Robotics Laboratory Research Articles

Применение роботов вертикального перемещения для очистки корпусов судов от обрастаний

Traditionally underwater robotics deals with UAVs and ROVs. This paper discusses a new type of robot for this application - vertical climbing robots. This type of robots has traditionally been considered for terrestrial use. However recent developments have confirmed the effectiveness of using such robots for underwater research. The article describes the work carried out in the Laboratory of robotics and mechatronics of the Ishlinsky Institute for Problems in Mechanics RAS on using climbing robots for technological operations in the aquatic. In particular we discuss issues of creating a robotic system for cleaning ship hulls from fouling. This work was carried out jointly with the St. Petersburg State Marine Technical University.

Has robotic surgery finally found robotic surgeons? The confluence of technology and the perfect user

Objective: Our study aims to identify traits of a surgeon that might determine facility with robotic surgery.Methods: Several robotics lab sessions were organised for 129 medical students, where they were introduced to robotics surgery. Their demographic and self-reported data regarding their experience was collected.Results: Our data suggests that traits commonly believed to facilitate robotic surgery skill acquisition, such as technological proficiency and playing musical instruments do not confer an advantage in robotic surgery.Conclusion: One compelling explanation is the ubiquitous familiarity with technology that characterises this generation of surgeons. This ability to easily interface with digital technology to perform tasks is a more significant contributor to facility in robotic surgery than any trait of dexterity. These findings are interesting and inform the need for further investigation into the relationship between technology, the acquisition of dexterity, and ultimately the evolution of how surgeons are trained.

Impact of Robot Programming Methods on Servo Drive Temperature

There are various motion commands used for programming ABB Industrial Robot & each command is used for a specific application. Also, there are various parameters in the motion command such as Velocity & Zone Error. Setting different values normally alters the Robot performance and eventually the servo drive temperature. This paper has made a case study on the temperature of Servo Drive with respect to the Programming Method. Programming is done using different move commands, Velocity values & Zone Error values to move the Robot End Effector between two fixed locations. The impact of these parameters on the temperature rise of Servo Drive is analyzed. Valid results are arrived using Taguchi L9 Orthogonal array. The optimal results obtained from the Taguchi analysis is checked with the real ABB Robot available at Robotics lab of VSVN Polytechnic College Key Words: ABB Robot RAPID Program – Velocity Data – Zone Error Data – Taguchi L9 Orthogonal Array.

Hovering flight regulation of pigeon robots in laboratory and field

Compared to traditional bio-mimic robots, animal robots show superior locomotion, energy efficiency, and adaptability to complex environments but most remained in laboratory stage, needing further development for practical applications like exploration and inspection. Our pigeon robots validated in both laboratory and field, tested with an electrical stimulus unit (2-s duration, 0.5ms pulse width, 80Hz frequency). In a fixed stimulus procedure, hovering flight was conducted with 8 stimulus units applied every 2s after flew over the trigger boundary. In a flexible procedure, stimulus was applied whenever they deviated from a virtual circle, with pulse width gains of 0.1ms or 0.2ms according to the trajectory angle. These optimized protocols achieved a success hovering rate of 87.5% and circle curvatures of 0.008 m-1-0.024 m-1, largely advancing the practical application of animal robots.

Robotic synthesis laboratory demonstrates comprehensive interrogation of hypotheses

Robotic synthesis laboratory demonstrates comprehensive interrogation of hypotheses

Towards robotic laboratory automation plug & play: Reference architecture model for robot integration

Supportive robotic solutions take over mundane, but essential tasks from human workforce in biomedical research and development laboratories. The newest technologies in collaborative and mobile robotics enable the utilization in the human-centered and low-structured environment. Their adaptability, however, is hindered by the additional complexity that they introduce. In our paper we aim to entangle the convoluted laboratory robot integration architectures. We begin by hierarchically decomposing the laboratory workflows, and mapping the activity representations to layers and components of the automation control architecture. We elaborate the framework in detail on the example of pick-and-place labware transportation - a crucial supportive step, which we identified as the number one area of interest among experts of the field. Our concept proposal serves as a reference architecture model, the key principles of which were used in reference implementations, and are in line with international standardization efforts.

Risk Identification of Robotic Systems through the Application of System-Theoretic Process Analysis

Autonomous multi-mobile robots are becoming increasingly prevalent in various applications, ranging from industrial automation to healthcare and logistics. While these robots offer enhanced efficiency and productivity, their operation introduces many safety challenges. In this paper, the application of System-Theoretic Process Analysis (STPA) is proposed as a systematic approach to identify and mitigate risks associated with the main features of autonomous multi-mobile robots. This approach is illustrated using a case study concerning a transportation task of hazardous products within a robotic analysis laboratory. Through a structured analysis process, STPA enables the identification of unsafe control actions, the establishment of safety constraints also the generation of safety requirements. The ultimate goal is to improve the autonomous attribute of mobile robots, so on ensuring their operational safety in high-risk environments.

