Summary: | Health pandemics such as Covid-19 have drastically shifted the world economics and boosted the development of automation
technologies in the industries for continuous operation without human intervention. This paper elaborates on an approach
to dynamically track and grasp moving objects using a robot arm. The robot arm has an eye-in-hand (EIH) configuration,
where a camera is installed on the robot arm’s end effector. The working principle of the robot arm in this paper is mainly
dependent on the recognition of augmented reality markers, i.e., Aruco markers, placed on the dynamically moving target
object with continuous tracking. Then, the proposed system updates the predicted location for the markers using the Kalman
filter for performing grasping. The proposed approach identifies the Aruco marker on the target object and estimates the
object’s location using previous states, and performs grasping at the exact predicted location. When extracted information
is updated, the vision system also implements a feedback control system for stability and reliability. The proposed approach
is tested using simulation of the dynamic moving object with different speeds and directions. The robot arm with the Kalman
filter can track and grasp the dynamic object at a speed of 0.2m/s with a 100% success rate while obtaining an 80% success
rate at a speed of 0.3m/s. In conclusion, the moving object’s speed is directly proportional to the grasping time until it
reaches the threshold speed for the camera in identifying the Aruco markers. Future works are required to improve the
dynamic visual servo algorithm with motion planning when obstacles are present in the path of robot grasping.
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