This paper proposes a novel non-linear lane selection algorithm for autonomous vehicles based on the Optimality Condition Decomposition algorithm. An initial algorithm based on mixed integer quadratic programming was studied, which proved untreatable due to its computational complexity, leading to similar formulations that do not require such a nature in their variables. As a result, a cascade strategy combining both distributed local planning and decision-making layers is implemented, leading to promising results. The proposed approach can determine paths that consider individual goals and the environment so that agents can collaborate to establish a formation that re-configures online, allowing vehicles to reach their individual goals.

In this paper, a novel distributed set-based localisation algorithm for autonomous vehicles based on zono-topes is proposed. The proposed approach structurally allows embedding data fusion locally and in a vehicle-to-vehicle or infrastructure-to-vehicle manner under treatable computational times. Moreover, the localisation is only based on zonotopic intersections, allowing each individual agent to freely acquire their local estimation by any means, being the single requirement the access of a piece of hardware providing a coupled measurement. The proposed approach is implemented in a distributed manner in a ROS network, mimicking the physical layout of a platoon of autonomous vehicles.

Teaching Cyber-Physical Systems (CPS) can be challenging due to the involvement of diverse, complex, and expensive devices. During the past year, Tecnologico de Monterrey together with Manchester Robotics Ltd. successfully implemented a teaching strategy for CPS under its model TEC21 on the way to graduate high-level students, and ready to join the workforce. Theoretical lectures combined with hands-on practices allowed students to solve a real problem using off-the-shelf mobile robots and ROS as the software platform. After implementing this teaching-learning strategy, significant improvements have been observed in students' learning outcomes. They have had the opportunity to work collaboratively in teams, develop their research skills, enhance their programming abilities, and gain a deeper understanding of mobile and manipulator arm robotics through practical implementation in the laboratory. The planning, methodology, and solutions performed by students are used to showcase the results of the teaching strategy and its outcomes.

Autonomous vehicles require sophisticated planning algorithms to navigate safely and efficiently in complex environments. Traditional centralised planning approaches face scalability challenges, especially as the number of agents increases. Distributed planning strategies have emerged as a promising solution to overcome these limitations. This paper presents a novel approach to distributed set-based planning for autonomous vehicles. Each vehicle can autonomously plan its path by lever-aging set-based representations of possible trajectories while considering uncertainty and dynamic environment changes. The proposed method enables vehicles to collaborate efficiently while maintaining decentralised decision-making capabilities, thus enhancing scalability and robustness. Furthermore, safe trajectories are derived by contemplating system uncertainties through set theory.

This paper presents a platform for measuring, evaluating and modeling the energy efficiency aspects of an O-RAN 5G wireless network. We describe our open-source based O-RAN testbed which includes both bare-metal and Kubernetes network functions, in addition to physical network components. We focus on measuring power consumption of servers and workloads of cloudified and virtualized network functions. We show that a combination of different power measurements can be used successfully to achieve the accuracy and granularity required for energy efficiency measurement, evaluation and modeling.

This article proposes an optimal-distributed control protocol for multivehicle systems with an unknown switching communication graph. The optimal-distributed control problem is formulated to differential graphical games, and the Pareto optimum to multiplayer games is sought based on the viability theory and reinforcement learning techniques. The viability theory characterizes the controllability of a wide range of constrained nonlinear systems; and the viability kernel and the capture basin are the pillars of the viability theory. The capture basin is the set of all initial states, in which there exist control strategies that enable the states to reach the target in finite time while remaining inside a set before reaching the target. In this regard, the feasible learning region is characterized by the reinforcement learner. In addition, the approximation of the capture basin provides the learner with prior knowledge. Unlike the existing works that employ the viability theory to solve control problems with only one agent and differential games with only two players, the viability theory, in this article, is utilized to solve multiagent control problems and multiplayer differential games. The distributed control law is composed of two parts: 1) the approximation of the capture basin and 2) reinforcement learning, which are computed offline and online, respectively. The convergence properties of the parameters' estimation errors in reinforcement learning are proved, and the convergence of the control policy to the Pareto optimum of the differential graphical game is discussed. The guaranteed approximation results of the capture basin are provided and the simulation results of the differential graphical game are provided for multivehicle systems with the proposed distributed control policy.

A stereo vision based autonomous navigation method for 3-DOF systems is presented in this paper. It is able to tackle the learning and recognition problem of generic scenes in an unstructured environment, providing motion-planning capability to control all the 3 DOFs of a robotic system. In this method, 3 spatial constraints are generated from a single visual recognition to estimate the robot pose. A feedback strategy is utilised for robot motion control, without the necessity of knowing any explicit distance information of the environment. The performance of the proposed method is evaluated in a novel wire-frame simulation environment, under the perturbation of multiple uncertainty sources. Autonomous navigation is achieved with good accuracy in the simulation environment, while preserving high robustness to all the uncertainty sources.

A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.