This paper reports an autonomous cooperative navigation system for robot formations in realistic scenarios. The formation movement control is based on a virtual structure composed by spring-dampers elements, which allows the formation to comply with the environment shape. A different navigation strategy is applied to the leader of the formation and to the rest of robots of the team. The leader plans the trajectories by using a two-level path planner with obstacle avoidance capabilities. The motion of the follower robots is controlled by the virtual structure, which adapts to the environment while the leader is tracked, taking into account the kinodynamic constraints of the vehicles. The system is evaluated in experiments carried out in simulations, some of them made in a realistic and complex urban scenario, and with real robots.

This paper addresses a flexible cooperative navigation system for small robot teams. The members of these groups move in formation, but with enough flexibility to be able to avoid obstacles and adapt to the environment shape. This approach is based on an analysis of the environment on a grid-based model that permits each robot to continuously evaluate the situation in which it is, in order to decide the best of the three developed navigation strategies, one for each situation type. Moreover, by using this model the robot cooperation for a coordinate navigation is improved, allowing each robot to share the observations with the other robots. So the field of view of each robot of the team on which it makes the decisions is enlarged. The system is implemented in a decentralized way so the number of robots is not a main issue, thus being scalable. Eventually, initial formation topology can be modified, in order to comply with the environment while the mission is accomplished. The system and the strategies are evaluated by means of simulations, showing the robustness against possible blocking situations.

Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants’ mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset’s potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems.