FPGA-BASED ARTIFICIAL VISION SYSTEM FOR ROBOT AND OBSTACLES DETECTION UNDER STRONG LUMINOSITY VARIABILITY

This paper presents a complete artificial vision system development and implementation for a mobile robot recognition and obstacles detection, which integrates a statistical segmentation method with frequency filtering in order to achieve luminosity independence, exploiting the advantages of known image processing techniques by mixing them into a robotic application. The system proposed determines the mobile robot's position and orientation, using a color segmentation approach based on the Mahalanobis Distance, and the position and size of obstacles in the robot's environment using a parallel scheme based on both Sobel edge detector and Otsu's threshold. The Mahalanobis distance calculus was implemented using a real-time FPGA architecture, in order to detect the robot's position. Tests in the real robot's environment are presented obtaining results that are independent from the background characteristics and strongly robust on the luminosity variability.