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery.

Virtual and Remote Robotic Laboratory

The Virtual Remote Robotic Laboratory (VRRL) represents a paradigm shift in scientific experimentation, offering researchers unprecedented access to remote laboratory facilities and robotic systems. This paper explores the design, implementation, and benefits of VRRL systems in enhancing access and collaboration in scientific research. VRRL leverages cloud computing, IoT technologies, and advanced user interfaces to enable researchers to remotely control and interact with robotic systems and laboratory equipment from anywhere with an internet connection. Through intuitive user interfaces, researchers can seamlessly navigate virtual environments, manipulate robots, monitor experiments in real- time, and analyze data, all without physical presence in the laboratory. Key features of VRRL include robust security protocols to protect sensitive data, flexible experimentation capabilities to accommodate diverse research needs, and collaboration tools to facilitate knowledge sharing and teamwork among researchers worldwide. By breaking geographical barriers and providing on-demand access to laboratory resources, VRRL empowers researchers to conduct experiments more efficiently, accelerate scientific discovery, and foster interdisciplinary collaboration

Development of an IoT based 3D printed Mobile Robot Platform for training of Mechatronics Engineering Students

The advent of the Internet of Things (IoT) has opened up new opportunities in education, particularly in the field of mobile robotics. Mobile robotics is a fast-growing field, and with the increasing demand for skilled robotics engineers, it is essential to provide students with hands-on experience in designing and developing robots. In this regard, an IoT-based 3D printed mobile robot platform has been developed for students training. STEM (Science, Technology, Engineering, and Mathematics) education has been gaining attention as a means to improve science and engineering education. STEM education aims to foster critical thinking, problem-solving skills, creativity, and innovation among students. It involves an educational policy that aims to develop the next generation of skilled professionals by integrating the fields of Science, Technology, Engineering and Mathematics. The goal of this approach is to provide students in mechatronics, who are assigned to the robotics lab during their studies, an up to date knowledge and experience in research and development activities. Thus, this paper aims to provide education that focuses on the acquisition and application of engineering knowledge, including control engineering, mechanical engineering, electrical and electronic engineering, information engineering, among others, with the main focus on mobile robot research.

Exploring the efficacy of collaborative learning in a remote robotics laboratory: a comparative analysis of performance and pedagogical approaches

In today's world, remote-controlled robots are widely used across various industries due to their ability to enhance working efficiency in various applications. Learning about robot operation and human–computer interaction has emerged as a popular topic in recent times. Indeed, learning robotics can be challenging for many students as it requires knowledge of programming, control systems, electronics, etc. Collaborative learning in a physical robotics setting is common in higher education and has received significant attention for its potential to enhance individual learning outcomes. However, the effectiveness of learning robotics in a remote setting is still a matter of debate. In this study, we establish a remote laboratory environment to teach undergraduate students in the engineering discipline. Students are required to utilize a robotic arm to grasp designated objects collaboratively among students through synchronous interactions online. To compare students’ performance under different pedagogical teaching approaches, students are divided into two groups. They each perform the task individually and collaboratively, albeit in a different order. Our study adopts a quantitative method to measure students' learning outcomes based on the assessment of performing the laboratory tasks and completion time. The results indicate a noteworthy improvement in the individual performance of the group of students who engage in collaborative work prior to the individual tasks. These findings have implications for other remote laboratory setups and highlight the effectiveness of collaborative learning in higher education.

Design of dynamically balanced gait for the biped robot while crossing the obstacle

The primary objective of this research work is to generate a dynamically balanced gait for the 16-DOF biped robot while crossing an obstacle using the concept of the zero moment point (ZMP). Also, the authors discussed both the theoretical justification and its practical feasibility on real biped robot. Initially, the position and orientation of the biped robot were obtained with the help of forward kinematics while crossing the obstacle. Later on, various joint angles of the biped robot were calculated using the inverse kinematics approach. Further, the Lagrange-Euler formulation approach was employed for evaluating the dynamics of the biped robot. To generate the smooth gait of the biped robot, a cubic polynomial equation has been assigned for foot and wrist trajectories in the sagittal plane and hip trajectories in the horizontal plane. This integration allows the robot to cross the obstacles while maintaining dynamic balance, marking a significant advancement while crossing the obstacle with a height and width equal to 50 mm, which is 16.10% of the length of the robot’s leg. While crossing the obstacle, the gait of the biped robot has been considered in three stages, such as landing the foot on the obstacle, landing the foot on the ground away from the obstacle by one leg and crossing over the obstacle by another leg. A simulation study has been conducted on MATLAB to verify the dynamically balanced gait while crossing the obstacle. Finally, the generated gait angles are fed into the real 16-DOF biped robot developed by the Robotics Lab at MANIT Bhopal. It has been observed that the generated gait at three stages is more dynamically balanced while crossing the obstacle.

Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Efficient synthesis recipes are needed to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Often, the solid-state synthesis of multicomponent oxides is impeded by undesired by-product phases, which can kinetically trap reactions in an incomplete non-equilibrium state. Here we report a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy, competing by-products, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides, with chemistries representative of intercalation battery cathodes and solid-state electrolytes, our robot performs 224 reactions spanning 27 elements with 28 unique precursors, operated by 1 human experimentalist. Our predicted precursors frequently yield target materials with higher phase purity than traditional precursors. Robotic laboratories offer an exciting platform for data-driven experimental synthesis science, from which we can develop fundamental insights to guide both human and robotic chemists.

Abstract 4501: First reported PIM kinase degraders: Design, profiling & optimization

Abstract The PIM serine/threonine kinases (PIM1/PIM2/PIM3) are downstream effectors of ABL, JAK2 and Flt-3 oncogenes and are required for tumorigenesis. Overexpression has been reported in hematological and solid tumors, myeloma, lymphoma, leukemia and adenocarcinoma. The first generation of PIM inhibitors to make it to the clinic, including SGI-1776, AZD1208 and PIM447, are known to be pan-PIM inhibitors, while the aim of this project was to identify novel & selective PIM3 inhibitors. To this end, several Hit-finding approaches were employed to rapidly generate novel chemical matter, which were then assessed for their activity against PIM3 using a robust ADP-Glo™ assay. In parallel to this Hit-findings activity, we used AZD1208 scaffold as a starting point to generate PIM3 bifunctional protein degraders and designed a specific screening cascade in order to profile such modalities. The PIM degrader library, synthetized using Automated Robotics Lab, was characterized using ADP-Glo assays. It was then screened on our binding and activity kinase platforms, KINOMEscan® and KinaseProfiler™, and then fully profiled including ternary complex formation with biophysics and in vitro Safety Pharmacology Profiling Panels. One example is the AZD1208 derivate degrader, i.e. EC108154-1, which showed nanomolar range affinity for PIM3 and the E3-ligase CRBN-DDB1, and its affinity range was also confirmed with the ternary complex in SPR and MST-TRIC. This comprehensive screening cascade ensures an in-depth characterization and optimization of protein degraders, with the possibility of monitoring the ternary complex formation with biophysics methods. It is a good illustration of how this combination of tests can be instrumental in selecting the best degraders to progress to the next step of drug discovery. Citation Format: Celine Legros, Paul Ratcliffe, Vanessa Porkolab, Michele Modugno, Olivier Mirguet. First reported PIM kinase degraders: Design, profiling & optimization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4501.

Obstacle detection and obstacle-surmounting planning for a wheel-legged robot based on Lidar

PurposeThis paper aims to investigate an autonomous obstacle-surmounting method based on a hybrid gait for the problem of crossing low-height obstacles autonomously by a six wheel-legged robot. The autonomy of obstacle-surmounting is reflected in obstacle recognition based on multi-frame point cloud fusion.Design/methodology/approachIn this paper, first, for the problem that the lidar on the robot cannot scan the point cloud of low-height obstacles, the lidar is driven to rotate by a 2D turntable to obtain the point cloud of low-height obstacles under the robot. Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping algorithm, fast ground segmentation algorithm and Euclidean clustering algorithm are used to recognize the point cloud of low-height obstacles and obtain low-height obstacle in-formation. Then, combined with the structural characteristics of the robot, the obstacle-surmounting action planning is carried out for two types of obstacle scenes. A segmented approach is used for action planning. Gait units are designed to describe each segment of the action. A gait matrix is used to describe the overall action. The paper also analyzes the stability and surmounting capability of the robot’s key pose and determines the robot’s surmounting capability and the value scheme of the surmounting control variables.FindingsThe experimental verification is carried out on the robot laboratory platform (BIT-6NAZA). The obstacle recognition method can accurately detect low-height obstacles. The robot can maintain a smooth posture to cross low-height obstacles, which verifies the feasibility of the adaptive obstacle-surmounting method.Originality/valueThe study can provide the theory and engineering foundation for the environmental perception of the unmanned platform. It provides environmental information to support follow-up work, for example, on the planning of obstacles and obstacles.

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting.

