Visual-Aided Obstacle Climbing by Modular Snake Robot.

Snake robots, also known as apodal robots, are among the most common and versatile modular robots. Primarily due to their ability to move in different patterns, they can evolve in scenarios with several constraints, some of them hardly accessible to other robot configurations. This paper deals with a specific environment constraint where the robot needs to climb a prismatic obstacle, similar to a step. The objective is to carry out simulations of this function, before implementing it in the physical model. To this end, we propose two different algorithms, parameterized by the obstacle dimensions determined by image processing, and both are evaluated in simulated experiments. The results show that both algorithms are viable for testing in real robots, although more complex scenarios still need to be further studied.

Quadruped Robot Control: An Approach Using Body Planar Motion Control, Legs Impedance Control and Bézier Curves.

In robotics, the ability of quadruped robots to perform tasks in industrial, mining, and disaster environments has already been demonstrated. To ensure the safe execution of tasks by the robot, meticulous planning of its foot placements and precise leg control are crucial. Traditional motion planning and control methods for quadruped robots often rely on complex models of both the robot itself and its surrounding environment. Establishing these models can be challenging due to their nonlinear nature, often entailing significant computational resources. However, a more simplified approach exists that focuses on the kinematic model of the robot's floating base for motion planning. This streamlined method is easier to implement but also adaptable to simpler hardware configurations. Moreover, integrating impedance control into the leg movements proves advantageous, particularly when traversing uneven terrain. This article presents a novel approach in which a quadruped robot employs impedance control for each leg. It utilizes sixth-degree Bézier curves to generate reference trajectories derived from leg velocities within a planar kinematic model for body control. This scheme effectively guides the robot along predefined paths. The proposed control strategy is implemented using the Robot Operating System (ROS) and is validated through simulations and physical experiments on the Go1 robot. The results of these tests demonstrate the effectiveness of the control strategy, enabling the robot to track reference trajectories while showing stable walking and trotting gaits.

How Do Escape Distance Behavior of Broiler Chickens Change in Response to a Mobile Robot Moving at Two Different Speeds?

In poultry farming, robots are considered by birds as intruder elements to their environment, because animals escape due to their movement. Their escape is measured using the escape distance (ED) technique. This study analyzes the behavior of animals in relation to their ED through the use of a robot with two speeds: 12 rpm and 26 rpm. The objective is to understand whether the speeds cause variations in ED and their implications for animal stress. A broiler breeding cycle was analyzed (six weeks) through the introduction of the robot weekly. ED analyses were carried out on static images generated from footage of the robot running. The results indicate higher escape distance rates (p < 0.05) peaking midway through the production cycle, notably in the third week. Conversely, the final weeks saw the lowest ED, with the most significant reduction occurring in the last week. This pattern indicates a gradual escalation of ED up to the fourth week, followed by a subsequent decline. Despite RPM12 having shown low ED results, it did not show enough ED to move the animals away from their path of travel, causing bumps and collisions. RPM26 showed higher ED in all breeding phases, but showed ED with no bumps and collisions.

Social robotics as an adjuvant during the hospitalization process in pediatric oncology patients.

OBJECTIVE: To describe the experience of implementing social robotics as an adjuvant during the hospitalization process in pediatric oncology patients. METHODS: Before and after cohort study, applying an intervention with the Lego Mindstorms EV3 kit in patients between 8 and 17 years old that are hospitalized with a cancer diagnosis. We excluded patients from the intensive care unit or when their treating physician recommended so. The intervention consisted of a three-phase workshop: an open architecture story, building a car robot using the Lego Mindstorm EV3 kit, and cooperative playing activities such as races and passing obstacles. RESULTS: Thirteen patients received the intervention with robotic lego. The median age was 15 years (IQR = 3), and 84.6% of the population (n = 11) were male. We found significant improvement in the language (topic management p = .011 and communicative intention p = .034). Other characteristics improved, but not significantly (self-care activities index, catching). No adverse events occurred during the intervention. CONCLUSIONS: The results of this pilot study suggest that implementing social robotics during hospitalization in children with cancer is a therapeutic adjuvant and safe intervention that promotes better communication, self-care, and a physical activity improvement. For future studies, the impact of this intervention could be measured in hospitalized pediatric cancer patients.

