Robotic Components Research Articles

Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits.

Most soft robots are pneumatically actuated and fabricated by molding and assembling processes that typically require many manual operations and limit complexity. Furthermore, complex control components (for example, electronic pumps and microcontrollers) must be added to achieve even simple functions. Desktop fused filament fabrication (FFF) three-dimensional printing provides an accessible alternative with less manual work and the capability of generating more complex structures. However, because of material and process limitations, FFF-printed soft robots often have a high effective stiffness and contain a large number of leaks, limiting their applications. We present an approach for the design and fabrication of soft, airtight pneumatic robotic devices using FFF to simultaneously print actuators with embedded fluidic control components. We demonstrated this approach by printing actuators an order of magnitude softer than those previously fabricated using FFF and capable of bending to form a complete circle. Similarly, we printed pneumatic valves that control a high-pressure airflow with low control pressure. Combining the actuators and valves, we demonstrated a monolithically printed electronics-free autonomous gripper. When connected to a constant supply of air pressure, the gripper autonomously detected and gripped an object and released the object when it detected a force due to the weight of the object acting perpendicular to the gripper. The entire fabrication process of the gripper required no posttreatment, postassembly, or repair of manufacturing defects, making this approach highly repeatable and accessible. Our proposed approach represents a step toward complex, customized robotic systems and components created at distributed fabricating facilities.

Robotic approach in complex ventral hernias: anterior component separation technique.

Open onlay ventral hernia repair is still one of the most-used surgical techniques for the repair of hernias worldwide. The robotic anterior component separation technique uses the surgeon's usual anatomical expertise on onlay mesh placement with the manipulation and advantages of minimally invasive surgery. It maintains the precepts of reestablishment the midline integrity and insertion of mesh in the preaponeurotic space, without contact with the viscera. The use of this technique is simple and quite reproducible if you compare it with other techniques. Also, the time spent in surgery does not last long.

Network lightweight method based on knowledge distillation is applied to RV reducer fault diagnosis

As a core component of industrial robots, the RV reducer directly affects the normal operation of the robot, so it is of great significance to monitor its status and diagnose faults. In the field of fault diagnosis, intelligent diagnosis methods based on deep learning have shown great advantages in accuracy and efficiency. However, as the network depth and scale increase, the exponentially growing model computation and parameter amounts require higher hardware requirements for computers, making it difficult to deploy on embedded platforms with limited computing resources. This makes it difficult for deep learning-based fault diagnosis methods to be applied in practical industrial settings that emphasize real-time performance, portability, and accuracy. This paper proposes a network lightweight method based on knowledge distillation (KD). Using the two-dimensional time–frequency map of vibration signals as the model input, the improved MobileNet-V3 network is used as the teacher network, and the simplified convolutional neural network is used as the student network (SN). The method of KD is applied to condense the knowledge and transfer it to the SN. The proposed method is validated using an RV reducer fault simulation experiment platform, and the results show that the proposed method reduces computation and parameter amounts by about 170 times at an accuracy rate of 94.37%, and run time is shortened by nearly one-third, and a generalization verification was conducted using the rotating mechanical fault simulation experiment platform. The models were also deployed on embedded devices to verify that the method proposed in this paper effectively reduces the deep learning network model’s demand for hardware resources of the operating environment. This provides an effective reference for deploying and implementing deep learning-based fault diagnosis on embedded systems with lower hardware configurations.

P‐38: Display Industry Industrial Robot Failure Warning System

Along with the concept of "Industry 4.0" in recent years, intelligent manufacturing has been increasingly emphasized by many enterprises in the production process. In the display industry, the normal and efficient operation of industrial robots is directly related to the output and yield of a company 's products. The "failure warning system" introduced in this paper enables real‐time monitoring and analysis of key parameters of the robot. This ensures that the status and operation of the robot's components are always available, so that maintenance can be planned in advance and uncontrollable downtime can be avoided

