A ROS-Based Data-Driven Motion Self-Recognition System Using Deep-Learning Convolutional Neural Networks in a Military Unmanned Ground Vehicle

The ability to recognize motions is an important feature in cutting-edge robotics or autonomous systems, such as self-driving cars, humanoid robots, and human-robot interactions, resulting in improved safety and efficiency. Addressing this critical issue, we introduce a simple framework leveraging the benefits of the normalized real-time network traffic data of the middleware ROS platform formulated in the form of RGB or grayscale images to train the Convolutional Neural Network (CNN) system in order to learn the motion pattern of the robot. For our experimental platform, we employ the GVR-BOT Unmanned Ground Vehicle (UGV), developed by the U.S. Army Combat Capabilities Development Command (CCDC), Ground Vehicle Systems Center (GVSC). We rigorously study the performance of our motion recognition system under several different lengths of data (epochs). In addition, we compare the relative merits of our proposed system with respect to the performance of the well-known ‘Bag-of-Features’ (BoFs) detection algorithm widely implemented in computer vision. Our research indicates the efficacy of the proposed motion recognition system as we can achieve a reasonably high detection accuracy ≥ 0.97 within a minimum detection time of two epochs highlighting its real-time benefits. Overall, our recognition system can also achieve superior detection performance compared to the efficacy of the BoFs algorithm.