Implementation of an Object-Grasping Robot Arm Using Stereo Vision Measurement and Fuzzy Control

In this paper, a method using a stereo vision device and fuzzy control to guide a robot arm to grasp a target object is proposed. The robot arm has five degrees of freedom including a gripper and four joints. The stereo vision device located beside the arm captures images of the target and the gripper. Image processing techniques such as color space transformation, morphologic operation, and 3-D position measurement are used to identify the target object and the gripper from the captured images and estimate their relative positions. Based on the estimated positions of the gripper and the target, the gripper can approach and grasp the target using inverse kinematics. However, since the robot arm’s accuracy of movement may be affected by gearbox backlash or hardware uncertainty, the gripper might not approach the desired position with precision using only inverse kinematics. Therefore, a fuzzy compensation method is added to correct any position errors between the gripper and target such that the gripper can grasp the target. Using the proposed method, the stereo vision device can not only locate the target object but also trace the position of the robot arm until the target object is grasped. Finally, some experiments are conducted to demonstrate successful implementation of the proposed method on the robot arm control.