Robotics Laboratory Research Articles

What is robotics made of? The interdisciplinary politics of robotics research

Under framings of grand challenges, robotics has been proposed as a solution to a wide range of societal issues such as road safety, ageing society, economic productivity and climate change. However, what exactly is robotics research? From its inception, robotics has been an inherently interdisciplinary field, bringing together diverse domains such as engineering, cognitive science, computer science and, more recently, knowledge from social sciences and humanities. Previous research on interdisciplinarity shows that this mode of knowledge production is often driven by societal concerns and political choices. The politics of who gets to make these choices and on what terms is the focus of empirical research in this paper. Using a novel mixed-method approach combining bibliometrics, desk-based analysis and fieldwork, this article builds a narrative of interdisciplinarity at the UK’s largest public robotics lab, the Bristol Robotics Laboratory. This paper argues for the recognition of the plural ways of knowing interdisciplinarity. From citation analysis, through tracing of the emerging fields and disciplines, to, finally, the investigation of researchers’ experiences; each method contributes a distinct and complementary outlook on “what robotics is made of”. While bibliometrics allows visualising prominent disciplines and keywords, document analysis reveals influential and missing stakeholders. Meanwhile, fieldwork explores the logics underpinning robotics and identifies the capabilities necessary to perform the research. In doing so, the paper synthesises plural ways of locating politics in interdisciplinary research and provides recommendations for enabling “structural preparedness for interdisciplinarity”.

PCB Electronic Component Defect Detection Method based on Improved YOLOv4 Algorithm

In an era of information, people’s demand for electronic products is greatly increased. As an important part of electronic components, Printed Circuit Board (PCB) has a huge annual output and a variety of sizes and types. Therefore, the traditional method of manually detecting PCB defects may fail to meet the required production standards due to the high error rate. With the development of deep learning, a batch of PCB defect detection models combined with deep learning have been produced, which improves the detection efficiency. However, there are still some problems in these methods, such as low automation degree, low detection degree, and poor stability. This paper proposes an improved algorithm, based on YOLOV4, which uses PCB defect data set released by the Intelligent Robot Laboratory of Peking University, and has abundant images of different defect types, which greatly increases the reliability of the model. By analyzing the feature distribution of CSPDarkNet53 structure layer and the detection target defect size distribution in the data set used, in the data pre-processing and input stage, the image is automatically subdivided according to the average size of the callout box of the detection image, and the probability of anchor containing detection target is increased. Experimental results show that the improved YOLOV4 algorithm has a mean Average Precision (mAP) of 96.88%.

Autonomous wheelchair for patient\u2019s transportation on healthcare institutions

The transport of patients from the inpatient service to the operating room is a recurrent task in a hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented a system, named Connected Driverless Wheelchair, that can receive transportation requests directly from the hospital information management system, pick up patients at their beds, navigate autonomously through different floors, avoid obstacles, communicate with elevators, and drop patients off at the designated operating room. As a result, a prototype capable of transporting patients autonomously in hospital environments was obtained. Although it was impossible to test the final developed system at the hospital as planned, due to the COVID-19 pandemic, the extensive tests conducted at the robotics laboratory facilities, and our previous experience in integrating mobile robots in hospitals, allowed to conclude that it is perfectly prepared for this integration to be carried out. The achieved results are relevant since this is a system that may be applied to support these types of tasks in the future, making the transport of patients more efficient (both from a cost and time perspective), without unpredictable delays and, in some cases, safer.

MiniCERNBot Educational Platform: Antimatter Factory Mock-up Missions for Problem-Solving STEM Learning.

Mechatronics and robotics appeared particularly effective in students’ education, allowing them to create non-traditional solutions in STEM disciplines, which have a direct impact and interaction with the world surrounding them. This paper presents the current state of the MiniCERNBot Educational Robotic platform for high-school and university students. The robot provides a comprehensive educative system with tutorials and tasks tuned for different ages on 3D design, mechanical assembly, control, programming, planning, and operation. The system is inspired to existing robotic systems and typical robotic interventions performed at CERN, and includes an education mock-up that follows the example of a previous real operation performed in CERN’s Antimatter Factory. The paper describes the learning paths where the MiniCERNBot platform can be used by students, at different ages and disciplines. In addition, it describes the software and hardware architecture, presenting results on modularity and network performance during education exercises. In summary, the objective of the study is improving the way STEM educational and dissemination activities at CERN Robotics Lab are performed, as well as their possible synergies with other education institutions, such as High-Schools and Universities, improving the learning collaborative process and inspiring students interested in technical studies. To this end, a new educational robotic platform has been designed, inspired on real scientific operations, which allows the students practice multidisciplinary STEM skills in a collaborative problem-solving way, while increasing their motivation and comprehension of the research activities.