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

The Evolving Landscape of Oil and Gas Chemicals: Convergence of Artificial Intelligence and Chemical-Enhanced Oil Recovery in the Energy Transition Toward Sustainable Energy Systems and Net-Zero Emissions

Chemical-enhanced oil recovery (EOR) is a field of study that can gain significantly from artificial intelligence (AI), addressing uncertainties such as mobility control, interfacial tension reduction, wettability alteration, and emulsifications. The primary objective of this paper is to introduce an integrated framework for AI and chemical EOR for energy harvest operations. Central emphasis is placed on the energy transition, with the aim of expediting the development of cleaner energy harvesting systems and attaining the goal of net-zero emission. To do so, we present how the energy transition is changing the manufacturing of the chemicals for EOR application. For this, the uncertainty associated with materials' design and critical role of the simulators for transferring the laboratory experiences into full-field implementations is discussed. The concept of digitalization and its impact on energy companies are highlighted. The role of digital twin in simulators integration is discussed, emphasizing how increased data access can help design more tolerant chemicals for harsh reservoir environments using real-time data. Also, we discuss how the chemical suppliers, research institutes, startups, and field operators can benefit from self-leaning and robotic laboratories for chemicals manufacturing. Moreover, this paper explores how including AI perspectives can improve our understanding of developing chemical formulations by blending hybrid capabilities. This approach contributes to making energy production more sustainable and aligning with the goal of zero emissions. A workflow is presented to demonstrate how the integration of AI and chemical EOR can be used for both hydrocarbon production and other energy transition operations, such as carbon capture, utilization and storage, hydrogen storage, and geothermal reservoirs. The outcome of this paper stands as a pioneering effort that uniquely addresses these challenges for both academia and the industry and can open many additional doors and identify topics requiring further investigations.

Staging the Robot: Performing Techno-Politics of Innovation for Care Robotics in Japan

In response to the challenges posed by a rapidly aging society and its associated socio-economic difficulties, the Japanese government has encouraged the adoption of AI and robotics technologies for care. Conspicuous investments in these technologies in Japan underscore the dominance of techno-politics of innovation and the advocacy for the robotization of care practices. Such narratives — disseminated by the Japanese state, industry, media, and academia — often overlook the perspectives of the expected users of these technologies. This paper, rooted in a 14-month-long ethnographic study conducted at robotics labs in Japan and the UK in 2022–2023, examines the performance and ethical implications of technoscientific imaginaries portraying Socially Assistive Robots (SARs) as already reliable, safe, and efficient. It sheds light on the intricate relationship between science, technology, the state, and society, emphasizing their use as instruments of power for state-led national development objectives. Moreover, it exposes how technology is presented, creating an illusion of efficiency while neglecting the necessity of involving society in co-designing and co-producing these technologies. The paper ultimately advocates for responsible innovation, emphasizing in particular the need for user involvement to ensure these technologies are not only more efficient and reliable, but also more accessible, inclusive, and fairer.

DBTL bioengineering cycle for part characterization and refactoring

The Design-Build-Test-Learn (DBTL) cycle is a crucial framework in Synthetic Biology for the development and optimization of biological systems. However, the manual nature of the cycle poses limitations in terms of time and labor. This paper focuses on the application of automation techniques to the DBTL cycle, specifically in the testing and characterization of standard bioparts, which are essential components of genetic circuits. By automating the testing process, throughput, reliability, and reproducibility can be significantly improved. This paper discusses the challenges associated with manual testing methods and explores various automation strategies and technologies that can address these challenges. High-throughput screening methods, laboratory robotics, and data analysis algorithms are key elements in the automation process. As a case study, we utilize the automated DBTL cycle to refactor a biosensor, aiming to enhance its performance. Integrating automation in the DBTL cycle offers numerous advantages, including increased efficiency, standardization, and quality control of bioparts. It also enables the exploration of larger design spaces and rapid prototyping of complex genetic systems. Here, we show the advantages of using the DBTL cycle to refactor a biosensor that presents improved performance and can be readily used in more complex circuits.

From Project to Sea: the journey of the robot to the Mediterranean

“It’s all about the learning” One of the most important elements of lecturing in a third level Institution is how best to enable learning. In fact, it can be seen as a professional driving force. There are many types of students, teachers and institutions with multiple pedagogical and andragogical models. The one guiding light that connects us all is the pursuit of knowledge through learning. Amongst the learning models which promote this pursuit are Project Based Learning (PjBL) and the 4 Ps of Creative learning (Project, Peers, Passion and Play). Combining these learning models with the closed loop feedback control model, found in all fields of applied robotics, contributes to interesting projects, scalable team environments and useful applications which actively promote iterative learning. The paper reflects on SETU Applied Robotics Lab PjBL project “The USV LIR” using the lens of the Four Ps of Creative learning: