Robotic Modules Research Articles

Positioning accuracy analysis for an electromechanical coupled robotic joint module under uncertainties

Positioning accuracy analysis for an electromechanical coupled robotic joint module under uncertainties

MINDS OF THE FUTURE: ENTREPRENEURIAL, INNOVATIVE AND TECHNOLOGICAL EDUCATION

This article presents partial results of a Brazilian entrepreneurial education project named Minds of the Future. This project is aimed at low-income high school students from public schools in the city of Juiz de Fora, Minas Gerais State, Brazil. Developed through a collaboration between Federal University of Juiz de Fora and Minas Gerais Education Department, the project aims to contribute for a transformative education, focused on training young people to cope with new life realities, social relationships, and work through the stimulation of critical, creative, technological and entrepreneurial thinking. This project is justified both by the need for young people's engagement in the rapid advances in systems and data automation and by the educational reality of the country, which has a high dropout rate during the transition between elementary and high school.The proposed approach to knowledge transfer and sharing is STEAM. In the first stage, Lego® Education robotics materials were used, which combine Lego® construction elements and intuitive programming language in a 12-hour module of lessons and playful activities offered to 135 students distributed in eight classes during 2024. This and other modules involving 3D modeling, soft skills, financial education and sustainable development will be offered in the second semester of 2024. The project includes professors from different areas and 14 scholarship holders from different undergraduate courses at the Federal University of Juiz de Fora - physics, engineering, computer science, business administration, accounting, law, nutrition and pedagogy - who acted as facilitators during the classes and activities developed. To evaluate the results of this initial experience with the application of the Lego programming and robotics module, a qualitative and quantitative research was conducted. Questionnaires with objective questions were administered to the students via Google Forms at the beginning of the first class and at the end of the last class of the module. Additionally, questionnaires with both objective and open-ended questions were given to the project scholarship holders. Minds of the Future project demonstrated significant positive impacts both among the students and the participating scholarship holders perceptions and skills. The results indicate improvements in various dimensions, from the relationship with teachers to the development of technical skills and increased interest in technology and innovation.

Evaluation Method and Performance Indices for the Motion/Force Constrainability of Overconstrained Parallel Manipulators Without Actuation Redundancy

Abstract Overconstrained parallel manipulators (PMs) without actuation redundancy have been widely used in various applications, such as the parallel module of the Exechon robot for workpiece machining. However, existing methods are inadequate for describing the motion/force constraint performance of nonredundant and overconstrained PMs (NOPMs) because they do not reasonably consider the influence of the overconstrained structure. This performance is crucial for evaluating the capability to resist external loads, which is of great significance for applications requiring high stiffness and precision. To address this issue, this paper proposes a new evaluation method for the motion/force constrainability of NOPMs. First, based on the four wrench and twist systems of limbs, the output constraint performances of all the single-degree-of-freedom (DOF) mechanisms under different cases can be obtained. These single-DOF mechanisms are constructed by selectively “losing” some constraint wrench screws and “locking” all the transmission wrench screws of the original overconstrained PM, which is the key step in the establishment of this method and achieved using the ergodic method. Then, the output and input constraint performance indices in a certain configuration can be calculated, followed by the definition of a local constraint index to evaluate the motion/force constrainability of the NOPM. Notably, all the proposed indices are characterized by coordinate independence. Finally, two PMs and their corresponding overconstrained structures are simulated to demonstrate the correctness of the proposed evaluation method. The evaluation strategy can also be used for the optimal design of the NOPMs in the future.

Benchmarking neural radiance fields for autonomous robots: An overview

Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene representation, offering high-fidelity renderings and reconstructions from a set of sparse and unstructured sensor data. In the context of autonomous robotics, where perception and understanding of the environment are pivotal, NeRF holds immense promise for improving performance. However, few survey has discussed such a potential. To fill this gap, we have collected over 200 papers since the publication of original NeRF in 2020 and present a thorough analysis of how NeRF can be used to enhance the capabilities of autonomous robots. We especially focus on the perception, localization and navigation, and decision-making modules of autonomous robots and delve into tasks crucial for autonomous operation, including 3-dimensional reconstruction, segmentation, pose estimation, simultaneous localization and mapping, navigation and planning, and interaction. Our survey meticulously benchmarks existing NeRF-based methods, comparing their reported performance, and providing insights into their strengths and limitations. Moreover, we target the existing challenges of applying NeRF in autonomous robots, including real-time processing, sparse input views, and explore promising avenues for future research and development in this domain. We especially discuss potential of integrating advanced deep learning techniques like 3-dimensional Gaussian splatting, large language models, and generative artificial intelligence. This survey serves as a roadmap for researchers seeking to leverage NeRF to empower autonomous robots, paving the way for innovative solutions that can navigate and interact seamlessly in complex environments.

Analysis of solid-state NMR data facilitated by MagRO_NMRViewJ with Graph_Robot: Application for membrane protein and amyloid.

Solid-state NMR (ssNMR) methods have continued to be developed in recent years for the efficient assignment of signals and 3D structure modeling of biomacromolecules. Consequently, we are approaching an era in which vigorous applications of these methods are more widespread in research, including functional elucidation of biomacromolecules and drug discovery. However, multidimensional ssNMR methods are not as advanced as solution NMR methods, especially for automated data analysis. This article describes how a newly developed Graph_Robot module, implemented in MagRO-NMRViewJ, evolved from integrated tools for NMR data analysis named Kujira (developed by Kobayashi et al. [1]). These packaged tools systematically utilize flexible, sophisticated, yet simple libraries that facilitate only for solution-NMR data analysis, offering an intuitive interface accessible even to novice users. In this study, semi-automated assignments of backbone and side chain signals of ssNMR datasets for uniformly 13C/15N labeled aquaporin Z and 42-residue amyloid-β fibril were examined as examples to demonstrate how Graph_Robot can expedite the visual inspection and handling of multidimensional ssNMR spectral data. In addition, the functionality of the Graph_Robot system enables a computer to interpret the behavior of magnetization transfer based on a finite automaton model.

A fabrication strategy for millimeter-scale, self-sensing soft-rigid hybrid robots.

Soft robots typically involve manual assembly of core hardware components like actuators, sensors, and controllers. This increases fabrication time and reduces consistency, especially in small-scale soft robots. We present a scalable monolithic fabrication method for millimeter-scale soft-rigid hybrid robots, simplifying the integration of core hardware components. Actuation is provided by soft-foldable polytetrafluoroethylene film-based actuators powered by ionic fluid injection. The desired motion is encoded by integrating a mechanical controller, comprised of rigid-flexible materials. The robot's motion can be self-sensed using an ionic resistive sensor by detecting electrical resistance changes across its body. Our approach is demonstrated by fabricating three distinct soft-rigid hybrid robotic modules, each with unique degrees of freedom: translational, bending, and roto-translational motions. These modules connect to form a soft-rigid hybrid continuum robot with real-time shape-sensing capabilities. We showcase the robot's capabilities by performing object pick-and-place, needle steering and tissue puncturing, and optical fiber steering tasks.

Hexagonal electrohydraulic modules for rapidly reconfigurable high-speed robots.

Robots made from reconfigurable modular units feature versatility, cost efficiency, and improved sustainability compared with fixed designs. Reconfigurable modules driven by soft actuators provide adaptable actuation, safe interaction, and wide design freedom, but existing soft modules would benefit from high-speed and high-strain actuation, as well as driving methods well-suited to untethered operation. Here, we introduce a class of electrically actuated robotic modules that provide high-speed (a peak contractile strain rate of 4618% per second, 15.8-hertz bandwidth, and a peak specific power of 122 watts per kilogram), high-strain (49% contraction) actuation and that use magnets for reversible mechanical and electrical connections between neighboring modules, thereby serving as building blocks for rapidly reconfigurable and highly agile robotic systems. The actuation performance of each hexagonal electrohydraulic (HEXEL) module is enabled by a synergistic combination of soft and rigid components; a hexagonal exoskeleton of rigid plates amplifies the motion produced by soft electrohydraulic actuators and provides a mechanical structure and connection platform for reconfigurable robots composed of many modules. We characterize the actuation performance of individual HEXEL modules, present a model that captures their quasi-static force-stroke behavior, and demonstrate both a high-jumping and a fast pipe-crawling robot. Using embedded magnetic connections, we arranged multiple modules into reconfigurable robots with diverse functionality, including a high-stroke muscle, a multimodal active array, a table-top active platform, and a fast-rolling robot. We further leveraged the magnetic connections for hosting untethered, snap-on driving electronics, together highlighting the promise of HEXEL modules for creating rapidly reconfigurable high-speed robots.

Design and Validation of an Ambulatory User Support Gait Rehabilitation Robot: NIMBLE

Relearning to walk requires progressive training in real scenarios—overground—along with assistance in basic tasks, such as balancing. In addition, user ability must be maximized through compliant robotic assistance as needed. Despite decades of research, gait rehabilitation robotic devices yield controversial results. This article presents the conceptual design of a novel walking assistance and rehabilitation robot, the NIMBLE robot, aimed at providing ambulatory, bodyweight-supported gait training, assisting the user’s center of mass trajectory to aid weight transfer and dynamic balance during walking. NIMBLE consists of a robotic mobile frame, a partial bodyweight support (PBWS) system, an ambulatory lower-limb exoskeleton (Exo-H3) and a cable-driven pelvis-assisting robot. Designed as a modular structure, it differentiates hierarchical communication levels through a Robot Operating System (ROS) 2 network. We present the mechatronic design and experimental results assessing the impact of the mechatronic coupling between the robotic modules on the walking kinematics and the frame movement control performance. The robotic frame hardly affects the walking kinematics up to 2 degrees in both the sagittal and frontal planes, making it feasible for lateral balance and weight translation training. Moreover, it successfully tracks and follows user trajectories. The NIMBLE robotic frame assessment shows promising results for ambulatory gait rehabilitation.

Development of a novel amphibious robot with a reusable cycloidal propulsion module

This paper proposes a novel amphibious robot with reusable cycloidal propulsion module, and conducts simulation and experimental studies on its propulsion performance. Underwater, the robot adopts the collaboration of multiple cycloidal propellers to achieve moving and turning, providing good maneuverability; on land, the four blades of each cycloidal propeller are used as the blade wheel to drive the robot to move and turn, thus realizing the reuse of the same propulsion module in two environments. The experimental results show that the robot's underwater movement speed coefficient reaches a maximum of 0.275; by changing the direction of the propellers on both sides, the robot can realize in-situ turning, and the turning speed increases with the increase of the revolution speed of the propeller. The robot multiplexes the cycloid propellers into blade wheels on land, moves with a maximum speed coefficient of 0.207, and realizes the functions of mobile turning and in-situ turning, respectively, by means of the speed difference between the blade wheels on both sides. The findings confirm the feasibility of the cycloid propeller as a reusable propulsion module for amphibious robots, and the developed robot has good maneuverability, which is promising for application in the field of amphibious operations.