Implementing teacher-centered robotics activities in science lessons: The effect on motivation, satisfaction and science skills

In studies on the use of robotic in science education, students are generally expected to design and program robots in specially designed robotic laboratories and during extracurricular activities. Although researchers claim that the student-centered approach and active student participation is more effective, teachers generally have to apply traditional teaching strategies in the field of science education due to the high number of students, a lack of materials, insufficient time and lack of professional teaching skills. Robotics activities can be performed in a traditional classroom environment and within a teacher-centered lesson structure. The aim of this study was to investigate the effect of teacher-centered robotics activities performed in science lessons on students' motivation, to determine their satisfaction with the activities and to collect their opinions about the activities. A parallel mixed-methods design was used for data collection. The results of the study indicated that teacher-centered robotics activities increased the motivation of students to participate in science lessons. Moreover, when the interviews with the students were examined, all of them commented that engaging in robotics activities improved their science skills. In addition, the majority of students were satisfied with the robotics activities and had positive feelings about them, believing that they helped them to learn and were enjoyable and interesting.

Experiments in Artificial Sociality

In this article, I explore how experiments with social robots enact and reconfigure more-than-human forms of sociality. I combine recent anthropological discussions of nonhuman sociality with Andy Pickering’s work on dances of agency (1993, 1995) and John Law’s method assemblages (2004) to show how human-robot interaction experiments enact open-ended and decentred configurations of entangling relations between humans and robots. I propose the concept of artificial sociality to capture both the ongoing enactments and multiple results of such experimental reconfigurations. Using these conceptual tools, I unpack the “curious robot experiment” from my ethnographic fieldwork in a Japanese robotics laboratory and compare the kinds of sociality produced in the two experimental conditions. I argue that the curious robot exemplifies what Pickering calls technologies of engagement (2018) by manifesting a form of artificial sociality that augments the unpredictability of dances of agency enacted in (re)configurations of entangling relations.

Voice frequency impact for voice control of collaborative robots

The project is focused on research of voice control of collaborative robots. Previous research of voice control and speech recognition research have given examples and evidence that machines can truly be controlled by human voice. The main problem in the voice control of collaborative robots is the lack of reliability and safety of control compared to manual control. Of the results achieved, the most and least relevant voice control problem has been clearly identified, next step is voice frequency impact for voice control. The experiments were performed in the robotics laboratory at the Faculty of Materials Science and Technology in Trnava, and this using an Arduino kit and a small robot “DOBOT Magician“. In the final part of the project are summarized all experimentally found facts and confronted with the assumptions set in the design of the experiment.

Educational robotics and STEAM in early childhood education

The interest in the future configuration, focusing on the innovative technologies and more specifically on STEAM (Science, Technology, Engineering, Arts, Math), is remarkably increased. The value of STEAM education is undeniable as a means of developing basic skills and competencies of young students improving the learning process, developing communication skills, and solving real-life difficulties. The current research study was completed in the context of an actual learning process, with the view to study educational robotics in kindergarten students to engage them with STEAM education, using the programmable robot Bee-Bot® initially. The didactic intervention was held, which was developed in two phases. More specifically, a sample of 12 children (age range: 5–6 years old) took part in an intensive educational robotics lab for 16 sessions (4 weeks) by using a bee-shaped robot called Bee-Bot®. The results of our current research study revealed that STEAM education could also take place in a speech therapy clinic using the appropriate educational robots. Our young students developed and mastered knowledge in programming and computerizing, and algorithmic thinking with playful mod using educational robots, and they also built their vocabulary and develop communication skills.

Phenotypic microbial response to antimicrobial exposure conditions with a molecular analysis quantification of species-specific 16S rRNA content

