Robotics Laboratory Research Articles

Soft Medical Robots and Probes: Concise Survey of Current Advances

Soft robotics has emerged as a new branch of robotics gaining huge research interest in recent decades. Owning intrinsic advantages such as compliance and safety, soft robots are closely associated with the medical requirements of medical robots. This review is written to overview advances in the medical applications of soft robots, either for readers primarily familiar with traditional medical systems, or for researchers planning to develop soft robots for medical applications. Recent publications related to soft medical robots were reviewed to represent the state’, ’of’, ’the’, ’art advances in this field. The review tends to compress the scope to trunk’, ’shaped soft robots and appraise the status of soft robots and their distance from clinical use. Several papers related to the construction and capabilities of soft robots were referenced. Roughly 190 related articles published in the current period from 2018 to the publication date (representing almost 90% of the references to the theme totally identified) were reviewed. Structure of soft robots, advances in technology, and the aptitudes in medical applications were discussed. The trunk’, ’like soft robots conspicuously are proposed for applications including robot assisted surgery where a probe is inserted into the human body. Such robots are also present in other medical robots as actuators. The literature shows that different methods are used to fabricate soft robots and employ them in different robotics tasks including positioning, grasping, and force exertion. Noticeably, such studies were done in robotics laboratories, dealing with robotics engineering problems. This review suggests that the technology is actively developing, but further focus on specific medical applications is required to fill the gap between soft robotics and its clinical use.

NU-Biped-4.5: A Lightweight and Low-Prototyping-Cost Full-Size Bipedal Robot

This paper presents the design of a new lightweight, full-size bipedal robot developed in the Humanoid Robotics Laboratory at Nazarbayev University. The robot, equipped with 12 degrees of freedom (DOFs), stands at 1.1 m tall and weighs only 15 kg (excluding the battery). Through the implementation of a simple mechanical design and the utilization of off-the-shelf components, the overall prototype cost remained under USD 5000. The incorporation of high-performance in-house-developed servomotors enables the robot’s actuation system to generate up to 2400 W of mechanical power, resulting in a power-to-weight ratio of 160 W/kg. The details of the mechanical and electrical design are presented alongside the formalization of the forward kinematic model using the successive screw displacement method and the solution of the inverse kinematics. Tests conducted in both a simulation environment and on the real prototype demonstrate that the robot is capable of accurately following the reference joint trajectories to execute a quasi-static gait, achieving an average power consumption of 496 W.

Ethorobotic rats for rodent behavioral research: design considerations.

The development of robots as tools for biological research, sometimes termed "biorobotics", has grown rapidly in recent years, fueled by the proliferation of miniaturized computation and advanced manufacturing techniques. Much of this work is focused on the use of robots as biomechanical models for natural systems. But, increasingly, biomimetic robots are being employed to interact directly with animals, as component parts of ethology studies in the field and behavioral neuroscience studies in the laboratory. While it has been possible to mechanize and automate animal behavior experiments for decades, only recently has there been the prospect of creating at-scale robotic animals containing the sensing, autonomy and actuation necessary for complex, life-like interaction. This not only opens up new avenues of enquiry, but also provides important ways to improve animal welfare, both by reducing or replacing the use of animal subjects, and by minimizing animal distress (if robots are used judiciously). This article will discuss the current state of the art in robotic lab rats, providing perspective on where research could be directed to enable the safe and effective use of biorobotic animals.

Collaborative robotics and adaptive machines laboratory – research advances in autonomous systems, advanced manufacturing, and healthcare

This report summarizes the recent research projects carried out at the Collaborative Robotics and Adaptive Machines Laboratory, Old Dominion University. The topics include collaborative robotics strategies for non-repetitive micro-drilling tasks, hand-held robots for spillage-free specimen retrieval tasks, development of a three-dimensional simulator for autonomous underwater vehicles, and enhancing search efficiency of glowworm-inspired robotic swarms for multiple source localization tasks. Research in these areas has applications in autonomous systems, advanced manufacturing, and health care.

A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films

Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.

Control and evaluation of a humanoid robot with rolling contact joints on its lower body.

In this paper, we introduce a new teen-sized humanoid platform dubbed DRACO 3, custom-built by Apptronik and altered for practical use by the Human Centered Robotics Laboratory at The University of Texas at Austin. The form factor of DRACO 3 is such that it can operate safely in human environments while reaching objects at human heights. To approximate the range of motion of humans, this robot features proximal actuation and mechanical artifacts to provide a high range of hip, knee, and ankle motions. In particular, rolling contact mechanisms on the lower body are incorporated using a proximal actuation principle to provide an extensive vertical pose workspace. To enable DRACO 3 to perform dexterous tasks while dealing with these complex transmissions, we introduce a novel whole-body controller (WBC) incorporating internal constraints to model the rolling motion behavior. In addition, details of our WBC for DRACO 3 are presented with an emphasis on practical points for hardware implementation. We perform a design analysis of DRACO 3, as well as empirical evaluations under the lens of the Centroidal Inertia Isotropy (CII) design metric. Lastly, we experimentally validate our design and controller by testing center of mass (CoM) balancing, one-leg balancing, and stepping-in-place behaviors.

Remote Operation of Marine Robotic Systems and Next-Generation Multi-Purpose Control Rooms

Since 2017, NTNU’s Applied Underwater Robotics Laboratory has been developing an infrastructure for remote marine/subsea operations in Trondheim Fjord. The infrastructure, named the OceanLab subsea node, allows remote experimentation for three groups of assets: seabed infrastructure, surface or subsea vehicles/robots, and assets at remote experimentation sites. To achieve this task, a shoreside control room serves as a hub that enables efficient and diverse communication with assets in the field as well as with remote participants/operators. Remote experimentation has become more popular in recent years due to technological developments and convenience, the COVID-19 pandemic, and travel restrictions that were imposed. This situation has shown us that physical presence at the experimentation site is not necessarily the only option. Sharing of the infrastructure among different experts, which are geographically distributed, but participating in a single, local, real-time experiment, increases the level of expertise available and the efficiency of the operations. This paper also elaborates on the development of a virtual experimentation environment that includes simulators and digital twins of various marine vehicles, infrastructures, and the operational marine environment. By leveraging remote and virtual experimentation technologies, users and experts can achieve relevant results in a shorter time frame and at a reduced cost.

Transformer-Based Imitative Reinforcement Learning for Multirobot Path Planning

Multi-robot path planning leads multiple robots from start positions to designated goal positions by generating efficient and collision-free paths. Multi-robot systems realize coordination solutions and decentralized path planning, which is essential for large-scale systems. The state-of-the-art decentralized methods utilize imitation learning and reinforcement learning methods to teach fully-decentralized policies, dramatically improving their performance. However, these methods cannot enable robots to perform tasks efficiently in relatively dense environments without communication between robots. We introduce the transformer structure into policy neural networks for the first time, dramatically enhancing the ability of policy neural networks to extract features that facilitate collaboration between robots. It mainly focuses on improving the performance of policies in relatively dense multi-robot environments under conditions where robots do not communicate with each other. Furthermore, a novel imitation reinforcement learning framework is proposed by combining contrastive learning and double deep Q-network to solve the problem of difficulty training policy neural networks after introducing the transformer structure. We present results in the simulation environment and compare the resulting policy against advanced multi-robot path planning methods in terms of success rate. Simulation results show that our policy achieves state-of-the-art performance when there is no communication between robots. Finally, we experimented with a real-world case using a total of three robots in our robotic laboratory.

