Vision-based intelligent path planning for SCARA arm

This paper proposes a novel algorithm combining object detection and potential field algorithm for autonomous operation of SCARA arm. The start, obstacles, and goal states are located and detected through the RetinaNet Model. The model uses standard pre-trained weights as checkpoints which is trained with images from the working environment of the SCARA arm. The potential field algorithm then plans a suitable path from start to goal state avoiding obstacle state based on results from the object detection model. The algorithm is tested with a real prototype with promising results.