NeoSLAM: Long-Term SLAM Using Computational Models of the Brain.

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in the field of robotics, enabling autonomous robots to navigate and create maps of unknown environments. Nevertheless, the SLAM methods that use cameras face problems in maintaining accurate localization over extended periods across various challenging conditions and scenarios. Following advances in neuroscience, we propose NeoSLAM, a novel long-term visual SLAM, which uses computational models of the brain to deal with this problem. Inspired by the human neocortex, NeoSLAM is based on a hierarchical temporal memory model that has the potential to identify temporal sequences of spatial patterns using sparse distributed representations. Being known to have a high representational capacity and high tolerance to noise, sparse distributed representations have several properties, enabling the development of a novel neuroscience-based loop-closure detector that allows for real-time performance, especially in resource-constrained robotic systems. The proposed method has been thoroughly evaluated in terms of environmental complexity by using a wheeled robot deployed in the field and demonstrated that the accuracy of loop-closure detection was improved compared with the traditional RatSLAM system.

A Comparison Study between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous Navigation in Dynamic Scenarios.

At the beginning of a project or research that involves the issue of autonomous navigation of mobile robots, a decision must be made about working with traditional control algorithms or algorithms based on artificial intelligence. This decision is not usually easy, as the computational capacity of the robot, the availability of information through its sensory systems and the characteristics of the environment must be taken into consideration. For this reason, this work focuses on a review of different autonomous-navigation algorithms applied to mobile robots, from which the most suitable ones have been identified for the cases in which the robot must navigate in dynamic environments. Based on the identified algorithms, a comparison of these traditional and DRL-based algorithms was made, using a robotic platform to evaluate their performance, identify their advantages and disadvantages and provide a recommendation for their use, according to the development requirements of the robot. The algorithms selected were DWA, TEB, CADRL and SAC, and the results show that-according to the application and the robot's characteristics-it is recommended to use each of them, based on different conditions.

Education by a social robot on nutrition and catheter care in pediatric oncology patients.

PURPOSE: To improve knowledge on nutrition and catheter care in children with cancer by an educational intervention with a social robot. METHODS: We conducted a cohort study on pediatric cancer patients in a high complexity Hospital in Bogotá, Colombia. We included 14 patients (8-17 years old) who underwent an educational intervention with the help of a humanoid robot (Nao V6). The robot was programmed to transmit educational messages about self-care in feeding and using the central venous catheter. A survey with yes-no questions was administered before and after the intervention. RESULTS: We found an improvement in understanding of the subject matter related to nutrition and catheter care, when comparing the knowledge on topics before and after the educational intervention (p < .001). CONCLUSION: Education by a social robot on nutrition and catheter care showed a positive effect on children's knowledge on these topics. Therefore, it potentially decreases the risk of poor feeding habits and inadequate central venous catheter management, and improves adherence to recommendations and quality of life.

Promoting Access in a Mexican-American Museum during the Pandemic: Online Community Events and Robots.

During COVID-19, many museums closed their physical structures and transitioned their exhibits to online platforms for public digital access. As museums reopen, there remains a need for some visitors to attend exhibits and cultural events with minimal risk. This article examines an innovative hybrid platform for museum digital access-personal telerobots to co-explore museums alongside community members. The way it works is as follows: a community member remains at home and remotely logs into the museum robot. A friend/family member is physically at the museum, and once the robot is embodied by the remote user, they can walk around the museum together, talk with each other, interact with artifacts, and experience the exhibits together. Ultimately, the robot user and the visitor can both be immersed in the venue, separate yet together at the same time. This article examines the use of online community events and personal robots in a Mexican-American history, art, and culture museum for cultural exhibits and how these technologies may facilitate the way community members learn, interact, and explore museum artifacts. It also explores the need for best practices on the use of online communities and personal robot technology in museums. This work contributes observations, reflections, and curatorial considerations on both forms of digital media for inclusive museum practices.

Adaptive controller based on barrier Lyapunov function for a composite Cartesian-delta robotic device for precise time-varying position tracking.