AI Path Prediction & Planning for Autonomous Vehicles

Autonomous navigation of vehicles at various places can tremendously improve daily traversal, especially while driving in unfamiliar territory. Path-planning systems have been continuously improved to cope with increasingly difficult issues and advances in computing technology. One example is the rise of sampling-based planners, which made difficult planning issues easier to solve. As a result, problems that were previously thought difficult can now be addressed, and evendynamical constraints can be overcome. The ongoing modifications and study in path planning are aimed at achieving robotic systems' fullautonomy. By relying solely on path planning approaches, this goal has not been fully realized. The majority of path-planning algorithms work well in simulations and laboratory robots, but real- world robots operate in unknown surroundings and interact with other agents whose motion is often unpredictable and whose conduct can be uncooperative. Real robots have inherent kinematical and dynamical (kinodynamical) constraints that make autonomous collision-free motion impossible in particular circumstances. This research demonstrates the implementation, testing, and comparison of two path-planning algorithms for autonomous navigation of a vehicle/rover, namely rapidly-exploring random trees (RRT) and second probabilistic roadmap (PRM). Using random samples from the search space, an RRT generates a tree rooted at the beginning configuration. As each sample is drawn, a link is made between it and the state closest to it in the tree. This results in the adding of the new state to the tree if the link is viable (goes wholly acrossfree space and obeys all constraints). The main idea underlying PRM, on the other hand, is to pick random samples from the robot's configuration space, test them to see whether they are in the free space, and then use a local planner to connect these configurations to other adjacent configurations.The beginning and goal configurations are brought in, and the resulting graph is subjected to a graph search algorithm in order to find a path between them. To assess the performance of the recommended approaches, simulations and experiments are carried out.

Common Components of General Surgery Robotic Educational Programs

Robotically assisted surgery has become more common in general surgery, but there is limited guidance from the Accreditation Council for Graduate Medical Education (ACGME) regarding this type of training. We sought to determine common elements and differences in the robotic educational curricula developed by general surgery residency programs. Robotic educational curricula were obtained from the 7 individuals who presented at the workshop, "Robotic Education in General Surgery" at the 2023 Association of Program Directors in Surgery annual meeting. All 7 general surgery programs had training beginning intern year, required online robotic modules, had at least 1 dedicated simulation training console not used for clinical purposes, and ran dry and wet (tissue) robotic labs at least annually. All programs had bedside and console surgeon case minimums and had administrative support to run the educational programs. Differences existed regarding how training intern year was executed, the simulations required, clinical practice minimum requirements, how progress was monitored over time, and how case numbers were tracked. Some programs had salary support for a director of robotic education. There are several common elements to robotic educational curricula in general surgery, however significant variation does exist between programs. Given the frequency of robotic use in general surgery and current lack of standardization, formal guidance from the ACGME specifically regarding robotic education in general surgery residency is warranted.

A GSM-Based Intelligent Monitoring System for Improving Robotics Laboratory Safety

Incorporating industrial robots into current robotics curricula has become a key component for engineering and technology programs. Recently, Toyota Motor Engineering & Manufacturing North America donated a Kawasaki ZB150S industrial robot to the Engineering Technology Programs at Northern Kentucky University. The entire Kawasaki industrial robot system was installed in a safeguard cage; however, the safeguard cage was not always able to prevent unauthorized students from coming into the working area of the robot. The lab manager/staff expressed an urgent need for a smart tool that could monitor occurrences of unauthorized operating conditions to help them improve laboratory safety. Thus, a Global System for Mobile (GSM)-based intelligent monitoring system has been designed and developed to provide a safe robotics lab operating environment for students. This intelligent monitoring system was divided into two subsystems: one was the Embedded Laboratory Environmental Monitoring and Robot Power Control (ELEMRPC) subsystem; the other was the GSM communication subsystem. The ELEMRPC subsystem received the presence-sensing signals from the light curtains at the cage entrance and disarmed the power supply of the robot when unauthorized students were detected. Additionally, the lab environmental parameters were monitored. The GSM subsystem wirelessly transmitted text messages (e.g., lab safety information) to designated cellular phones. These text notifications can alert lab manager/staff immediately to prevent potential accidents within the footprint of the robot safety cage. This paper discusses in greater detail the design, development, and hardware implementation of the robotics laboratory safety monitoring system.

