Abstract
In the present paper, we propose a control approach to perform docking of an autonomous surface vehicle (ASV) while avoiding surrounding obstacles. This control architecture is composed of two sequential controllers. The first outputs a feasible trajectory between the vessel's initial and target state while avoiding obstacles. This trajectory also minimizes the vehicle velocity while performing the maneuvers to increase the safety of onboard passengers. The second controller performs trajectory tracking while accounting for the actuator's physical limits (extreme actuation values and the rate of change). The method's performance is tested on simulation, as it enables a reliable ground truth method to validate the control architecture proposed.

Abstract
In the field of intelligent autonomous robots, integrating optimization techniques with classical control theory methods for mobile robot control is an increasingly prominent area of research. The combination enhances robots' ability to perform their tasks more efficiently, reliably, and safely. This paper addresses the development of a path and motion planning framework for omnidirectional robots, leveraging B-Splines and Trajectory Tracking with Model Predictive Control. The proposed framework is evaluated through software-in-the-loop tests using two distinct dynamical models and sets of hyperparameters. Final validation is conducted by implementing the framework within a ROS environment and performing field tests on a robotic platform. The results demonstrate that the robot can reliably track trajectories at its actuation limits, and the proposed framework enables the robot to increase its velocity up to 50% when compared to a PID path-following controller.

Abstract
Companies are putting more emphasis on the customer experience, associating services with their physical products with the help of emerging technologies. At the same time, several actors participating in research and innovation projects, such as universities, research institutes, and service providers, are involved in the value co-creation process. Thus, this study describes how digitalisation and servitisation in the context of participation in research and innovation projects contributed to innovation in industrial companies’ business model (BM). Qualitative exploratory research took place, collecting data through interviews with twelve key actors in industrial companies. The interviewees were professionals in management and R&D areas and founders from nine European countries who participated in six research and innovation European projects. The exchange of knowledge and experiences between the different actors of the innovation ecosystem influences this. From a practical point of view, research provides managers of industrial companies with the best practices and describes the main changes observed in the BM Canvas. This study also contributes to categorising companies in terms of their service maturity by associating factors other than servitisation, such as digitalisation and the actors of the research and innovation projects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
This paper investigates the real-time tracking capabilities of the Long Baseline (LBL) acoustic tracking system, developed by INESC TEC, for near-shore monitoring applications with human divers. The study aims to determine the system's suitability for environmental monitoring under challenging conditions, such as very shallow water and areas close to the coastline, where acoustic multipath effects are prevalent and can cause significant measurement errors. To mitigate these errors, a two-stage outlier rejection algorithm was implemented. The algorithm's performance was evaluated by comparing the measurement data at each stage and assessing the reduction in erroneous readings. The tracking performance was evaluated based on accuracy and repeatability. Two dives were performed, during which positions marked using the developed system were compared with GNSS data. © 2024 IEEE.

Abstract
This paper presents an interactive game designed to improve users’ experience related to driving behaviour, as well as to provide decision support in this context. This paper explores machine learning (ML) methods to enhance the decision-making and automation in a gaming environment. It examines various ML strategies, including supervised, unsupervised, and Reinforcement Learning (RL), emphasizing RL’s effectiveness in interactive environments and its combination with Deep Learning, culminating in Deep Reinforcement Learning (DRL) for intricate decision-making processes. By leveraging these concepts, a practical application considering a gaming scenario is presented, which replicates vehicle behaviour simulations from real-world driving scenarios. Ultimately, the objective of this research is to contribute to the ML and artificial intelligence (AI) fields by introducing methods that could transform the way player agents adapt and interact with the environment and other agents decisions, leading to more authentic and fluid gaming experiences. Additionally, by considering recreational and serious games as case studies, this work aims to demonstrate the versatility of these methods, providing a rich, dynamic environment for testing the adaptability and responsiveness, while can also offer a context for applying these advancements to simulate and solve real-world problems in the complex and dynamic domain of mobility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract