Bibliometric Mapping of the Mathematics Role in Daily Life

This study aims to analyze the bibliometric mapping the mathematics' role in everyday life. This study uses a qualitative method by comparing the information obtained from a Google Scholer search. This method is entirely based on the hunt for journal articles developed through bibliometric analysis. Data was collected using Publish or Perish (PoP) and visualized using VOSviewer. In this study, researchers obtained metadata from as many as 995 articles of applied mathematics in everyday life published on April 15, 2022, using a database of 1000 articles from 1926 to 2022. The results and discussion of the bibliometric analysis that has been carried out show that this research topic is still possible. Other researchers for the next period because the data that has been analyzed is still categorized as feasible, and there are still few studies related to bibliometric mapping of the role of mathematics in everyday life. Then suggestions for further research if researchers are interested in choosing a research topic on bibliometric mapping of the role of mathematics in everyday life, namely by connecting various other activities in society and combining it with the progress that developed during the research period so that the data obtained is always up-to-date to evaluate the effectiveness of mathematics in daily lif

Urban Swarms: A new approach for autonomous waste management

Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past years and they are expected to become one of the main driving factors for innovation in the field of robotics. The research presented in this paper explores the feasibility of a swarm robotics system in an urban environment. By using bio-inspired foraging methods such as multi-place foraging and stigmergy-based navigation, a swarm of robots is able to improve the efficiency and autonomy of the urban waste management system in a realistic scenario. To achieve this, a diverse set of simulation experiments was conducted using real-world GIS data and implementing different garbage collection scenarios driven by robot swarms. Results presented in this research show that the proposed system outperforms current approaches. Moreover, results not only show the efficiency of our solution, but also give insights about how to design and customize these systems.Comment: Manuscript accepted for publication in IEEE ICRA 201

Editorial: Translational research in medical robotics—challenges and opportunities

In the last few decades, emerging medical technologies and the growing number of commercial robotic platforms have supported diagnosis and treatment of both acute and chronic diseases of the human body, improving the clinical outcome, reducing trauma, shortening the patient recovery time, and increasing postoperative survival rates (Troccaz et al., 2019). Medical robots–including surgical robots, rehabilitation and assistive robots, and hospital automation robots–with improved safety, efficacy and reduced costs, robotic platforms will soon approach a tipping point, moving beyond early adopters to become part of the mainstream clinical practice, defining the future of smart hospitals and home-based patient care. Surgical robots promise to enhance minimally invasive surgery with precise instrument control, intuitive hand-eye coordination, and superior dexterity within tight spaces (Dupont et al., 2021). Rehabilitation robotics facilitates robot-assisted therapy and automated recovery training (Xue et al., 2021). Assistive robots aid individuals with physical limitations, either enhancing or compensating for functions, promoting independence, and lessening the burden on caregivers (Trainum et al., 2023). Additionally, robotic systems can automate hospital operations, spanning service robots aiding clinicians to robots in labs for high-throughput testing (Kwon et al., 2022). These technologies aim to revolutionize healthcare, offering improved patient care and operational efficiency

From Psychological Intention Recognition Theories to Adaptive Theory of Mind for Robots

Progress in robots' application to everyday scenarios has increased the interest in human-robot interaction (HRI) research. However, robots' limited social skills are associated with decreased humans' positive attitude during HRI. Here, we put forward the idea of developing adaptive Theory of Mind (ToM) model-based systems for social robotics, able to deal with new situations and interact with different users in new tasks. Therefore, we grouped current research from developmental psychology debating the computational processes underlying ToM for HRI strategy development. Defining a model describing adaptive ToM processes may in fact aid the development of adaptive robotic architectures for more flexible and successful HRI. Finally, we hope with this report to both further promote the cross-talk between the fields of developmental psychology and robotics and inspire future investigations in this direction

Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics

Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics

Development of an anthropomorphic mobile manipulator with human, machine and environment interaction

An anthropomorphic mobile manipulator robot (CHARMIE) is being developed by the University of Minho's Automation and Robotics Laboratory (LAR). The robot gathers sensorial information and processes using neural networks, actuating in real time. The robot's two arms allow object and machine interaction. Its anthropomorphic structure is advantageous since machines are designed and optimized for human interaction. Sound output allows it to relay information to workers and provide feedback. Allying these features with communication with a database or remote operator results in establishment of a bridge between the physical environment and virtual domain. The goal is an increase in information flow and accessibility. This paper presents the current state of the project, intended features and how it can contribute to the development of Industry 4.0. Focus is given to already finished work, detailing the methodology used for two of the robot's subsystems: locomotion system; lower limbs of the robot.- This project has been supported by the ALGORITMI Research Centre of University of Minho's School of Engineering