Blood pressure (BP) is a critical biomarker for cardiovascular health. Several proposals of surrogate measurements and methods, such as Pulse Arrival Time (PAT), aim to provide non-invasive cuffless alternatives to traditional BP monitoring. However, the reliability of PAT-based models remains uncertain due to calibration challenges and the complex nature of BP regulation. This study investigated the performance of PAT-based models using portable electrocardiograms and photoplethysmography to estimate systolic (SBP) and diastolic (DBP) BP in ambulatory settings with 307 participants from the AuroraBP dataset. The best-performing PAT-based models achieved a mean absolute error (MAE) of$\mathbf{1 4. 2 3 ~ m m H g}$for SBP and 9.67 mmHg for DBP estimation. Both models failed to meet current international standards for BP monitoring. Additionally, the baseline models outperformed the best PAT-based model for SBP (MAE:$\mathbf{1 3. 0 4 ~ m m H g}$) and achieved comparable performance to the top PAT-based model for DBP (MAE: 9.67). Despite promising results in previous studies, our findings highlight the poor performance of PAT-based models, particularly in dynamic and ambulatory conditions. These results highlight the limitation of multi-signal time-based models, which fail to generalize in complex dynamic situations. More advanced approaches that succeed in capturing the nonlinearities of BP variations and provide reliable, continuous BP monitoring in real-life settings are necessary.

This study evaluates the impact of immersive (Head-Mounted Display, HMD) and non-immersive (2D conventional monitor) visual displays on user performance in a haptic-enabled virtual reality (VR) platform. Fourteen healthy participants completed three interactive tasks—Pick and place (P&P), Canvas, and Box and Block Test (BBT)—under both display conditions. A counterbalanced design was employed to minimize order effects. Statistical analysis revealed that display type significantly influenced performance in the P&P task ($\mathbf{p}=\mathbf{0. 0 0 3}$), where users performed better with the HMD, likely due to enhanced depth perception. However, no significant differences were observed in Canvas ($\mathbf{p}=0.258$) or BBT ($\mathbf{p}=0.634$), where depth perception plays a less critical role. Despite its potential effectiveness for rehabilitation, the platform does have limitations, such as the constrained workspace of the Touch${ }^{T M}$haptic device. Future studies should involve clinical populations to evaluate its therapeutic potential. These findings underscore the influence of display modality on VR-based tasks and its implications for humancomputer interaction, particularly in rehabilitation contexts.

This paper describes the mechanical design and a cognition system for a novel concept-of-camera robotic assistant. The system combines the advantages of intra-abdominal devices and autonomous camera navigation. The robotic assistant is composed of a magnetic intra-abdominal camera robot with two internal cable-driven degrees of freedom and an external robot that handles an external magnet. The intelligence of the robot is implemented in a cognitive architecture based on a long-term memory that stores the robot's knowledge and learning capabilities to improve the robot's behavior. The navigation strategy combines a reactive control based on instrument tracking and a proactive control based on predefined behaviors, depending on the actual state of the task. The robot's learning capabilities include a semantic customization, to adapt the camera's behavior to the surgeon's preferences, and reinforcement learning, to improve the camera navigation strategy. This paper details both the hardware implementation of the system and the software implementation in a robotic operating system architecture. The cognition system and the performance of the cable-driven mechanism have been validated with a set of in vitro experiments. Moreover, the camera robot has been evaluated through an in-vivo experiment in a pig.

Robotics constitutes a multidisciplinary area, congregating knowledge from different scientific domains. The learning of robotic systems requires the acquisition of multidisciplinary scientific bases, and high integration and synthesis abilities, which is not an easy task. This paper describes the implementation of a lifelong course that aims to provide a global insight on robotics field, introducing the concepts and technologies for different domain applications, namely industrial robotics, autonomous mobile robotics and robotics applied in medicine. This is accomplished in an international framework where individual knowledge and experiences will be confronted in a multidisciplinary level and intercultural environment.

This paper deals with sensor-based path planning and exploration for robots (with non-trivial geometry and kinematics, such as a manipulator arm) moving in unknown environments. The manipulator (with many degrees of freedom) is assumed to be equipped with two sensing modalities: (i) a large number of proximity sensors mounted on its body (the "skin" sensor) and (ii) a range sensor mounted on its wrist (an "eye" sensor). The task for the robot is to move around and explore its (initially) unknown environment while avoiding collisions with obstacles that are (initially) unknown to the robot. We present a sensor-based planning algorithm that utilizes information from these two drastically different sensing modalities, the "eye" and the "skin". Planar simulations show that combining use of eye and skin sensors leads to more efficient and more extensive exploration than with eye sensing modality alone.

This paper presents a new method for online path planning for robotic arms in dynamic environments. Most online path planning methods are based on local algorithms that end up being inefficient due to their lack of global information (local minima problems). The method presented in this paper avoids local minima by using a two stage framework. The robots react to dynamic environments using a local and reactive planning method restricted to a subset of its configuration space. Since the subset has few degrees of freedom the computational cost of the online stage is very low. An off-line stage chooses the subset of the configuration space that minimizes the probability of blockades and inefficient motions.

Three-dimensional modeling of rotations and translations in kinematic calibration is most commonly performed using homogeneous transformations. The paper presents a technique for the calibration of robots based on the quaternion-vector pair approach for the identification of geometrical errors.

In a multirobot system (MRS) several robots can operate simultaneously on the same product. We propose the use of timed Petri nets to evaluate the efficiency of the system. The throughput and utilization of robots allows us to evaluate the benefits of the MRS. A methodology has been developed that, given a set of robots and platforms, allows what the optimum operation mode is, under different conditions, to be decided.

In a multirobot system (MRS) several robots can operate simultaneously on the same product, and allow the accomplishment of tasks that a single robot could not approach due to restrictions associated to the product and/or the robot. We propose a MRS consisting of three robots and four different working platforms, that allows the characteristics of joint operation to be maintained even if a robot is out of service. In order to make a detailed analysis of the operating characteristics of the MRS we propose to use timed Petri nets (GSPN and DSPN), than allow the efficiency of the system to be evaluated. This paper analyses the influence of the robot maintenance and failures on the throughput of an MRS, under different conditions of operative configuration, number of subtasks, degree of interference, operation mode on the product, etc.

Presents a distributed planning and control architecture for autonomous multi-manipulator systems (MMS). The control architecture is implemented using an agent-based approach. A team of distributed and autonomous agents is deployed to model the flexible assembly system in such a way that the agents negotiate, collaborate, and cooperate to achieve the goals of assembly tasks. The main focus of the paper is on assembly task allocation and assembly task execution. We describe the agent models and communication mechanism, and explain how they handle complex interactions among agents. A distributed trajectory planning approach based on artificial potential fields is also presented. Experimental results show that our multi-agent planning and control framework is suitable for flexible robotic assembly tasks. Our approach addresses the issues of flexibility, scalability, reconfigurability, and fault-tolerance. We anticipate that the same approach can be applied to other flexible manufacturing environments.

This work describes a path planning method applied to a three robot system. Trajectories are optimized to allow minimum time performance of the overall system. Mathematical programming methods with a global approach are used.