Abstract
Frequently carrying high loads and performing repetitive tasks compromises the ergonomics of individuals, a recurrent scenario in hospital environments. In this paper, we design a logistic planner of a fleet of autonomous mobile robots for the automation of transporting trolleys around the hospital, which is independent of the space configuration, and robust to loss of network and deadlocks. Our robotic solution has an innovative gripping system capable of grasping and pulling non-modified standard trolleys just by coupling a plate. Robots are able to navigate autonomously, to avoid obstacles assuring the safety of operators, to identify and dock a trolley, to access charging stations and elevators, and to communicate with the latter. An interface was built allowing users to command the robots through a web server. It is shown how the proposed methodology behaves in experiments conducted at the Faculty of Engineering of the University of Porto and Braga's Hospital.

Abstract
Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach has to find a solution inside a search space that contains every possible combination of robots and goals. This leads to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a new way to solve this kind of tasks efficiently. Previous works on MAP have proposed to factorize the problem to decrease the planning effort i.e. dividing the goals among the agents (robots). However, these techniques do not scale when the number of agents and goals grow. Also, in most real world scenarios with big maps, goals might not be reached by every robot so it has a computational cost associated. In this paper we propose a combination of robotics and planning techniques to alleviate and boost the computation of the goal assignment process. We use Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. Thus, specific information can be extracted to know which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to a multi-agent planning algorithm, the goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.

Abstract
Purpose In the vast majority of the individual robot installations, the robot arm is just one piece of a complex puzzle of components, such as grippers, jigs or external axis, that together compose an industrial robotic cell. The success of such installations is very dependent not only on the selection of such components but also on the layout and design of the final robotic cell, which are the main tasks of the system integrators. Consequently, successful robot installations are often empirical tasks owing to the high number of experimental combinations that could lead to exhaustive and time-consuming testing approaches. Design/methodology/approach A newly developed optimized technique to deal with automatic planning and design of robotic systems is proposed and tested in this paper. Findings The application of a genetic-based algorithm achieved optimal results in short time frames and improved the design of robotic work cells. Here, the authors show that a multi-layer optimization approach, which can be validated using a robotic tool, is able to help with the design of robotic systems. Originality/value To date, robotic solutions lack flexibility to cope with the demanding industrial environments. The results presented here formalize a new flexible and modular approach, which can provide optimal solutions throughout the different stages of design and execution control of any work cell.

Abstract
Nowadays, industrial robots are still commonly programmed using essentially off-line tools, such as is the case of structured languages or simulated environments. This is a very time-consuming process, which necessarily requires the presence of an experienced programmer with technical knowledge of the set-up to be used, as well as a concept and a complete definition of the details associated with the operations. Moreover, considering some industrial applications such as coating, painting, and polishing, which commonly require the presence of highly skilled shop floor operators, the translation of this human craftsmanship into robot language using the available programming tools is still a very difficult task. In this regard, this paper presents a programming by demonstration solution, that allows a skilled shop floor operator to directly teach the industrial robot. The proposed system is based on the 6D Mimic innovative solution, endowed with an IMU sensor as to enable the system to tolerate temporary occlusions of the 6D Marker. Results show that, in the event of an occlusion, a reliable and highly accurate pose estimation is achieved using the IMU data. Furthermore, the selected IMU was a low-cost model, to not severely increase the 6D Mimic cost, despite lowering the quality of the readings. Even in these conditions, the developed algorithm was able to produce high-quality estimations during short time occlusions.

Abstract
The transport of patients from the inpatient service to the operating room is a recurrent task in the hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented the design of a driverless wheelchair under development capable of providing an on-demand mobility service to hospitals. The proposed wheelchair can receive transportation requests directly from the hospital information management system, pick-up patients at their beds, navigate autonomously through different floors, avoid obstacles, communicate with elevators, and drop patients off at the designated destination.

Abstract
A robots simulation system is a basis need for any robotics application. With it, developers teams of robots can test their algorithms and make initial calibrations without risk of damage to the real robots, assuring safety. However, build these simulation environments is usually a time-consuming work, and when considering robot fleets, the simulation reveals to be computing expensive. With it, developers building teams of robots can test their algorithms and make initial calibrations without risk of damage to the real robots, assuring safety. An omnidirectional robot from the 5DPO robotics soccer team served to test this approach. The modeling issue was divided into two steps: modeling the motor's non-linear features and modeling the general behavior of the robot. A proper fitting of the robot was reached, considering the velocity robot's response.