This study presents the design of an adaptive event-driven controller for solving the trajectory tracking problem of a composite robotic device made up of a three-dimensional Cartesian and a parallel Delta robot. The proposed composite device has a mathematical model satisfying a standard Lagrangian structure affected by modeling uncertainties and external perturbations. The adaptive gain of the controller is considered to enforce the convergence of the tracking error while the state bounds are satisfied. The barrier Lyapunov function addresses the preconceived state constraints for both robotic devices by designing a time-varying gain that guarantees the ultimate boundedness of the tracking error under the effect of external perturbations. The event-driven approach considers that the Cartesian robot is moving into a predefined invariant zone near to the origin. In contrast, the delta robot can complete the tracking problem once the end-effector is inside the given zone. The suggested controller was evaluated using a virtual representation of the composite robotic device showing better tracking performance (while the restrictions are satisfied) than the performances obtained with the traditional linear state feedback controllers. Analyzing the mean square error and its integral led to confirming the benefits of using the adaptive barrier control.

Inteligencia Artificial: pinocho dos mil años luz / Artificial Intelligence: pinocchio two thousand light years

Una versión preliminar de este texto fue publicada en 2001 en ocasión del estreno del film. Dos décadas después y ante la explosión de la Inteligencia Artificial Generativa se presenta una versión ampliada que mantiene sin embargo la tesis original. Se trata de la importancia y vigencia de los mitos para comprender la distancia entre la lógica computacional y lo propio de la condición humana. En esta línea se analiza el film de Spielberg en interlocución con el célebre relato de Carlo Collodi y con los aportes del psicoanálisis para pensar el presente y el futuro de la Inteligencia Artificial

A preliminary version of this text was published in 2001 on the occasion of the film's premiere. Two decades later and given the explosion of Generative Artificial Intelligence, an expanded version is presented that nevertheless maintains the original thesis. It is about the importance and validity of myths to understand the distance between computational logic and the human condition. Along these lines, Spielberg's film is analyzed in dialogue with the famous story by Carlo Collodi and with the contributions of psychoanalysis to think about the present and future of Artificial Intelligence

La inteligencia artificial en el cine / Artificial intelligence in movies

La mayor parte de los films sobre inteligencia artificial hacen de esta un pretexto para tratar otras cuestiones: los peligros de la tecnociencia al servicio de intereses económicos, bélicos o políticos, la violencia de género, la segregación, los riesgos de un sistema político totalitario, o la deshumanización de la sociedad consumista en que vivimos. Las películas que optan por imaginar un futuro cercano en que se produzca la "singularidad" de un programa que se subjetive y empiece a desear, odiar o amar, pueden ordenarse en cinco grandes escenarios típicos: programas autoconscientes empoderados, subjetividades humanas transformadas en programa computacional, androides diseñados mediante biotecnología, robots que devienen humanos, y robots que semejan a humanos, pero no lo son

Most films about artificial intelligence make this a pretext to address other issues: the dangers of technoscience at the service of economic, war or political interests, gender violence, segregation, the risks of a totalitarian political system, or the dehumanization of the consumerist society in which we live. The films that choose to imagine a near future in which the "singularity" of a program that becomes subjective and begins to desire, hate or love occurs, can be organized into five large typical scenarios: empowered self-conscious programs, human subjectivities transformed into a computer program, androids designed through biotechnology, robots that become humans, and robots that look like humans, but are not.

An open-source smart fraction collector for isocratic preparative liquid chromatography.

Preparative liquid chromatography is a technique for separating complex samples or isolating pure compounds from complex extracts. It involves eluting samples through a packed column and selectively collecting or isolating the separated bands in a sequence of fractions. Depending on the column length and the sample complexity, a large number of fractions may be obtained, making fraction collection a laborious and time-consuming process. Manual fraction collection is also tedious, error-prone, less reproducible, and susceptible to contamination. Several commercial and lab-made solutions are available for automated fraction collection, but most systems do not synchronize with the instrument detector and collect fractions at fixed volumes or time intervals. We have assembled a low-cost Arduino-based smart fraction collector that can record the signal from the UV-vis detector of the chromatography instrument and enable the automated selective collection of the targeted bands. The system consists of a robot equipped with position sensors and a 3-way solenoid valve that switches the column effluent between the waste or collection positions. By proper programming, an Arduino board records the detector response and actuates the solenoid valve, the position sensors, and the stepper motors to collect the target chromatographic bands.

Social Robots and Brain-Computer Interface Video Games for Dealing with Attention Deficit Hyperactivity Disorder: A Systematic Review.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity that affects a large number of young people in the world. The current treatments for children living with ADHD combine different approaches, such as pharmacological, behavioral, cognitive, and psychological treatment. However, the computer science research community has been working on developing non-pharmacological treatments based on novel technologies for dealing with ADHD. For instance, social robots are physically embodied agents with some autonomy and social interaction capabilities. Nowadays, these social robots are used in therapy sessions as a mediator between therapists and children living with ADHD. Another novel technology for dealing with ADHD is serious video games based on a brain-computer interface (BCI). These BCI video games can offer cognitive and neurofeedback training to children living with ADHD. This paper presents a systematic review of the current state of the art of these two technologies. As a result of this review, we identified the maturation level of systems based on these technologies and how they have been evaluated. Additionally, we have highlighted ethical and technological challenges that must be faced to improve these recently introduced technologies in healthcare.

Drone-Robot to Clean Power Line Insulators.

The inspection and maintenance of transmission systems are necessary for their proper functioning. In this way, among the line's critical points are the insulator chains, which are responsible for providing insulation between conductors and structures. The accumulation of pollutants on the insulator surface can cause failures in the power system, leading to power supply interruptions. Currently, the cleaning of insulator chains is performed manually by operators who climb towers and use cloths, high-pressure washers, or even helicopters. The use of robots and drones is also under study, presenting challenges to be overcome. This paper presents the development of a drone-robot for cleaning insulator chains. The drone-robot was designed to identify insulators by camera and perform cleaning through a robotic module. This module is attached to the drone and carries a battery-powered portable washer, a reservoir for demineralized water, a depth camera, and an electronic control system. This paper includes a literature review on the state of the art related to strategies used for cleaning insulator chains. Based on this review, the justification for the construction of the proposed system is presented. The methodology used in the development of the drone-robot is then described. The system was validated in a controlled environment and in field experimental tests, with the ensuing discussions and conclusions formulated, along with suggestions for future work.

Automation of a tertiary hospital pharmacy drug dispensing system in a lower-middle-income country: A case study and preliminary results.

Background: An increase in the use of automated systems has optimised the drug dispensing process in hospitals. Methods: This case study describes the implementation of automated drug dispensing system and presents the preliminary results of automated pharmaceutical dispensing with robots (PillPick® and BoxPicker®, ©Swisslog Healthcare) at Hospital Sírio-Libanês, a private tertiary hospital in Brazil. Results: During the study period, between 2013 (pre-automation) and 2017 (post-automation) the number total of medication errors has not changed post-automation, but there was significant reduction in error in the dispensing phase with a relative risk of 0.84 (95% confidence interval: 0.70-0.99) withal a reduction in the numbers of returned items, breakages, and loss of medications, although that delivery times have increased.. Conclusion: The study results suggest that the use of robotic systems in the central pharmacy may improve hospital pharmacy management and generate only a few errors in dispensing pharmaceuticals.

A New Approach for Including Social Conventions into Social Robots Navigation by Using Polygonal Triangulation and Group Asymmetric Gaussian Functions.

Many authors have been working on approaches that can be applied to social robots to allow a more realistic/comfortable relationship between humans and robots in the same space. This paper proposes a new navigation strategy for social environments by recognizing and considering the social conventions of people and groups. To achieve that, we proposed the application of Delaunay triangulation for connecting people as vertices of a triangle network. Then, we defined a complete asymmetric Gaussian function (for individuals and groups) to decide zones where the robot must avoid passing. Furthermore, a feature generalization scheme called socialization feature was proposed to incorporate perception information that can be used to change the variance of the Gaussian function. Simulation results have been presented to demonstrate that the proposed approach can modify the path according to the perception of the robot compared to a standard A* algorithm.

Group Emotion Detection Based on Social Robot Perception.