Edukasi robotika untuk mingkatkan kemampuan motorik halus di SD Baiturrahman

Current formal education does not only focus on developing scientific and theoretical knowledge, but also pays attention to thought processes and behavior. Extracurricular activities are a means that can be optimized to improve thinking skills or behavior. Of the various kinds of extracurricular activities that exist, robotics is one of the new extracurricular activities, but is very much liked by students. Limited human resources who understand robotics and school support is one of the challenges in introducing robotics technology, especially for elementary school (SD) students. The solution to addressing this problem is that in this community service activity socialization and workshops on assembling robots for elementary school students are carried out. There are three main stages carried out in service activities: initial preparation, implementation, evaluation and reporting. Activities during the implementation of activities, begins with the introduction of robot components to be assembled. Participants in the activity totaling 37 students were divided into 6 groups. Each group was guided to follow the steps of assembling the robot, which consisted of: assembling several robot components and tightening the screws using a screwdriver, uploading the previously made computer program to the chip installed on the robot, activating the robot and controlling it via a smartphone. Partners filled out the questionnaire after the main activities were carried out. Partner responses to 6 statements related to the implementation of community service activities, on average giving a value in the category S = Agree, this means that partners agree and support this community service activity.

Trajectory planning method of 6-DOF modular manipulator based on polynomial interpolation

Robots are widely used in all walks of life, and their excellent work efficiency has been paid attention to. As the key component of robot, manipulator plays an important role in the running performance of robot. In order to effectively improve the trajectory accuracy and efficiency of the manipulator, a six degree of freedom (6-DOF) modular manipulator trajectory planning method based on polynomial interpolation is proposed, and its feasibility and effectiveness are verified by experiments. At the same time, the performance of the method is compared with two other methods of the same type. The experimental results show that the six degree of freedom modular trajectory planning method has a shorter running time, and the shortest running time is 1.62 s. Compared with the directions in previous studies, the planning trajectory of the proposed method is more practical and its accuracy is higher. In the iterative process, the running time of the proposed method is also the shortest. In addition, the minimum error of the three methods is about 1%, which is lower than the other two methods. It is concluded that the six degree of freedom modular trajectory planning method has high feasibility and performance, which is of great significance to improve the operating efficiency and stability of the robot.

Flagellum‐Inspired Soft Rotary Motor

AbstractSoft robots have gained significant attention in recent years owing to their high safety in human–machine interactions and exceptional adaptability to unpredictable environments. Despite great advances, achieving continuous rotation with tunable stepping angle in soft robots remains a challenge. Herein, inspired by the stepping rotation strategy of the flagellum, an artificial soft rotary motor capable of bidirectional continuous rotation and tunable stepping angles is presented. The motor features a wide range of stepping angles (−72.5° to 73.4°), enabling it to rotate to any angle position with high resolution. The soft motor also exhibits high rotation speed (18.23 r min−1), zero‐energy position‐holding, and excellent durability. Furthermore, the potential applications of the soft rotary motors, including tuning the grasping direction of a soft gripper and adjusting the winding direction of a soft tentacle is demonstrated. This work offers a universal soft fluidic component for soft robots, endowing them with enhanced agility and movement capabilities.

Therapeutic Effects of Robotic-Exoskeleton-Assisted Gait Rehabilitation and Predictive Factors of Significant Improvements in Stroke Patients: A Randomized Controlled Trial.

Robotic-exoskeleton-assisted gait rehabilitation improves lower limb strength and functions in post-stroke patients. However, the predicting factors of significant improvement are unclear. We recruited 38 post-stroke hemiparetic patients whose stroke onsets were <6 months. They were randomly assigned to two groups: a control group receiving a regular rehabilitation program, and an experimental group receiving in addition a robotic exoskeletal rehabilitation component. After 4 weeks of training, both groups showed significant improvement in the strength and functions of their lower limbs, as well as health-related quality of life. However, the experimental group showed significantly better improvement in the following aspects: knee flexion torque at 60°/s, 6 min walk test distance, and the mental subdomain and the total score on a 12-item Short Form Survey (SF-12). Further logistic regression analyses showed that robotic training was the best predictor of a greater improvement in both the 6 min walk test and the total score on the SF-12. In conclusion, robotic-exoskeleton-assisted gait rehabilitation improved lower limb strength, motor performance, walking speed, and quality of life in these stroke patients.

