Given a graph, the maximum clique problem (MCP) asks for determining a complete subgraph with the largest possible number of vertices. We propose a new exact algorithm, called CliSAT, to solve the MCP to proven optimality. This problem is of fundamental importance in graph theory and combinatorial optimization due to its practical relevance for a wide range of applications. The newly developed exact approach is a combinatorial branch-and-bound algorithm that exploits the state-of-the-art branching scheme enhanced by two new bounding techniques with the goal of reducing the branching tree. The first one is based on graph colouring procedures and partial maximum satisfiability problems arising in the branching scheme. The second one is a filtering phase based on constraint programming and domain propagation techniques. CliSAT is designed for structured MCP instances which are computationally difficult to solve since they are dense and contain many interconnected large cliques. Extensive experiments on hard benchmark instances, as well as new hard instances arising from different applications, show that CliSAT outperforms the state-of-the-art MCP algorithms, in some cases by several orders of magnitude., This publication is part of the R&D project “Cognitive Personal Assistance for Social Environments (ACOGES)”, reference PID2020-113096RB-I00, funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

In this paper, a new multidimensional Takagi–Sugeno (T-S) identification technique is proposed for multivariable nonlinear systems. In this technique, multidimensional membership functions are designed using concepts from solid mechanics. The design of membership functions is carried out in multidimensional space, defining the principal axes from the eigenvectors of the inertia matrix, and it has the characteristic of dividing the space into regions with the same inertia. These regions are analyzed to define the center of gravity for each rule. Illustrative examples of multivariable nonlinear systems, such as a thermal mixing process and a binary distillation column, are selected to evaluate the effectiveness of the proposed method. The proposed method is compared with traditional T-S identification that uses one-dimensional membership functions and shows a reduction in the relative identification error and the algorithm execution time. Additionally, the proposed method prevents rules from being positioned outside the system’s range, thereby avoiding the generation of unnecessary rules., This publication is part of the R&D project “Cognitive Personal Assistance for Social Environments (ACOGES)”, reference PID2020-113096RB-I00, funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

This article belongs to the Special Issue Advances in Robotics and Autonomous Systems., This paper presents an innovative and cost-effective solution for the absolute localization of mobile robots using ultrasound beacons. The proposed system addresses the challenge of precise positioning within a controlled environment by employing Differential Time-of-Flight (ToF) measurements to determine the relative distances between the robot and optimally placed beacons. Unlike other ToF methods that require synchronization pulses, the proposed approach eliminates this requirement, significantly simplifying the setup and reducing system complexity. Furthermore, the system achieves a higher sampling rate than conventional synchronization-based systems, enhancing real-time performance. Detailed analysis and simulation demonstrate the system’s ability to provide accurate and reliable localization. The results highlight the potential for broad application in various robotic environments, offering a robust solution for absolute positioning without complex synchronization strategies. This work underscores the advantages of using ToF measurements with ultrasound beacons and contributes to the ongoing development of efficient and cost-effective robotic localization systems., This paper is part of the R&D project “Cognitive Personal Assistance for Social Environments (ACOGES)”, reference PID2020-113096RB-I00, funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

The increasing interest in developing robots capable of navigating autonomously has led to the necessity of developing robust methods that enable these robots to operate in challenging and dynamic environments. Visual odometry (VO) has emerged in this context as a key technique, offering the possibility of estimating the position of a robot using sequences of onboard cameras. In this paper, a VO algorithm is proposed that achieves sub-pixel precision by combining optical flow and direct methods. This approach uses only a downward-facing, monocular camera, eliminating the need for additional sensors. The experimental results demonstrate the robustness of the developed method across various surfaces, achieving minimal drift errors in calculation., This paper is part of the R&D project “Cognitive Personal Assistance for Social Environments (ACOGES)”, reference PID2020-113096RB-I00, funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

In this work, a new nonlinear control method is proposed, which integrates the Takagi–Sugeno (T–S) fuzzy model with the Principal Component Analysis (PCA) technique.
The approach uses PCA to reduce the system’s dimensionality, minimizing the number of fuzzy rules required in the T–S fuzzy model. This reduction not only simplifies the system variables but also decreases the computational complexity, resulting in a more efficient control with smooth transient responses and zero steady-state error. To validate the performance of this PCA-based approach for both system identification and control, an interconnected double-tank system was employed. The results demonstrate the method’s
capacity to maintain control accuracy while reducing computational load, making it a
promising solution for applications in industrial and engineering systems that require
robust, efficient control mechanisms., This work is part of the R&D project “Cognitive Personal Assistance for Social Environments
(ACOGES)”, reference PID2020-113096RB-I00, funded by MCIN/AEI/10.13039/ 501100011033., Peer reviewed

The increasing complexity of nonlinear multivariable systems poses significant challenges for effective modeling and control. Fuzzy modeling and control typically use fuzzy inference with one-dimensional membership functions. However, the use of multidimensional membership functions can provide significant benefits in optimizing and reducing the computational cost of a fuzzy controller. In this work, we propose the use of fuzzy clustering techniques to adjust and design multidimensional membership functions. These techniques represent a well-developed and comprehensive framework, though they are often disconnected from traditional fuzzy modeling and control methodologies. Thus, this work also seeks to combine fuzzy techniques of different applications with a single ultimate goal, namely, to optimize the modeling and control of nonlinear systems. Our main objective is system identification, modeling, and control using the Takagi–Sugeno method based on one-dimensional and multidimensional membership functions. Moreover, a comparison of various fuzzy clustering techniques for the design of multidimensional membership functions is carried out to demonstrate the effectiveness of the proposed methods in optimizing control performance and reducing computational cost., This publication is part of the R&D project “Cognitive Personal Assistance for Social Environments (ACOGES)”, reference PID2020-113096RB-I00, funded by MCIN/AEI/10.13039/ 501100011033., Peer reviewed

