Implementation of Object Detection and Recognition Algorithms on a Robotic Arm Platform Using Raspberry Pi

In this paper, it is aimed to implement object detection and recognition algorithms for a robotic arm platform. With these algorithms, the objects that are desired to be grasped by the gripper of the robotic arm are recognized and located. In the experimental setup that established, OWI-535 robotic arm with 4 DOF and a gripper, which is similar to the robotic arms used in the industry, is preferred. Local feature-based algorithms such as SIFT, SURF, FAST, and ORB are used on the images which are captured via the camera to detect and recognize the target object to be grasped by the gripper of the robotic arm. These algorithms are implemented in the software for object recognition and localization, which is written in C++ programming language using OpenCV library and the software runs on the Raspberry Pi embedded Linux platform. In the experimental studies, the performance of the features which are extracted with the SIFT, SURF, FAST, and ORB algorithms are compared. This study, which is first implemented with OWI-535 robotic arm, shows that the local feature-based algorithms are suitable for education and industrial applications.