position from the motor encoder has been the only sensor measurement for axis control. In this case, it is not easy to estimate the end-effector motion accurately because of the kinematic errors of links, joint flexibility of gear mechanisms, and so on. Direct measurement of the end-effector using the vision sensor is considered as a solution but its performance is often limited by the slow sampling rate and the latency. To overcome these limitations, this paper extends the basic idea of the kinematic Kalman filter (KKF) to general rigid body motion leading to the formulation of the multidimensional kinematic kalman filter (MD-KKF). By combining the measurements from the vision sensor, the accelerometers and the gyroscopes, the MD-KKF can recover the intersample values and compensate for the measurement delay of the vision sensor providing the state information of the end-effector fast and accurately. The performance of the MD-KKF is verified experimentally using a planar two-link robot. The MD-KKF will be useful for widespread applications such as the high speed visual servo and the high-performance trajectory learning for robot manipulators, as well as the control strategies which require accurate velocity information.

1.
Good
,
M. C.
, and
Sweet
,
L. M.
, 1984, “
Structures for Sensor-Based Robot Motion Control
,”
Proceedings of the American Control Conference
,
San Diego
,
CA, Jun
.
2.
Sweet
,
L. M.
and
Good
,
M. C.
, 1985, “
The Redefinition of the Robot Motion-Control Problem
,”
IEEE Control Syst. Mag.
0272-1708,
5
(
3
), pp.
18
25
.
3.
Shim
,
H. C.
,
Kochem
,
M.
, and
Tomizuka
,
M.
, 1998, “
Use of Accelerometer for Precision Motion Control of Linear Motor Driven Positioning System
,”
Proceedings of the 24th Annual Conference of the IEEE
, Vol.
4
,
Aachen, Germany
, pp.
2409
2414
.
4.
Lee
,
D. J.
, and
Tomizuka
,
M.
, 2001, “
State/Parameter/Disturbance Estimation With an Accelerometer in Precision Motion Control of a Linear Motor
,”
Proceedings of ASME IMECE’01
,
New York
.
5.
Jeon
,
S.
, and
Tomizuka
,
M.
, 2007, “
Benefits of Acceleration Measurements in Velocity Estimation and Motion Control
,”
Control Eng. Pract.
0967-0661,
15
(
3
), pp.
325
332
.
6.
Titterton
,
D.
, and
Weston
,
J.
, 2004,
Strapdown Inertial Navigation Technology
,
2nd ed.
,
The Institution of Electrical Engineers
,
Stevenage, UK
.
7.
Armesto
,
L.
,
Tornero
,
J.
and
Vincze
,
M.
, 2007, “
Fast Ego-Motion Estimation With Multi-Rate Fusion of Inertial and Vision
,”
Int. J. Robot. Res.
0278-3649,
26
(
6
), pp.
577
589
.
8.
Tao
,
Y.
,
Hu
,
H.
, and
Zhou
,
H.
, 2007, “
Integration of Vision and Inertial Sensors for 3D Arm Motion Tracking in Home-Based Rehabilitation
,”
Int. J. Robot. Res.
0278-3649,
26
(
6
), pp.
607
624
.
9.
Jeon
,
S.
, 2007, “
State Estimation Based on Kinematic Model and Relay Feedback Stability Arising in Controlled Mechanical Systems
,” Ph.D. thesis, University of California at Berkeley, Berkeley, CA.
You do not currently have access to this content.