Status and noise reduction analysis of robotics lab in an academic institute by using acoustic materials

In recent decades, noise has emerged as one of the most annoying and tiring factors in working environments, be it in factory, workshop, academic or research setup. This paper presents the noise status and noise reduction analysis of Robotics lab in an academic institute. The average equivalent continuous noise level ([Formula: see text]) inside the Robotics lab (64.90–75.99 dB) was within or close to permissible limits of 75 dB as per Indian standards for industrial environment; but the operation of Mig and Spot welding were a source of noise nuisance to the adjoining academic area due to the sound transmission from the Robotics lab. The noise reduction analysis in the study demonstrated the usage of acoustics (glass, curtain, cardboard and thermocol) in windows as sound barriers that dampens the transmission of indoor noise. The Sound Transmission Loss (STL) was observed to be highest in the range of 18.12–22.62 dB (28.88–29.59%) by using the combination of glass and curtain as acoustic barrier in windows of the Robotics lab; thereby, reducing noise nuisance significantly in the academic area. The study revealed the necessity of effective acoustic barrier in labs/workshops under-industrial conditions in academic institutes and proposed a model for predicting STL for the combination of glass and curtain from [Formula: see text] value.

A Mobile Laboratory Robot for Various and Precise Measurements of Crops and Soil in Agricultural Fields: Development and Pilot Study

Localized management based on multipoint measurements of crops and soil is expected to improve agricultural productivity. The difficulties with this process are as follows: it is time-consuming due to the many measurement points; it requires various sensors for various measurements; it can lead to unstable measurements due to sunlight and wind. To solve the above issues, the system we propose has the advantages of efficient measurements performed by a robot, various measurements performed through exchangeable sensor units, and stable measurements through protecting the crop from sunlight and wind. As a pilot study for the system, we conducted an experiment to simultaneously measure the crops and soil in a cabbage field. The robot achieved mobility in the field, >4 h of operation time, and the ability to obtain soil electrical conductivity measurements and crop imaging at 100 points. Furthermore, the stability of the measurement conditions within the sensor unit during the experiment was evaluated. Compared to the case without the covering, the illuminance became 280-fold stabler (standard deviation = 0.4 lx), and the wind-induced crop shaking became 20-fold lower (root mean square error of the image pixels = 0.5%). The potential impacts of this research include high reproducibility because of the shareable sensor unit and the expectation of new discoveries using precise indoor sensors.

Affective Corners as a Problematic for Design Interactions

Domestic robots are already commonplace in many homes, while humanoid companion robots like Pepper are increasingly becoming part of different kinds of care work. Drawing on fieldwork at a robotics lab, as well as our personal encounters with domestic robots, we use here the metaphor of “hard-to-reach corners” to explore the socio-technical limitations of companion robots and our differing abilities to respond to these limitations. This article presents “hard-to-reach-corners” as a problematic for design interaction, offering them as an opportunity for thinking about context and intersectional aspects of adaptation.

A Virtual Reality Laboratory for Blended Learning Education: Design, Implementation and Evaluation

Launched during the pandemic, the EU-funded JANUS project aimed to ensure the continuity of student workshops at universities using a virtual reality (VR) robotics laboratory. With the return to normality, the project has been redesigned to capitalise on the positive outcomes of the experience. The VR lab provides safe and unrestricted access to the labs and experiments with the machines, reducing the consequences of student mistakes and improving the user experience by allowing the experiment to be repeated from different angles, some of which are impossible to access in the real lab. In addition, integration with an interactive learning platform called “ViLLE” allows for continuous assessment of the learning experience. Self-evaluation of the material taught and learned can be integrated with the execution of the exercises that pave the way for Kaizen. Two VR workshops for the blended learning of robotics were developed during the JANUS project. Their evaluation reported favourable responses from the students whose learning performance was indirectly measured.

Impact of Life Cycle Costing in Procurement of Robotic Track-Based Central Laboratory at Apex Medical Institute in India.