The emergence and rapid spread of resistant bacteria has become a serious public health concern worldwide. Delayed antimicrobial therapy significantly increases mortality in high-risk infections with a particularly strong association with septic shock. Therefore, antimicrobial agents are often injudiciously used without any evidence-based microbiological confirmation. Antimicrobial consumption is strongly linked to the emergence and dissemination of antimicrobial-resistant bacteria strains in several epidemiological studies. According to CDC's recent publication, an estimated 30% of outpatient oral antimicrobial prescriptions may have been inappropriate. A compact and rapid pathogen identification (ID) and antimicrobial susceptibility testing (AST) can assist to address both the unnecessary use and overuse of antimicrobials, and therefore effectively reduce antimicrobial resistance. The overall goal of these AST protocols is to deliver a molecular diagnostic platform that is capable of profiling the antimicrobial susceptibility of causative pathogens in hours, not days. The presented AST utilizes an electrochemical sensor to quantify the microbial changes of 16S rRNA after exposure to various antimicrobial conditions either from clinical isolates or directly from unprocessed clinical specimens such as urine and blood. These protocols can be performed by our robotic lab automation systems or manual benchtop assays with associated reagent kits, AST stripwells and sensor chips.•A rapid, quantifiable antimicrobial efficacy profiling comparable to traditional AST reporting.•Customized antimicrobials and dilution ranges tailored to unique specifications for research and development.•Direct antimicrobial susceptibility of viable pathogen from whole blood, urine, or subculture.

Real time defect detection during composite layup via Tactile Shape Sensing

Abstract In this study an automated composite layup end effector is presented which is the first to be able to find defects in real time during layup using tactile shape sensing. Based around an existing sensor concept developed by the Bristol Robot Laboratory known as the ‘TacTip’, a new end effector is developed, replacing the soft gel core of the original sensor was replaced by a much firmer elastomer, enabling it to apply up to 400N of compaction force. In this paper it is shown to successfully detect typical defects such as wrinkles, foreign objects, layup errors or incorrect material types while simultaneously compacting preimpregnated composite materials over complex mould shapes.

CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

The Convolutional Neural Network (CNN) is an object classification method that has been widely used in recent research. In this paper, we propose CNN for use in the self-localization of wheeled soccer robots on a soccer field. If the soccer field is divided into equally sized quadrants with imaginary vertical and horizontal lines intersecting in the middle of the field, then the soccer field has an identical shape for each quadrant. Every quadrant is a reflection of the other quadrants. Superficially similar images appearing in different positions may result in positioning mistakes. This paper proposes a solution to this problem by using a visual modelling of the gyrocompass line mark and omni-vision image for the CNN-based self-localization system. A gyrocompass is used to obtain the angle of the robot on the soccer field. A 360° omni-vision camera is used to capture images that cover all parts of the soccer field wherever the robot is located. The angle of the robot is added to the omni-vision image using the visual modelling method. The implementation of self-localization without visual modelling gives accuracy rates of 0.3262, and this result is increased to 0.6827 with the proposed methods. The experiment was carried out in the robotics laboratory of the Institut Teknologi Sepuluh Nopember (ITS) with the ITS Robot with Intelligent System (IRIS) robot.

Astronomer-turned-filmmaker strives to ignite an interest in space

For scientists at NASA’s Jet Propulsion Laboratory in Pasadena, CA, the night of January 3, 2004 entailed six minutes of nerve-wracking terror. Around 8:30 PM Pacific Time, the Spirit rover began its plunge to the Martian surface, as an audience of engineers and others—temporarily unable to receive any communication from the craft—waited anxiously to get confirmation of its success. Aided by a heat shield, a giant parachute, airbags, and reverse-firing rockets, the rolling robotic laboratory had to decelerate from 12,000 miles per hour to zero miles per hour, with no input from its human designers, who were nervously drumming on their desks in Pasadena. Spirit’s successful landing was a defining moment in the human exploration of Mars. As part of projects he calls science-driven art, Jose Francisco Salgado created a mini-movie for each of the seven parts of Holst’s suite, which Holst had based on astrological characteristics of each world. Image credit: Jose Francisco Salgado (artist). But how might one capture the gravity of that moment, the tension among observers, the majesty of the feat, and its implications for researchers and the public? Astronomer-turned-filmmaker Jose Francisco Salgado had some ideas. A few years ago, he put together a film in which that landing event unfolds as a suspenseful, and ultimately heroic, scene. Audiences who attend presentations of the film—one of many in his “Science and Symphony” series—watch a reconstruction of Spirit’s journey on a giant screen while a live orchestra plays “Mars, The Bringer of War” from The Planets , Gustav Holst’s most famous contribution to classical music. In Salgado’s telling, the visuals and music line up exactly, so that the rover’s stressful fall occurs during a long and building crescendo in Holst’s piece (see https://vimeo.com/46390508). The treads of a rover sigh and settle into the alien soil at the …

Effectiveness of multi-gated sequence model for the learning of kinematics and dynamics of an industrial robot

Purpose For efficient trajectory control of industrial robots, a cumbersome computation for inverse kinematics and inverse dynamics is needed, which is usually developed using spatial transformation using Denavit–Hartenberg principle and Lagrangian or Newton–Euler methods, respectively. The model is highly non-linear and needs to deal with uncertainties because of lack of accurate measurement of mechanical parameters, noise and non-inclusion of joint friction, which results in some inaccuracies in predicting accurate torque trajectories. To get a guaranteed closed form solution, the robot designers normally follow Pieper’s recommendation and compromise with the mechanical design. While this may be acceptable for the industrial robots where the aesthetic look is not that important, it is not for humanoid and social robots. To help solve this problem, this study aims to propose an alternative machine learning-based computational approach based on a multi-gated sequence model for finding appropriate mapping between Cartesian space to joint space and motion space to joint torque space. Design/methodology/approach First, the authors generate sufficient data required for the sequence model, using forward kinematics and forward dynamics by running N number of nested loops, where N is the number of joints of the robot. Subsequently, to develop a learning-based model based on sequence analysis, the authors propose to use long short-term memory (LSTM) and hence, train an LSTM model, the architecture details of which have been discussed in the paper. To make LSTM learning algorithms perform efficiently, the authors need to detect and eliminate redundant features from the data set, which the authors propose to do using an elegant statistical tool called Pearson coefficient. Findings To validate the proposed model, the authors have performed rigorous experiments using both hardware and simulation robots (Baxter/Anukul robot) available in their laboratory and KUKA simulation robot data set made available from Neural Learning for Robotics Laboratory. Through several characteristic plots, it has been shown that a sequence-based LSTM model of deep learning architecture with non-redundant features could help the robots to learn smooth and accurate trajectories more quickly compared to data sets having redundancy. Such data-driven modeling techniques can change the future course of direction of robotics research for solving the classical problems such as trajectory planning and motion planning for manipulating industrial as well as social humanoid robots. Originality/value The present investigation involves development of deep learning-based computation model, statistical analyses to eliminate redundant features, data creation from one hardware robot (Anukul) and one simulation robot model (KUKA), rigorously training and testing separately two computational models (specially configured two LSTM models) – one for learning inverse kinematics and one for learning inverse dynamics problem – and comparison of the inverse dynamics model with the state-of-the-art model. Hence, the authors strongly believe that the present paper is compact and complete to get published in a reputed journal so that dissemination of new ideas can benefit the researchers in the area of robotics.

Rancang Bangun Prototipe Pengendali Pintu Air Berbasis SMS (Short Message Service) Untuk Pengairan Sawah Menggunakan Arduino

AbstrakCurrently opening and closing the floodgates for rice fields is still using the manual method, the manual method certainly still requires more effort to do this. Therefore it is necessary to develop a floodgate controller prototype using SMS (Short Message Service) with Arduino as the microcontroller, before making it into a larger form.This research uses experimental research methods. This research was conducted in the robotics laboratory of the Electrical Engineering Study Program, Faculty of Engineering, Bengkulu University. The results of the study, the time to send the fastest SMS when the fields need water is 10.21 seconds and the time to send the fastest SMS when the water needs for the fields is sufficient is 10.00 seconds using the Tri SIM card and the longest time to send SMS when the fields need water is 16.28 seconds and the sending time The longest SMS when the water requirement for rice fields is sufficient is 16.71 seconds using an Indosat SIM card. The performance of the floodgate controller prototype can open and close properly according to the command given via SMS service on the cellphone.Kata kunci: Module SIM800L, sensor ultrasonic, motor servo, Arduino Uno R3.

A Systematic Review for Service Humanoid Robotics Model in Hospitality

Nowadays, the Fourth Industrial Revolution has brought artificial intelligence to the forefront, and more and more intelligent robots begin to be used in the hospitality industry. In this study, the application of service humanoid robots in the hospitality industry is investigated based on Cardiff Metropolitan University EUREKA Robotics Lab’s robot as reported by Lab (in Eureka robotics lab, 2017, https://www.cardiffmet.ac.uk/technologies/Pages/EUREKA-Robotics-Lab.aspx). The research ontology of this study is post-positivism. The research philosophy of this research is phenomenology. Phenomenological studies have indicated that this phenomenon can only be truly understood and experienced through subjective immersive research directly involving researchers, and the interaction among researchers is an integral part of the research. In this study, the data are collated through case researches and experimental interviews. Finally, Some proposals for transforming the traditional hospitality industry into the direction of intelligence is summarized. In future research, a technical model combining artificial intelligence will be further developed.