A reference implementation for knowledge assisted robot development for planetary and orbital robotics

The use of modular, yet reusable and interchangeable components offers flexibility for system designers and thus leads to a higher degree of reconfigurability with cost effective, adaptable and scalable solutions. The development of a robotic system remains, however, a complex task with increasing demands depending on the targeted capabilities, functionalities and operational constraints of the robot. This requires expert knowledge in mechanical, electrical and software engineering, as well as tools that can collect, process and use relevant data. As a result, the amount of available and managed information is increasing, while its relevance, usability, and knowledge that can be extracted from it are consequently decreasing: a situation described as information overload paradox.The design of a modular system can be based on the use of standardized modules with universal interconnects, but it should be aligned with a strong formalism, e.g. the semantic representation of components, to allow for a better management of the overall complexity. An ontology-based knowledge representation, for instance, which provides domain and application specific knowledge for the design of robotic systems, can be used as a method of storing and sharing data in a uniform, machine-readable and standardized way.In this paper, we introduce the Knowledge-based Open Robot voCabulary as Utility Toolkit (korcut) as a core component to support the ontology-driven development of robotic systems as part of a reference implementation of the Q-Rock development cycle. Q-Rock gathers methods to assist users in designing robot configuration by: (a) automatically exploring robot capabilities based on robot hardware, (b) proposing robot designs to meet the user’s needs, (c) refining the proposed robot designs. We use korcut to improve the robot design process in orbital and planetary robotics from the requirements definition to the development phases of complex modular robotic structures (e.g. composition of an unmanned ground vehicle with robotic arms). This is implemented by embedding semantic component descriptions in a state-of-the-art open-source 3D modeling software. Furthermore, the korcut ontology family includes various sub-ontologies to address specific space-related tasks, such as the modeling of a Standard Interconnect (SI) for On Orbit Services (OOS) and Orbital Factory. The ontology design follows criteria that have been derived from a survey conducted as part of this work. This paper also covers an evaluation of its current applications from a methodological, knowledge representation, and software tool perspectives. In the application part, we introduce the use of the ontology to model a multi-functional SI that enables mechanical connections between various (modular) robotic components, as well as transfer of power and data. Finally, we provide a critical analysis of our work and outline its future work.

Exploiting the Internet Resources for Autonomous Robots in Agriculture

Autonomous robots in the agri-food sector are increasing yearly, promoting the application of precision agriculture techniques. The same applies to online services and techniques implemented over the Internet, such as the Internet of Things (IoT) and cloud computing, which make big data, edge computing, and digital twins technologies possible. Developers of autonomous vehicles understand that autonomous robots for agriculture must take advantage of these techniques on the Internet to strengthen their usability. This integration can be achieved using different strategies, but existing tools can facilitate integration by providing benefits for developers and users. This study presents an architecture to integrate the different components of an autonomous robot that provides access to the cloud, taking advantage of the services provided regarding data storage, scalability, accessibility, data sharing, and data analytics. In addition, the study reveals the advantages of integrating new technologies into autonomous robots that can bring significant benefits to farmers. The architecture is based on the Robot Operating System (ROS), a collection of software applications for communication among subsystems, and FIWARE (Future Internet WARE), a framework of open-source components that accelerates the development of intelligent solutions. To validate and assess the proposed architecture, this study focuses on a specific example of an innovative weeding application with laser technology in agriculture. The robot controller is distributed into the robot hardware, which provides real-time functions, and the cloud, which provides access to online resources. Analyzing the resulting characteristics, such as transfer speed, latency, response and processing time, and response status based on requests, enabled positive assessment of the use of ROS and FIWARE for integrating autonomous robots and the Internet.