A dual-instrument Kalman-based tracker to enhance robustness of microsurgical tools tracking

Purpose:The integration of a surgical robotic instrument tracking module within optical microscopes holds the potential to advance microsurgery practices, as it facilitates automated camera movements, thereby augmenting the surgeon’s capability in executing surgical procedures.Methods:In the present work, an innovative detection backbone based on spatial attention module is implemented to enhance the detection accuracy of small objects within the image. Additionally, we have introduced a robust data association technique, capable to re-track surgical instrument, mainly based on the knowledge of the dual-instrument robotics system, Intersection over Union metric and Kalman filter.Results:The effectiveness of this pipeline was evaluated through testing on a dataset comprising ten manually annotated videos of anastomosis procedures involving either animal or phantom vessels, exploiting the Symani®Surgical System—a dedicated robotic platform designed for microsurgery. The multiple object tracking precision (MOTP) and the multiple object tracking accuracy (MOTA) are used to evaluate the performance of the proposed approach, and a new metric is computed to demonstrate the efficacy in stabilizing the tracking result along the video frames. An average MOTP of 74±0.06% and a MOTA of 99±0.03% over the test videos were found.Conclusion:These results confirm the potential of the proposed approach in enhancing precision and reliability in microsurgical instrument tracking. Thus, the integration of attention mechanisms and a tailored data association module could be a solid base for automatizing the motion of optical microscopes.

Pushing with Soft Robotic Arms via Deep Reinforcement Learning

Soft robots can adaptively interact with unstructured environments. However, nonlinear soft material properties challenge modeling and control. Learning‐based controllers that leverage efficient mechanical models are promising for solving complex interaction tasks. This article develops a closed‐loop pose/force controller for a dexterous soft manipulator enabling dynamic pushing tasks using deep reinforcement learning. Force tests investigate the mechanical properties of a soft robot module, resulting in orthogonal forces of N. Then, the policy is trained in simulation leveraging a dynamic Cosserat rod model of the soft robot. Domain randomization mitigate the sim‐to‐real gap while careful reward engineering induced pose and force control even without explicit force inputs. Despite the approximate simulation, the sim‐to‐real transfer achieved an average reaching distance of mm (), an average orientation error of rad () and applied pushing forces up to N. Such performance is reasonable for the intended assistive tasks of the manipulator. The experiments uncovered that the soft robot interacting with the environment exhibited torsional and counter‐balancing movements. Although not explicitly enforced, they emerged from the mechanical intelligence of the manipulator. The results demonstrate the potential of soft robotic manipulation via reinforcement learning.

End-to-end, real time and robust behavioral prediction module with ROS for autonomous vehicles

In the world where urbanization and population density are increasing, transportation methods are also diversifying and the use of unmanned vehicles is becoming widespread. In order for unmanned vehicles to perform their tasks autonomously, they need to be able to perceive their own position, the environment and predict the possible movements/routes of environmental factors, similar to living things. In autonomous vehicles, it is extremely important for the safety of the vehicle and the surrounding factors to be able to predict the future position of the objects around it with high performance so that the vehicle can plan correctly. Due to the stated reasons, the behavioral prediction module is a very important component for autonomous vehicles, especially in moving environments. In this study, fast and successful robotic behavioral prediction module has been developed to enable the autonomous vehicle to plan more safely and successfully.

Self-Amputating and Interfusing Machines.

Biological organisms exhibit phenomenal adaptation through morphology-shifting mechanisms including self-amputation, regeneration, and collective behavior. For example, reptiles, crustaceans, and insects amputate their own appendages in response to threats. Temporary fusion between individuals enables collective behaviors, such as in ants that temporarily fuse to build bridges. The concept of morphological editing often involves the addition and subtraction of mass and can be linked to modular robotics, wherein synthetic body morphology may be revised by rearranging parts. This work describes a reversible cohesive interface made of thermoplastic elastomer that allows for strong attachment and easy detachment of distributed soft robot modules without direct human handling. The reversible joint boasts a modulus similar to materials commonly used in soft robotics, and can thus be distributed throughout soft robot bodies without introducing mechanical incongruities. To demonstrate utility, the reversible joint is implemented in two embodiments: a soft quadruped robot that self-amputates a limb when stuck, and a cluster of three soft-crawling robots that fuse to cross a land gap. This work points toward future robots capable of radical shape-shifting via changes in mass through autotomy and interfusion, as well as highlights the crucial role that interfacial stiffness change plays in autotomizable biological and artificial systems.

A Biomechanics-Aware Robot-Assisted Steerable Drilling Framework for Minimally Invasive Spinal Fixation Procedures.

In this paper, we propose a novel biomechanics-aware robot-assisted steerable drilling framework with the goal of addressing common complications of spinal fixation procedures occurring due to the rigidity of drilling instruments and implants. This framework is composed of two main unique modules to design a robotic system including (i) a Patient-Specific Biomechanics-aware Trajectory Selection Module used to analyze the stress and strain distribution along an implanted pedicle screw in a generic drilling trajectory (linear and/or curved) and obtain an optimal trajectory; and (ii) a complementary semi-autonomous robotic drilling module that consists of a novel Concentric Tube Steerable Drilling Robot (CT-SDR) integrated with a seven degree-of-freedom robotic manipulator. This semi-autonomous robot-assisted steerable drilling system follows a multi-step drilling procedure to accurately and reliably execute the optimal hybrid drilling trajectory (HDT) obtained by the Trajectory Selection Module. Performance of the proposed framework has been thoroughly analyzed on simulated bone materials by drilling various trajectories obtained from the finite element-based Selection Module using Quantitative Computed Tomography (QCT) scans of a real patient's vertebra.

Autonomous Alignment and Docking Control for a Self-Reconfigurable Modular Mobile Robotic System

This paper presents the path planning and motion control of a self-reconfigurable mobile robot system, focusing on module-to-module autonomous docking and alignment tasks. STORM, which stands for Self-configurable and Transformable Omni-Directional Robotic Modules, features a unique mode-switching ability and novel docking mechanism design. This enables the modules that make up STORM to dock with each other and form a variety configurations in or to perform a large array of tasks. The path planning and motion control presented here consists of two parallel schemes. A Lyapunov function-based precision controller is proposed to align the target docking mechanisms in a small range of the target position. Then, an optimization-based path planning algorithm is proposed to help find the fastest path and determine when to switch its locomotion mode in a much larger range. Both numerical simulations and real-world experiments were carried out to validate these proposed controllers.

Static characteristics of a biomimetic robot module driven by water hydraulic artificial muscles resisted with helical spring

Soft modular biomimetic robots, driven by flexible actuators, are extensively used in various fields due to their excellent flexibility, environmental adaptability, and isomorphism. However, existing flexible modules typically possess no more than two degrees of freedom for structural limitations. This paper proposes a biomimetic robot module supported by a spring and actuated by water hydraulic artificial muscles (WHAMs). Combining the mechanical properties of WHAMs and spring, the static model of the soft module was established by force balance method. Moreover, the contraction force coefficient and contraction ratio coefficient of WHAMs were identified through the static characteristics test. The rod length coefficient and stress coefficient of the spring were also identified through the spring bending test. The errors between the experimental results and the model are analyzed and discussed, uncovering the main reasons. The influence of spring pre-compression, spring elasticity coefficient, initial working pressure of WHAM, and the installation distance of WHAM on the motion space of module were analyzed and discussed. This work serves as a guide for the structural design of flexible modules to meet specific requirements and lays the foundation for future research on integrated flexible biomimetic robots driven by WHAMs.

Modular multi-degree-of-freedom soft origami robots with reprogrammable electrothermal actuation

Soft robots often draw inspiration from nature to navigate different environments. Although the inching motion and crawling motion of caterpillars have been widely studied in the design of soft robots, the steering motion with local bending control remains challenging. To address this challenge, we explore modular origami units which constitute building blocks for mimicking the segmented caterpillar body. Based on this concept, we report a modular soft Kresling origami crawling robot enabled by electrothermal actuation. A compact and lightweight Kresling structure is designed, fabricated, and characterized with integrated thermal bimorph actuators consisting of liquid crystal elastomer and polyimide layers. With the modular design and reprogrammable actuation, a multiunit caterpillar-inspired soft robot composed of both active units and passive units is developed for bidirectional locomotion and steering locomotion with precise curvature control. We demonstrate the modular design of the Kresling origami robot with an active robotic module picking up cargo and assembling with another robotic module to achieve a steering function. The concept of modular soft robots can provide insight into future soft robots that can grow, repair, and enhance functionality.

