Enhancing Robot Autonomy: Path Planning via Hand-Drawn Sketches and Computer Vision Integration

Abstract

This investigation delves into an advanced method for enhancing robot autonomy by integrating hand-drawn sketches with computer vision systems to optimize route planning effectively. The procedure starts with the capture of image data, which is then processed through multiple stages until the sketches are turned into exact coordinates that guide a robot. Canny edge detection, a specific computer vision technique, is employed to detect the edges of the drawn lines, facilitating precise and dependable navigation for autonomous robot operations. Initially, the image is subjected to noise reduction and edge enhancement before converting the sketch lines into Cartesian coordinates. This step is succeeded by point filtering and ordering to ascertain accurate path adherence by the robot, with no coordinates overlooked. Scale adjustments are also made to match digital image coordinates with the real-world setting, thereby improving the robot’s interaction based on the received visual inputs. The program has been tested in Robotic Operating System 2 (ROS) using MoveIt2 and also in RoboDK software, where it was used to verify the results. It is important to note that path planning, which focuses on determining an optimal route from start to finish, is a subset of motion planning, which also considers the dynamics and kinematics of the robot's movement along that route.