Optical Flow-Based Robot Navigation and Obstacle Avoidance Algorithm
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In this article, we discuss an optical flow-based algorithm for robot navigation and obstacle avoidance. This algorithm employs computer vision techniques to determine the robot's movement direction and velocity by analyzing pixel variations between consecutive image frames. The implementation typically involves key functions such as feature detection (using methods like Lucas-Kanade or Horn-Schunck), motion vector calculation, and velocity estimation through optical flow field analysis. To validate the algorithm's effectiveness, we conducted simulation experiments on a virtual robot platform. These experiments demonstrated that the algorithm accurately navigates the robot, avoids obstacles, and adapts to different environmental conditions and motion requirements. The algorithm shows broad application potential for various robotic navigation and automation tasks, including drones, autonomous vehicles, and other mobile robotics systems.
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