Deep-Learning-Based Floor Path Model for Route Tracking of Autonomous Vehicles

Abstract

Real-time route tracking is an important research topic for autonomous vehicles used in industrial facilities. Traditional methods such as copper line tracking on the ground, wireless guidance systems, and laser systems are still used in route tracking. In this study, a deep-learning-based floor path model for route tracking of autonomous vehicles is proposed. A deep-learning floor path model and algorithm have been developed for highly accurate route tracking, which avoids collisions of vehicles and follows the shortest route to reach the destination. The floor path model consists of markers. Routes in the floor path model are created by using these markers. The floor path model is transmitted to autonomous vehicles as a vector by a central server. The server dispatches the target marker address to the vehicle to move. The vehicle calculates all possible routes to this address and chooses the shortest one. Marker images on the selected route are processed using image processing and classified with a pre-trained deep-CNN model. If the classified image and the image on the selected route are the same, the vehicle proceeds toward its destination. While the vehicle moves on the route, it sends the last classified marker to the server. Other autonomous vehicles use this marker to determine the location of this vehicle. Other vehicles on the route wait to avoid a collision. As a result of the experimental studies we have carried out, the route tracking of the vehicles has been successfully achieved.