The increasing isolation of the elderly both in their own homes and in care homes has made the problem of caring for elderly people who live alone an urgent priority. This article presents a proposed design for a heterogeneous multirobot system consisting of (i) a small mobile robot to monitor the well-being of elderly people who live alone and suggest activities to keep them positive and active and (ii) a domestic mobile manipulating robot that helps to perform household tasks. The entire system is integrated in an automated home environment (AAL), which also includes a set of low-cost automation sensors, a medical monitoring bracelet and an Android application to propose emotional coaching activities to the person who lives alone. The heterogeneous system uses ROS, IoT technologies, such as Node-RED, and the Home Assistant Platform. Both platforms with the home automation system have been tested over a long period of time and integrated in a real test environment, with good results. The semantic segmentation of the navigation and planning environment in the mobile manipulator for navigation and movement in the manipulation area facilitated the tasks of the later planners. Results about the interactions of users with the applications are presented and the use of artificial intelligence to predict mood is discussed. The experiments support the conclusion that the assistance robot correctly proposes activities, such as calling a relative, exercising, etc., during the day, according to the user’s detected emotional state, making this is an innovative proposal aimed at empowering the elderly so that they can be autonomous in their homes and have a good quality of life.

One of the major problems of today's society is the rapid aging of its population. Life expectancy is increasing, but the quality of life is not. Faced with the growing number of people who require cognitive or physical assistance, new technological tools are emerging to help them. In this article, we present the ADAM robot, a new robot designed for domestic physical assistance. It mainly consists of a mobile base, two arms with grippers and vision systems. All this allows the performance of physical tasks that require navigation and manipulation of the environment. Among ADAM's features are its modularity, its adaptability to indoor environments and its versatility to function as an experimental platform and for service applications. In addition, it is designed to work respecting the user's personal space and is collaborative, so it can learn from experiences taught by them. We present the design of the robot as well as examples of use in domestic environments both alone and in collaboration with other domestic platforms, demonstrating its potential.

The advancement of robotics in recent years has driven the growth of robotic applications for more complex tasks requiring manipulation capabilities. Recent works have focused on adapting learning methods to manipulation applications which are stochastic and may not converge. In this paper, a kinesthetic learning method based on fast marching square is presented. This method poses great advantages such as ensuring convergence and is based on learning from the experience of a human demonstrator. For this purpose, the demonstrator teaches paths by physically guiding one of the UR3 arms of a mobile manipulator. After this first phase, the fast marching Learning method is used to make the robot learn from this experience. As a novelty, an auto-learning functionality is presented, which provides the kinesthetic learning algorithm with an exploration capacity. The base of this algorithm is not only using the information provided by the taught trajectories, but also expanding its ability in order to explore unknown states of the environment. The effectiveness of the proposed method has been evaluated through simulations in 2D and 3D environments and in a real mobile manipulator. The learning process is analyzed with other 2D learning approaches using the LASA dataset and it is tested in complex 3D scenarios with different obstacles, proving its effectiveness.

Nowadays, most mobile robot applications use two-dimensional LiDAR for indoor mapping, navigation, and low-level scene segmentation. However, single data type maps are not enough in a six degree of freedom world. Multi-LiDAR sensor fusion increments the capability of robots to map on different levels the surrounding environment. It exploits the benefits of several data types, counteracting the cons of each of the sensors. This research introduces several techniques to achieve mapping and navigation through indoor environments. First, a scan matching algorithm based on ICP with distance threshold association counter is used as a multi-objective-like fitness function. Then, with Harmony Search, results are optimized without any previous initial guess or odometry. A global map is then built during SLAM, reducing the accumulated error and demonstrating better results than solo odometry LiDAR matching. As a novelty, both algorithms are implemented in 2D and 3D mapping, overlapping the resulting maps to fuse geometrical information at different heights. Finally, a room segmentation procedure is proposed by analyzing this information, avoiding occlusions that appear in 2D maps, and proving the benefits by implementing a door recognition system. Experiments are conducted in both simulated and real scenarios, proving the performance of the proposed algorithms.

The presence of mobile robots in indoor environments is becoming increasingly significant. While there are algorithms such as SLAM that allow robots to navigate these environments, they do not take into account structural elements of modern buildings such as large glass areas. Due to the use of light-based sensors such as laser scans, these elements are only visible at few incident angles, so they are seen as noise by SLAM algorithms. In this work, reflective surfaces are detected by analyzing laser scan intensity values in order to build a reflection-aware map for safe indoor robot navigation. The main novelty of the proposed method is the usage of a single sensor to gather environment information and a single parameter to determine where reflective zones are. Experiments are carried out in multiple complex real scenarios, quantitatively testing how well the method performs against state of the art works and against factors such as changes in robot orientation, a large number of reflective areas or changes in illumination.

Mobile robot navigation has been studied for a long time, and it is nowadays widely used in multiple applications. However, it is traditionally focused on two-dimensional geometric characteristics of the environments. There are situations in which robots need to share space with people, so additional aspects, such as social distancing, need to be considered. In this work, an approach for social navigation is presented. A multi-layer model of the environment containing geometric and topological characteristics is built based on the fusion of multiple sensor information. This is later used for navigating the environment considering social distancing from individuals and groups of people. The main novelty is combining fast marching square for path planning and navigation with Gaussian models to represent people. This combination allows to create a continuous representation of the environment from which smooth paths can be extracted and modified according to dynamically captured data. Results prove the practical application of the method on an assistive robot for navigating indoor scenarios, including a behavior for crossing narrow passages. People are efficiently detected and modeled to assure their comfort when robots are around.