To achieve the recognition and positioning functions of indoor mobile robots under limited computing power conditions, a method based on color recognition for robot recognition and positioning is proposed. The global image of the robot working in the field is collected by a camera outside the field, and the position of the robot is obtained through computer vision processing. Then, the robot is controlled to move according to this information. Experiments conducted within a 2m × 1m area have shown that the maximum error during robot operation is 7.8cm/m, with an average error of 7.0cm/m. The maximum error of the steering angle is 16.6°, with an average error of 7.7°.
With the rapid development of computer vision and image processing, geometric shape recognition has become a highly regarded research area. This study aims to explore a method that combines the FAST feature point recognition algorithm with the Hough transform for fast and accurate recognition of geometric shapes in images, including lines, circles, and rectangles. The FAST feature point recognition algorithm is known for its speed and robustness in effectively detecting key points in images, while the Hough transform allows mapping these key points into parameter space, enabling us to detect and recognize specific geometric shapes. This research focuses on explaining the principles of the method, presenting experimental results, performance evaluations, and discussing future research directions.
To reduce the cost of tracking moving targets by robots and enhance the accuracy of target recognition and tracking, a vision-based tracking robot is designed. The onboard camera captures image data, which is initially processed by the ESP32. After processing, the data is transmitted to a cloud environment via a WiFi module. In the cloud environment, a Siamese neural network algorithm is employed to analyze and process the images, and then it sends motion control commands back to the robot, enabling remote control.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.