A sensor review for human detection with robotic systems in regular and smoky environments

Search and rescue robots gained a significant attention in the past, as they assist firefighters during their rescue missions. The opportunity to move autonomously or remotely controlled with intelligent sensor technology, to detect victims in unknown fire smoke environments, introduces a growing technology in fire engineering. Since sensor systems are a component of mobile robots, there is a demand for intelligent robot vision, especially for human detection in fire smoke environments. In this article, an overview of sensor technologies and their algorithms for human detection in regular and smoky environments is presented. These sensor technologies are categorized into single sensor and multi-sensor systems. Novel sensor approaches are led by artificial intelligence, 3D mapping and multi-sensor fusion. The article provides a contribution for future research directions in algorithms and applications and supports decision-makers in fire engineering to get knowledge in trends, novel applications and challenges in this field of research.

Agricultural Harvesting Robot Concept Design and System Components: A Review

Developing different robotic platforms for farm operations is vital to addressing the increasing world population. A harvesting robot significantly increases a farm’s productivity while farmers focus on other relevant farm operations. From the literature, it could be summarized that the design concepts of the harvesting mechanisms were categorized as grasping and cutting, vacuum suction plucking systems, twisting and plucking mechanisms, and shaking and catching. Meanwhile, robotic system components include the mobile platform, manipulators, and end effectors, sensing and localization, and path planning and navigation. The robotic system must be cost-effective and safe. The findings of this research could contribute to the design process of developing a harvesting robot or developing a harvesting module that can be retrofitted to a commercially available mobile platform. This paper provides an overview of the most recent harvesting robots’ different concept designs and system components. In particular, this paper will highlight different agricultural ground mobile platforms and their associated mechanical design, principles, challenges, and limitations to characterize the crop environment relevant to robotic harvesting and to formulate directions for future research and development for cotton harvesting platforms.

Development of a flexible embedded neurostimulator for animal robots

The neural stimulator is a core component of animal robots. While the control effect of animal robots is influenced by various factors, the performance of the neural stimulator plays a decisive role in regulating animal robots. In order to optimize animal robots, embedded neural stimulators had been developed using flexible printed circuit board technology. This innovation not only enabled the stimulator to generate parameter-adjustable biphasic current pulses through control signals, but also optimized its carrying mode, material, and size, overcoming the disadvantages of traditional backpack or head-inserted stimulators, which have poor concealment and are prone to infection. Static, in vitro, and in vivo performance tests of the stimulator demonstrated that it not only had precise pulse waveform output capability, but also was lightweight and small in size. It had excellent in vivo performance in both laboratory and outdoor environments. Our study has high practical significance for the application of animal robots.

ORB-Livox: A real-time dynamic system for fruit detection and localization

Accurate fruit localization plays an essential role in many agricultural tasks and is one of the most vital components of an autonomous harvesting robot. Although fruit localization in stationary conditions has been extensively studied, the method to perform fruit localization in dynamic conditions is rarely studied. Fruit localization under dynamic conditions is much more challenging, since movement will introduce continuously irregular ego-motion on visual sensors, which can lead to considerable accuracy degeneration or even system failure. However, fruit localization under dynamic conditions is an essential task since it can significantly improve the harvesting efficiency. In this study, a fruit localization method is proposed to perform accurate fruit localization under dynamic conditions. Firstly, an ORB-SLAM3 SLAM algorithm is utilized to estimate the camera’s pose under dynamic conditions. Secondly, YOLO-V5 is used to identify apples in RGB images. Moreover, Lidar is used to measure the depth of each apple accurately. Finally, a clustering technique based on the Gaussian distribution is proposed to mitigate the accuracy loss under dynamic conditions to optimize apple positions. Our method is extensively evaluated in indoor and outdoor experiments. The results of indoor experiments show that the average localization error is 21.1 mm, with 90% of the errors are less than 30 mm. The experimental result in an orchard environment demonstrates that our proposed method can detect and localize apples under dynamic conditions in unstructured natural environments.

