There is a growing interest in applying intelligent technologies to assistant robots. These robots should have a number of characteristics such as autonomy, easy reconfiguration, robust perception systems and they should be oriented towards close interaction with humans. In this paper we present an automatic hotel assistant system based on a series of mobile platforms that interact with guests and service personnel to help them in different tasks. These tasks include bringing small items to customers, showing them different points of interest in the hotel, accompanying the guests to their rooms and providing them with general information. Each robot can also autonomously handle some daily scheduled tasks. Apart from user-initiated and scheduled tasks, the robots can also perform tasks based on events triggered by the building's automation system (BAS). The robots and the BAS are connected to a central server via a local area network. The system was developed with the Robotics Integrated Development Environment (RIDE) and was tested intensively in different environments.

Automated Driving Systems (ADSs) require robust and scalable control systems in order to achieve a safe, efficient and comfortable driving experience. Most global planners for autonomous vehicles provide as output a sequence of waypoints to be followed. This paper proposes a modular and scalable waypoint tracking controller for Robot Operating System (ROS)-based autonomous guided vehicles. The proposed controller performs a smooth interpolation of the waypoints and uses optimal control techniques to ensure robust trajectory tracking even at high speeds in urban environments (up to 50 km/h). The delays in the localization system and actuators are compensated in the control loop to stabilize the system. Forward velocity is adapted to path characteristics using a velocity profiler. The controller has been implemented as an ROS package providing scalability and exportability to the system in order to be used with a wide variety of simulators and real vehicles. We show the results of this controller using the novel and hyper realistic CARLA Simulator and carrying out a comparison with other standard and state-of-art trajectory tracking controllers.

Nowadays, one of the most important objectives in health research is the improvement of the living conditions and well-being of the elderly, especially those who live alone. These people may experience undesired or dangerous situations in their daily life at home due to physical, sensorial or cognitive limitations, such as forgetting their medication or wrong eating habits. This work focuses on the development of a database in a home, through non-intrusive technology, where several users are residing by combining: a set of non-intrusive sensors which captures events that occur in the house, a positioning system through triangulation using beacons and a system for monitoring the user’s state through activity wristbands. Two months of uninterrupted measurements were obtained on the daily habits of 2 people who live with a pet and receive sporadic visits, in which 18 different types of activities were labelled. In order to validate the data, a system for the real-time recognition of the activities carried out by these residents was developed using different current Deep Learning (DL) techniques based on neural networks, such as Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM) or Gated Recurrent Unit networks (GRU). A personalised prediction model was developed for each user, resulting in hit rates ranging from 88.29% to 90.91%. Finally, a data sharing algorithm has been developed to improve the generalisability of the model and to avoid overtraining the neural network.

Urban complex scenarios are the most challenging situations in the field of Autonomous Driving (AD). In that sense, an AD pipeline should be tested in countless environments and scenarios, escalating the cost and development time exponentially with a physical approach. In this paper we present a validation of our fully-autonomous driving architecture using the NHTSA (National Highway Traffic Safety Administration) protocol in the CARLA simulator, focusing on the analysis of our decision-making module, based on Hierarchical Interpreted Binary Petri Nets (HIBPN). First, the paper states the importance of using hyper-realistic simulators, as a preliminary help to real test, as well as an appropriate design of the traffic scenarios as the two current keys to build safe and robust AD technology. Second, our pipeline is introduced, which exploits the concepts of standard communication in robotics using the Robot Operating System (ROS) and the Docker approach to provide the system with isolation, flexibility and portability, describing the main modules and approaches to perform the navigation. Third, the CARLA simulator is described, outlining the steps carried out to merge our architecture with the simulator and the advantages to create ad-hoc driving scenarios for use cases validation instead of just modular evaluation. Finally, the architecture is validated using some challenging driving scenarios such as Pedestrian Crossing, Stop, Adaptive Cruise Control (ACC) and Unexpected Pedestrian. Some qualitative (video files: Simulation Use Cases) and quantitative (linear velocity and trajectory splitted in the corresponding HIBPN states) results are presented for each use case, as well as an analysis of the temporal graphs associated to the Vulnerable Road Users (VRU) cases, validating our architecture in simulation as a preliminary stage before implementing it in our real autonomous electric car.