REAL TIME PREDICTIVE CONTROL OF UR5 ROBOTIC ARM THROUGH HUMAN UPPER LIMB MOTION TRACKING

Abstract

This thesis reports the authors’ results on developing a real-time predictive control system for an Universal Robot UR5 robotic arm through human motion capture with a visualization environment built in the Blender Game Engine. The UR5 is a 6 degree of freedom serial manipulator commonly used in academia and light industry. It is a very safe robot by design that comes at a cost of a rather limited API with very little support of real-time operation. The motion tracking is performed by a wireless low-cost inertial motion capture setup produced in-house. The motion tracker is an extension of author’s previous work on replacing a forearm IMU in conventional inertial motion tracking suits with a potentiometer in order remove anatomical constraints from corresponding data fusion algorithms. The external controller incorporates an iTaSC SDLS IK solver and a Python wrapped C explicit model predictive controller generated using the Multi Parametric Toolbox. The visualisation provides the user with the feedback on the robot’s progress towards the target. It is planned to extend the visualisation to virtual reality in future. Tests have shown that the robot follows the operator’s wrist position and orientation with an average of 0.05sec. time lag in the case when the operator moves under the robot’s velocity and acceleration limits. When the operator moves too fast for the robot to keep up in real-time, the robot is able to catch up with the operator with little or no overshooting. Thesis results are described in a late-breaking report and demo accepted by the 12th annual IEEE/ACM international conference Human-Robot Interaction (HRI2017).