The purpose of this paper is to present the results of the analysis of the application of Deep Learning in the railway domain with a particular focus on a train stop operation. The paper proposes an approach consisting of ...

Autonomous vehicles’ presence is becoming a reality in everyday life, with autonomous driving cars on the road, GOA3-GOA4 trains in the railway domain, or automated guided vehicles in the industrial domain. These autonomous ...

Training robots in the real-world using reinforcement learning is both expensive and risky. World Models—a simulated environment that mirrors real-world conditions—have been proved to offer an alternative to real-world ...

As the investment on a dedicated quality control stations is not desirable for limited production batches. In general, those systems result in very optimised systems and the lack of flexibility since they are designed for ...

The introduction of collaborative robots in industrial environments reinforces the need to provide these robots with better cognition to accomplish their tasks while fostering worker safety without entering into safety ...

Drones, mobile robots, and autonomous vehicles use Visual Odometry (VO) to move around complex environments. ORB-SLAM or deep learning-based approaches like DF-VO are two of the state-of-the-art technics for monocular VO. ...