Path Planning Method for Underground Survey Robot in Dangerous Scenarios

Over the past 20 years, there has been a growing demand for autonomous exploration of underground environments such as tunnels, urban underground corridors, and complex cave environments. With the development of robotics, dangerous tasks that used to be done by humans have been replaced by underground survey robots. To avoid obstacles in the process of robot work, path planning technology has become one of the important components of underground survey robot. The underground survey robot not only needs to move in the plane, but also needs to climb along the pipe. However, the existing research is mostly two-dimensional plane, which cannot be directly applied to the underground exploration robot. Firstly, this paper compares the existing research and chooses Dijkstra algorithm for three-dimensional implementation. After that, the environment map is established, and the position of obstacles faced by the robot was set for simulation verification. Simulation results show the effectiveness of the proposed algorithm.

Path Planning of Autonomous Mobile Robots Based on an Improved Slime Mould Algorithm

Path planning is a crucial component of autonomous mobile robot (AMR) systems. The slime mould algorithm (SMA), as one of the most popular path-planning approaches, shows excellent performance in the AMR field. Despite its advantages, there is still room for SMA to improve due to the lack of a mechanism for jumping out of local optimization. This means that there is still room for improvement in the path planning of ARM based on this method. To find shorter and more stable paths, an improved SMA, called the Lévy flight-rotation SMA (LRSMA), is proposed. LRSMA utilizes variable neighborhood Lévy flight and an individual rotation perturbation and variation mechanism to enhance the local optimization ability and prevent falling into local optimization. Experiments in varying environments demonstrate that the proposed algorithm can generate the ideal collision-free path with the shortest length, higher accuracy, and robust stability.

The role of chemical microstructures and compositions on the actuation performance of dielectric elastomers: A materials research perspective

AbstractDielectric elastomer actuators (DEAs) are one of the emerging areas of investigation in smart elastomer research and have attracted considerable interests due to their wide‐ranging applications, for instance in automation and soft robotic components. Commonly utilized dielectric elastomers (DEs) such as nitrile butadiene rubber (NBR) and its hydrogenated and carboxylated derivatives along with the dielectric fillers (titanium oxide, barium titanate, etc.) were considered to be one of the most established composite systems demonstrating high actuation performance. When subjected to external electrical field, the DEAs derived from the composites were, therefore, capable of reproducing high energy density and large deformation with fast response time. The recent research emphasizes that tunable material properties eventually triggered the improved actuation performance. The exploration of compliant electrodes fabricated by the conventional and state‐of‐the‐art lithographic methods was also crucial to improve actuation performance for the soft robotic applications. This review summarizes the commonly used DEs and dielectric fillers, their performances and challenges, and outlines the research progress (experimental and theoretical) in the field of actuators. Finally, a brief introduction to DEA‐based soft robotics application is also presented.

Compound Fault Diagnosis of Harmonic Drives Using Deep Capsule Graph Convolutional Network

Harmonic drive is a key component of the industrial robot. Because of its large reduction ratio and excessive dynamic loading, various kinds of faults may occur. In particular, since the robot is an integrated system, it is not unusual to have multiple harmonic drives malfunction simultaneously, which is difficult to diagnose. In practice, these kinds of compound faults are often mislabeled as single faults causing missing repair. In this paper, we propose a deep capsule graph convolutional network (DCGCN) approach to diagnose compound faults of harmonic drives. First, the multi-sensor data is used to obtain the frequency spectrum of the fault signal and construct the label relationship map of the adjacency matrix. Second, the deep capsule network is used to learn the representation of the fault vector, and the graph convolutional network is used to learn the relationship between different single-label faults. Third, the two networks are combined to obtain diagnosing results. Finally, the dynamic routing algorithm and the margin loss function are used to optimize the DCGCN. The experimental results show that the proposed DCGCN can effectively diagnose compound faults under varying working conditions, outperforming other existing state-of-the-art methods.