In Choi et al. (2024), the main trends in assistive technologies for healthcare and home environments were analyzed. In that study, assistive technologies were classified into three major groups: physical aid or mobility devices, sensor and monitoring systems, and assistive robots. In an aging society, with people living longer and a lack of medical personnel, there is undoubtedly a growing interest in the development and commercialization of robotic systems that are able to provide support at healthcare facilities and home environments (Bajones et al., 2018; Keroglou et al., 2023; Silvera-Tawil, 2024). At healthcare facilities, robots improve the diagnosis and treatment of many different diseases (mental and physical), and they help professional staff be more efficient, with more time for patients. At home, they assist to prevent accidents, and they have the potential to perform different household tasks, keep the users active with cognitive and physical activities, and raise alarms if needed. Assistive robots designed for therapeutic purposes have proven useful in reducing agitation of elderly people and stress of caregivers (Kolstad et al., 2020).
One major challenge is the robustness and reliability of these robotic systems, which involve complex technologies and software, and usually operate in highly unpredictable environments (Bajones et al., 2018). Progress in fundamental problems such as object detection and recognition (Thalhammer et al., 2024), navigation (De La Puente et al., 2019a), and grasping (De La Puente et al., 2019b) is achieved every year, but more practical and robust solutions are yet to be found. Human–robot interaction (HRI) is also of utmost importance for the acceptance of such robots (Bajones et al., 2019; Fernández-Blanco Martín et al., 2023). All these topics are of course interconnected since advances in one direction may reveal and help address other kinds of issues (Bajones et al., 2018).
This Frontiers Research Topic aims at compiling a general overview of ongoing studies and recent results in these areas, specifically targeted to solve open problems for assistive and service robots in health and home applications., The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported in part by the project “10.13039/501100011033 (ACOGES),” reference PID2020-113096RB-I00, funded by MCIN/AEI/10.13039/501100011033 and by ESF Investing in your future, in part by the RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, under Grant S2018/NMT-4331, in part by Programas de Actividades I+D en la Comunidad de Madrid, and in part by the Structural Funds of the EU., Peer reviewed

This work presents a multi-strategy fuzzy controller (MSFC) based on the Takagi–Sugeno (T–S) model, where the membership functions are based on polar coordinates. Adopting a polar coordinate system helps to reduce the number of rules significantly, especially when fuzzy variables exhibit extreme values. This approach also allows more rules to be assigned in certain directions (angles) than others, while the radius represents the distance to the reference. The MSFC uses three strategies: constant input (CI), where a predefined gain is set in the exterior rules; standard discrete state–space model linear–quadratic regulator (LQR), in the intermediate rules; and an incremental state–space model LQR (INC-LQR) in the central rule. A technique employing fuzzy fusion is suggested to facilitate smooth transitions among the various methods. The results show that the proposed MSFC in polar coordinates has a faster transient response and zero steady-state error compared to other fuzzy controllers, and it requires fewer rules compared to an MSFC based on Cartesian coordinates., This publication is part of the R&Dproject ‘‘Cognitive Personal Assistance for Social Environments (ACOGES),’’ reference PID2020- 113096RB-I00, funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

Datos de sensor láser, RGB-D y odometría obtenidos con un robot Tiago real y en simulación para pruebas de localización híbrida mediante detección de marcas rectangulares.

Este dataset contiene nubes de puntos parciales derivadas de los modelos Bunny, Dragon, Armadillo y Buda, originalmente del reconocido Stanford 3D Scanning Repository. Se incluyen 15 vistas parciales para cada modelo, generadas mediante simulaciones de un sensor tipo ToF (Time-of-Flight) utilizando el software Blender y la extensión Blensor. Estas simulaciones permiten evaluar el rendimiento de algoritmos de registro al enfrentar nubes parciales y su correspondiente nube completa de referencia.

Directorio raíz: dataset
- Subdirectorio bunny: contiene 15 nubes de puntos parciales (scanX.pcd) del modelo Bunny, junto con una imagen asociada de cada escaneo parcial y la nube completa de referencia (source.pcd).
- Subdirectorio dragon: contiene 15 nubes de puntos parciales (scanX.pcd) del modelo Dragon, junto con una imagen asociada de cada escaneo parcial y la nube completa de referencia (source.pcd).
- Subdirectorio armadillo: contiene 15 nubes de puntos parciales (scanX.pcd) del modelo Armadillo, junto con una imagen asociada de cada escaneo parcial y la nube completa de referencia (source.pcd).
- Subdirectorio buda: contiene 15 nubes de puntos parciales (scanX.pcd) del modelo Buda, junto con una imagen asociada de cada escaneo parcial y la nube completa de referencia (source.pcd).

Cada carpeta incluye las nubes parciales y una imagen por cada escaneo parcial, facilitando la visualización de las capturas desde diferentes ángulos. Las nubes de puntos completas (source.pcd) sirven como referencia para el registro en cada subdirectorio.


5. Notas