ИСПОЛЬЗОВАНИЕ РОБОТ-АССИСТИРОВАННОЙ ТОРАКОСКОПИЧЕСКОЙ ХИРУРГИИ У РЕБЕНКА 9 ЛЕТ С КИСТОЗНОЙ ФОРМОЙ УДВОЕНИЯ ПИЩЕВОДА

Purpose of this research was to share the Russia’s first clinical experience in the use of robot for thoracoscopic removal of cystic esophageal duplication and to explore whether robot could be involved in further thoracic esophageal surgeries. Authors represent a report of a clinical case of robot-assisted surgical treatment of a cystic tumor of the esophagus in a patient aged 9 y/o using the Versius Surgical Robot. The procedure was performed using three robotic modules - optical (10 mm, 3D visualization) and the two instrumental (5 mm each). An additional 5 mm port was used to deliver suture material and non-robotic instruments. Two-lung traditional ventilation was used during the surgical intervention. Aspiration of the cyst before final resection facilitated thoracoscopic visualization and removal of the specimen without the need for a mini-thoracotomy. The surgical intervention consisted of a longitudinal dissection of the muscular layer of the esophagus and enucleation of the cystic formation followed by covering of the muscular defect of the esophagus in order to ensure the integrity of its lumen. The total intervention duration was 155 minutes, of which the duration of robot installation (docking time) was 15 minutes and the duration of the main stage of the procedure was 140 minutes. No intraoperative or postoperative complications were recorded. Histological examination demonstrated the signs of the cystic form of duplication of the esophagus with the presence of ectopic gastric mucosa in its structure. The patient stayed in the hospital for seven days; there was no relapse of the disease observed during the entire observation period and therefore no repeated interventions were required. The subsequent control fibroesophagogastroscopy demonstrated a smooth profile of the esophageal mucosa without signs of a tumor mass. Conclusion: the use of robot-assisted thoracoscopic surgical access is a safe and effective method for treating cystic duplication of the thoracic esophagus.

A lightweight optimal design method for magnetic adhesion module of wall-climbing robot based on surrogate model and DBO algorithm

A lightweight optimal design method for magnetic adhesion module of wall-climbing robot based on surrogate model and DBO algorithm

Comparative analysis of approaches necessary for designing a robotic module for industrial parts defect detection

Over the past two decades, the most frequently implemented method of oil extraction in the Russian Federation has been a mechanized method using an electric submersible pump (ESP) unit. Due to the remoteness of the fields from the base of repair enterprises, the cost of transporting spent but maintainable ESPs significantly exceeds their purchase price, which over time leads to the formation of a significant amount of decommissioned oil production equipment. To solve this problem, it is proposed to develop a mobile robotic module for sorting, defecting and storing pump parts, thanks to its use, minor repairs of equipment will be possible directly at production sites. The article deals with the problems of complex flaw detection of metal and non-metallic parts with the possibility of applying the developed methodology for a wide range of industrial products, at the same time, the main attention is paid to work with ESP parts. Based on the decomposition of the problem, the most problematic operations were identified: classification of parts, surface control (identification of defects), dimensional control. The results of a brief comparative analysis for each of the above subtasks are based on a review of the scientific literature over the past 30 years, with the largest number of sources reviewed in the last 5 years. As a result optimal methods for solving the task were derived — a machine learning technique for classifying surface defects, the use of a coordinate measuring machine with a manipulator for dimensional control. A new approach is also proposed to solve the main problem of machine learning methods (lack of training samples) in the form of using synthetic photorealistic images for classification with transfer of defect features from semantically close and publicly available training samples.