V04-02 INTRACORPOREAL ROBOTIC SIGMOID &amp; VAGINOPLASTY

You have accessJournal of UrologyCME1 Apr 2023V04-02 INTRACORPOREAL ROBOTIC SIGMOID & VAGINOPLASTY Daniel Tennenbaum, Luciana Lerendegui, Rafael Gosalbez, and Miguel Castellan Daniel TennenbaumDaniel Tennenbaum More articles by this author , Luciana LerendeguiLuciana Lerendegui More articles by this author , Rafael GosalbezRafael Gosalbez More articles by this author , and Miguel CastellanMiguel Castellan More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003252.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: There are different strategies in the construction of a neovagina, with no consensus on which is the best approach. We have previously published our experience with sigmoid Monti vaginoplasty via an open approach. In this video, we present a case of neovagina construction by way of sigmoid Monti vaginoplasty, via an intracorporeal robotic-assisted approach. METHODS: An 18-year-old transwoman with a long history of gender dysphoria, followed since puberty by a multidisciplinary team, sought definitive gender affirmation surgery. The procedure was performed by two teams working simultaneously, with the external component of the surgery happening whilst the bowel surgery was being performed. The intra-abdominal component of the surgery was performed using one umbilical camera port and 4 additional ports. The neovagina was created using a ∼12 cm segment of distal sigmoid colon and detubularized longitudinally before being retubularized to form a Monti cuff by closing the mesenteric edge, while leaving the mesentery in a cephalad position. Dissection into the pouch of Douglas was then performed to advance the neovagina into the perineum, before anastomosis to the surrounding tissues. RESULTS: Total operative time was approximately 8 hours, with the robotic component approximately 5 hours. The postoperative course was uneventful, with expected return of bowel function and adequate pain control, and with discharge on postoperative day 6. The patient reported great satisfaction at 3-month followup. CONCLUSIONS: Intracorporeal robotic Monti sigmoid vaginoplasty allows for the construction of a neovagina with a tension-free vascular pedicle, with improved morbidity and quicker time to recovery. This approach allows two surgical teams working simultaneously and preparing the perineum for anastomosis. Our video, the first of its kind to the knowledge of the authors, demonstrates that this approach can be performed in a safe and effective manner. Further pursuit of this approach may be warranted in the right clinical environment. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e338 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Daniel Tennenbaum More articles by this author Luciana Lerendegui More articles by this author Rafael Gosalbez More articles by this author Miguel Castellan More articles by this author Expand All Advertisement PDF downloadLoading ...

Dramatic morphological changes in liposomes induced by peptide nanofibers reversibly polymerized and depolymerized by the photoisomerization of spiropyran.

Cytoskeletons such as microtubules and actin filaments are natural protein assemblies, which dynamically control cellular morphology by reversible polymerization/depolymerization. Recently, the control of polymerization/depolymerization of fibrous protein/peptide assemblies by external stimuli has attracted significant attention. However, as far as we know, the creation of an "artificial cytoskeleton" that reversibly controls the polymerization/depolymerization of peptide nanofiber in giant unilamellar vesicles (GUVs) has not been reported. Here, we developed peptide nanofiber self-assembled from spiropyran (SP)-modified β-sheet-forming peptides, which can be reversibly polymerized/depolymerized by light. The reversible photoisomerization of the SP-modified peptide (FKFECSPKFE) to the merocyanine-peptide (FKFECMCKFE) by ultraviolet (UV) and visible light irradiation was confirmed by UV-visible spectroscopy. Confocal laser scanning microscopy with thioflavin T staining and transmission electron microscopy of the peptides showed that the SP-peptide formed β-sheet nanofibers, whereas the photoisomerization to the merocyanine-peptide almost completely dissociated the nanofibers. The merocyanine peptide was encapsulated in spherical GUVs comprising of phospholipids as artificial cell models. Interestingly, the morphology of GUV encapsulating the merocyanine-peptide dramatically changed into worm-like vesicles by the photoisomerization to the SP-modified peptide, and then reversibly changed into spherical GUV by the photoisomerization to the MC-modified peptide. These dynamic morphological changes in GUVs by light can be applied as components of a molecular robot with artificially controlled cellular functions.