Introduction Life cycle costing is an important management tool that takes into account the implications of planning, acquiring, operating, maintaining, and disposing of an asset during its complete life cycle. A major hindrance to the procurement of expensive equipment in developing countries is the lack of a reliable framework combining and integrating all the equipment life cycle aspects into procurement process. Methods The study was conducted from the data collected from the bids that were received for procurement of two robotic track-based central laboratories which were installed at All India Institute of Medical Sciences (AIIMS), New Delhi. The procurement was done as per the guidelines laid down under General Finance Rules (GFR) 2017 following the two bid systems: technical bid and price/commercial bid. Results A complete financial analysis of the robotic laboratory was done that involved gathering of all the pertinent financial information into one place and then using that data to analyze the feasibility of the bid. The life cycle costs of both the labs were calculated by assuming the life of equipment as 10 years and by factoring in cost of equipment including 5-year warranty, comprehensive maintenance from years 6 to 10, indicative cost of all reagents for 10 years, and indicative cost of all other consumables for 10 years. Conclusion Results showed that the cost of equipment alone should not be the sole predictor of making purchase decisions of equipment. Further research may additionally explore differences between processes being followed in government versus private organizations, as well as national guidelines and subnational practices.

BLE beacon-based floor detection for mobile robots in a multi-floor automation laboratory

Abstract As an important task of multi-floor localization, floor detection has elicited great attention. Wireless infrastructures like Wi-Fi and Bluetooth Low Energy (BLE) play important roles in floor detection. However, most floor detection research tends to focus on data modelling but pays little attention to the data collection system, which is the basis of wireless infrastructure-based floor detection. In fact, the floor detection task can be greatly simplified with proper data collection system design. In this paper, a floor detection solution is developed in a multi-floor life science automation lab. A data collection system consisting of BLE beacons, a receiver node and an Internet of Things (IoT) cloud is provided. The features of the BLE beacon under different settings are evaluated in detail. A mean filter is designed to deal with the fluctuation of the received signal strength indicator data. A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection tests. The time delay and floor detection accuracy under different settings are discussed. Finally, floor detection is evaluated on the H20 multi-floor transportation robot. Two sensor nodes are installed on the robot at different heights. The floor detection performance with different installation heights is discussed. The experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5 s.

BacterAI maps microbial metabolism without prior knowledge.

Training artificial intelligence (AI) systems to perform autonomous experiments would vastly increase the throughput of microbiology; however, few microbes have large enough datasets for training such a system. In the present study, we introduce BacterAI, an automated science platform that maps microbial metabolism but requires no prior knowledge. BacterAI learns by converting scientific questions into simple games that it plays with laboratory robots. The agent then distils its findings into logical rules that can be interpreted by human scientists. We use BacterAI to learn the amino acid requirements for two oral streptococci: Streptococcus gordonii and Streptococcus sanguinis. We then show how transfer learning can accelerate BacterAI when investigating new environments or larger media with up to 39 ingredients. Scientific gameplay and BacterAI enable the unbiased, autonomous study of organisms for which no training data exist.

Adaptive chaos control of a humanoid robot arm: a fault-tolerant scheme

Abstract. Safety in human–robot physical interaction and cooperation is of paramount interest. In this work, a human–humanoid interaction and cooperation scenario is considered. The robot arm is controlled by a proportional derivative (PD) controller in combination with an inherently fault-tolerant sliding-mode controller. During normal operation, if any of the joints of the robot arm develops a fault, the robot arm end effector may go into chaotic and dangerous motion. If such a scenario occurs, it poses a serious danger to the human in the loop as well as the robot. In this paper, an adaptive chaos sliding-mode controller is proposed to recover the tracking of the end effector when a fault occurs in any of the actuators. This tracking restoration is very important to complete a safety-critical task. The proposed scheme may help in addressing some safety issues arising from a joint failure, allowing it to finish the task at hand and stop it from going into a dangerous situation. The scheme is tested in simulation on the four degrees of freedom (DOF) model of the Bristol Robotics Laboratory (BRL) robot arm. Simulation results show efficient tracking recovery after a joint actuator failure. The controller demonstrated good performance in terms of tracking and stability when dealing with different joint actuator failures.

AI-driven robotics lab joins the hunt for materials breakthroughs

Setup is the first fully automated effort seeking novel inorganic materials for emerging technologies.