Social robotics is an emerging area that is becoming present in social spaces, by introducing autonomous social robots. Social robots offer services, perform tasks, and interact with people in such social environments, demanding more efficient and complex Human-Robot Interaction (HRI) designs. A strategy to improve HRI is to provide robots with the capacity of detecting the emotions of the people around them to plan a trajectory, modify their behaviour, and generate an appropriate interaction with people based on the analysed information. However, in social environments in which it is common to find a group of persons, new approaches are needed in order to make robots able to recognise groups of people and the emotion of the groups, which can be also associated with a scene in which the group is participating. Some existing studies are focused on detecting group cohesion and the recognition of group emotions; nevertheless, these works do not focus on performing the recognition tasks from a robocentric perspective, considering the sensory capacity of robots. In this context, a system to recognise scenes in terms of groups of people, to then detect global (prevailing) emotions in a scene, is presented. The approach proposed to visualise and recognise emotions in typical HRI is based on the face size of people recognised by the robot during its navigation (face sizes decrease when the robot moves away from a group of people). On each frame of the video stream of the visual sensor, individual emotions are recognised based on the Visual Geometry Group (VGG) neural network pre-trained to recognise faces (VGGFace); then, to detect the emotion of the frame, individual emotions are aggregated with a fusion method, and consequently, to detect global (prevalent) emotion in the scene (group of people), the emotions of its constituent frames are also aggregated. Additionally, this work proposes a strategy to create datasets with images/videos in order to validate the estimation of emotions in scenes and personal emotions. Both datasets are generated in a simulated environment based on the Robot Operating System (ROS) from videos captured by robots through their sensory capabilities. Tests are performed in two simulated environments in ROS/Gazebo: a museum and a cafeteria. Results show that the accuracy in the detection of individual emotions is 99.79% and the detection of group emotion (scene emotion) in each frame is 90.84% and 89.78% in the cafeteria and the museum scenarios, respectively.

A bibliometric and thematic approach to agriculture 4.0.

Researchers are developing digital solutions for agriculture. Humanity has perfected agriculture throughout history because this activity is fundamental to our existence. The agricultural sector is currently incorporating new technologies from other areas. This phenomenon is agriculture 4.0. However, a challenge to research is the integration of technologies from different knowledge fields, and this has caused theoretical and practical difficulties. Thus, our purpose with this study has been to understand the core agriculture 4.0 research themes. We have used a bibliometric analysis, and guided the data collection by the PRISMA protocol. VosViewer and Bibliometrix software generated the results. We found two main research fronts, one focussed on agriculture 4.0 development, and another on the impacts of agriculture 4.0, which may be positive or negative. We found 21 main keywords or topics researched in agriculture 4.0 related to these research fronts. These themes are within five different axes. We managed to establish a good understanding of the topics around agriculture 4.0. Future studies could focus on the responsible development of digital solutions for agriculture. This is because the social, environmental, and economic impacts of these new solutions may be positive or negative. We conclude that digital agriculture is the node technologies integration for the automation of agricultural activities.

Passive Landmark Geometry Optimization and Evaluation for Reliable Autonomous Navigation in Mining Tunnels Using 2D Lidars.

Autonomous navigation in mining tunnels is challenging due to the lack of satellite positioning signals and visible natural landmarks that could be exploited by ranging systems. Solutions requiring stable power feeds for locating beacons and transmitters are not accepted because of accidental damage risks and safety requirements. Hence, this work presents an autonomous navigation approach based on artificial passive landmarks, whose geometry has been optimized in order to ensure drift-free localization of mobile units typically equipped with lidar scanners. The main contribution of the approach lies in the design and optimization of the landmarks that, combined with scan matching techniques, provide a reliable pose estimation in modern smoothly bored mining tunnels. A genetic algorithm is employed to optimize the landmarks' geometry and positioning, thus preventing that the localization problem becomes ill-posed. The proposed approach is validated both in simulation and throughout a series of experiments with an industrial skid-steer CAT 262C robotic excavator, showing the feasibility of the approach with inexpensive passive and low-maintenance landmarks. The results show that the optimized triangular and symmetrical landmarks improve the positioning accuracy by 87.5% per 100 m traveled compared to the accuracy without landmarks. The role of optimized artificial landmarks in the context of modern smoothly bored mining tunnels should not be understated. The results confirm that without the optimized landmarks, the localization error accumulates due to odometry drift and that, contrary to the general intuition or belief, natural tunnel features alone are not sufficient for unambiguous localization. Therefore, the proposed approach ensures grid-based SLAM techniques can be implemented to successfully navigate in smoothly bored mining tunnels.