Advanced Robotics

A robot tool center point is a coordinate frame transformation at the end of the serial chain manipulator. This allows every different tool to have a different spatial configuration and for these difference to be reflected in the robot controller path generator and not in user modifications to any path/trajectory. In the case of the LARPS robot the process tool had a linear offset, but the mapping tool had a full 6 degree of freedom homogenous transformation. Since the tooling was optical a technique was developed for calculating the tool center point transformation using the robot and tool plus a constant geometry fixture that was scanned from multiple points and then a regression algorithm was applied to generate the TCP transformation. This was developed in junction with Dr. John Craig at Silma using Cimstation in 1992. Cimstation is no longer sold as far as I know.

It is filled with nice images and it documents the technique quite well. Perceptron and Diffracto have used this technique for more than 2 decades to calibrate their sensors for their automotive assembly body-in-white gauges that virtually automobile space frames are now made with. Perceptron now makes a cloud fitting reverse engineering product called Scan Works that makes use of the surface mapping techniques and this tool transformation algorithm.

I had a 3 meter standoff 3 meter ambiquity LIDAR sensor manufactured in 1992 for 3D surface mapping of large objects. This system also used a derivative of the tool center point calibration algorithm. The sensor because of it size was fixture mounted in the test workcell. By moving a tool (we actually had the exact same pointer tool made to .001 inch tolerance for calibration) with a robot the sensor was transformed into a metric space that was usable. The LIDAR sensors were very expensive, and relatively fragile due to the rotating polygonal mirrors that generated the 3-D data cloud. The optical “telescope” that generated the phase shift that was calibrated was prone to drift from mechanical vibration and temperature variations. They were never made in high enough volume to be a successful commercial product. Odetics in Anaheim CA also made a family of these scanners that were slight more accurate, better designed, smaller and lighter with an active temperature control in the sensor head for minimizing “telescope” problems. They were discontinued in the early ’90’s for the same reason. A good highly accurate 2-D scanner attached to a robot arm gave equally good data at the expense of calibration, motion and tool changing.

Johan Hallenberg did an excellent job in documenting his technique the thesis is very readable, sorry that it is not new. You can download it here. He also implemented the vision algorithms using the Intel OpenCV open source product. It runs very nicely in Ch interpreter from softintegration for those of you that want to script in ANSI C.