One‐Pot Access towards 4,5‐Disubstituted 2‐Amino‐1H‐imidazoles Starting from Mannich Substrates and their Transformation Utilities

An efficient protocol for the preparation of 4,5‐functionalised 2‐amino‐1H‐imidazoles as fragment‐like structures was developed in isolated yields up to 95 %. The demonstrated one‐pot manner includes an intramolecular oxidative annulation and ring cleavage sequence starting from Mannich precursors. The suggested one‐pot sequential synthetic methodology is easy to apply in automatic and robotic chemistry laboratories for which a rapidly increasing demand is foreseen because of the ongoing revolution in the field of continuous manufacturing of pharmaceutical drug substances and products. Further transformation utilities such as Groebke–Blackburn–Bienaymé 3CR and the formation of marine alkaloid analogs were also represented.

Human–robot collaborative strategies for advanced manufacturing and surgical robotics tasks

This report provides a summary of recent research activities conducted by Prof. Krishna Kaipa who directs the Collaborative Robotics and Adaptive Machines (CRAM) Laboratory at Old Dominion Universi...

A Novel Resident Robotic Curriculum Using Self-Selection for Advanced Training and a Single Web-Based Tool for Feedback and Case-Tracking

Study Objective Describe a novel two-phase robotic curriculum facilitated by a web-based feedback and case-tracking tool, which allows residents to self-select into advanced training. Design Descriptive. Setting Academic Hospital. Patients or Participants OBGYN residents and faculty. Interventions Phase I (Basic), required of all residents, includes online training modules, assessment, and robotic dry lab. Phase II (Advanced) console training is elective. Residents complete 10 SIM drills to proficiency before performing live surgery. A web-based tool is used for surgical feedback. Resident online assessments, SIM lab reports, feedback, and case-logs were reviewed (7/2018-6/2019). Attendings and residents completed a beta-tested satisfaction survey. Measurements and Main Results All 24 residents completed Phase I training. Phase II was initiated by 12 residents and 10/12(83.3%) completed all 10 SIM drills. A median of 10(3,26) attempts were required to complete each drill with a mean time of 5.1hrs to complete all drills. 128 web-based post-surgical feedback entries were completed. Residents performed as bedside assistants (75%,n=96), console surgeon (5.5%,n=7), or both (19.5%,n=25). Most common tracked procedure was hysterectomy 111/193(57.5%). Console surgeons performed 32 cases with a mean console time of 34.6±19.5min. Most common procedure steps included: colpotomy/cuff closure 31.1%, uterine artery 75% and utero-ovarian vessel seal/transection 84.4%(n=32). 28/32(87.5%) Global Evaluative Assessment of Robotic Skills (GEARS) assessments were completed. Mean GEARS score 20.6±3.7(n=28), communication (4.2±0.8,n=61), workload management (3.9±0.9,n=54), and team skills (4.3±0.8,n=60). Residents completing >50% of case were assessed as “apprentice” 38.5% or “competent” 23.1%(n=13). 27/44(61.4%), satisfaction survey response rate. After curriculum change, attendings considered residents prepared as bedside assistants and console surgeons (100%), whereas only 16.7% were prepared before (n=6). Respondents were satisfied with the curriculum (95.0%), found the web-based tool helpful to give/receive feedback (90.0%), and recommended continued use (90.0%) (n=19). Conclusion This novel robotic curriculum allows residents to self-select into advanced training. A web-based tool improves individualized feedback and case-tracking alleviates many challenges of graduated robotic training.

Position Tracking of a Pneumatic-Muscle-Driven Rehabilitation Robot by a Single Neuron Tuned PID Controller

Pneumatic muscle actuators (PMAs) own compliant characteristics and are suitable for use in rehabilitation equipment. This paper introduces a rehabilitation robot driven by PMAs devised in the Rehabilitation and Medical Robot Laboratory. Considering high nonlinearities inside PMAs, a single neuron tuned PID controller is carefully designed. Experimental setup is built up and trials are performed. Results demonstrate the proposed advanced PID algorithm can achieve better capacity in position tracking than the conventional PID controller.

Design of an IoT Smart Meteorological Station Complete with Data Acquisition and Realizing Test Measurements in Cyber-Physical Robotics Lab

Abstract The presented project was completed using modern, widely used IoT based devices. These include the ESP-01 microcontroller, which is programmable using the Arduino IDE (with its integrated ESP8266 interpreter), and a Raspberry Pi Zero W single board computer, which requires a Raspbian Linux distribution. In this project, an original meteorological station was created (complete with data acquisition), employing various sensors.