An autonomous excavator with vision-based track-slippage control

Abstract

This paper describes a vision-based control system for a tracked mobile robot (an excavator). The system includes several controllers that collaborate to move the mobile vehicle from a starting position to a goal position. First, the path planner designs an optimum path using a predefined elevation map of the work space. Second, a fuzzy logic path-tracking controller estimates the rotational and translational velocities for the vehicle to move along the predesigned path. Third, a cross coupling controller corrects the possible orientation error that may occur when moving along the path. A motor controller then converts the track velocities to the corresponding rotational wheel velocities. Fourth, a vision-based motion tracking system is implemented to find the three-dimensional (3-D) motion of the vehicle as it moves in the work space. Finally, a specially-designed slippage controller detects slippage by comparing the motion through reading of flowmeters and the vision system. If slippage has occurred, the remaining path is corrected within the path tracking controller to stop at the goal position. Experiments are conducted to test and verify the presented control system. An analysis of the results shows that improvement is achieved in both path-tracking accuracy and slippage control problems.