Robotic Technology Research Articles

Phenobot: An Autodigital Modeling System for in situ Phenotyping in Horticulture

Accurate reconstruction of plant models for phenotyping analysis is critical for optimizing sustainable agricultural practices in precision agriculture. Traditional laboratory‐based phenotyping, while valuable, falls short of understanding how plants grow under uncontrolled conditions. Robotic technologies offer a promising avenue for large‐scale, direct phenotyping in real‐world environments. This study explores the deployment of emerging robotics and digital technology in plant phenotyping to improve performance and efficiency. Three critical functional modules, environmental understanding, robotic motion planning, and in situ phenotyping, are introduced to automate the entire process. Results demonstrate the effectiveness of the system in agricultural environments. The phenorobot system autonomously collects high‐quality data by navigating around plants. In addition, the in situ modeling model reconstructs high‐quality plant models from the data collected by the robot. The developed robotic system shows high efficiency and robustness, demonstrating its potential to advance plant science in real‐world agricultural environments.

Dynamic responses of a vibro-impact capsule robot self-propelling in the large intestine via multibody dynamics

Abstract In recent years, colonic capsule endoscopy has become available in clinical practice as an alternative modality to colonoscopy. However, it faces challenges such as prolonged examination time and the absence of clinician navigation. Leveraging their pioneering work in the field of vibro-impact self-propulsion technique for gastrointestinal endoscopy, Zhang et al. (IEEE Robot. Autom. Lett. 8:1842–1849, 2023) developed a novel, untethered, self-propelled, endoscopic capsule robot, with the aim of providing a new means of examining bowel cancer in real time. To evaluate and optimize the passage of this capsule robot self-propelling in the large intestine, this work adopts multibody dynamics analysis and experimental investigation to study the robot’s dynamics and its interaction with the intestinal environment. Considering the complex anatomy of the large intestine, containing different sections, e.g., cecum, ascending, transverse, descending, and sigmoid colon, and variations of the haustra, e.g., with various radii, lengths, and heights, the robot was driven by the square-wave excitation of an inner mass interacting with the capsule body and tested on a real porcine colon. The robot’s driving parameters, including the excitation frequency, amplitude, and duty cycle, and the dimensions of the haustra are the two main factors influencing the robot’s progression in the intestine. By comparing with the experimental results, the proposed multibody dynamics model developed using MSC Adams can estimate the movement of the capsule robot and the intestinal resistance quantitatively. Extensive numerical and experimental studies suggest an excitation frequency of 60 Hz and a duty cycle of 0.4 as the optimal parameters for driving the robot, and the longer the haustral length is, the faster the robot passes through. These results ensure the validity of the proposed multibody dynamics platform, which can be used by robotic engineers for developing medical robots for intestinal examinations.

Does the rise of robotic technology reduce fertility? longitudinal evidence from China

Does the rise of robotic technology reduce fertility? longitudinal evidence from China

Patient Perspective on Robotic-Assisted Total Joint Arthroplasty.

Robotic-assisted total joint arthroplasty (TJA) has gained popularity in recent years. Despite mixed patient and surgeon perceptions, conflicting evidence regarding efficacy and cost-effectiveness in comparison to manual TJA exists. Patients' beliefs surrounding robotic-assisted TJA remain unclear. This study aims to assess patients' expectations on robotic technology in TJA. A 9-question survey assessing patient understanding and expectations of the use of robotics in TJA was distributed to preoperative and postoperative hip and knee patients of five surgeons at a high-volume academic center. Responses were descriptively analyzed. A total of 498 responses were collected. Of all respondents, 69.1% are aware of robotic usage in TJA, 68.5% are interested but unsure of the benefits, and only 19.5% feel it is superior to manual surgery. Most patients did not consider robotic TJA as minimally invasive surgery, with 61.7% stating they are not the same. In addition, 52.3% were not comfortable with extra or longer incisions for robotic procedures. Regarding surgeon choice, 94.9% did not consider if the surgeon is able to perform robotic TJA, 74.4% wanted their surgeon proficient in manual TJA, and 72.4% felt that surgeons who use robotic technology are not more capable than manual surgeons. Awareness and curiosity of robotic-assisted TJA exists; however, most patients did not appear to acknowledge superiority or benefits over manual surgery. Furthermore, patients appear to prefer surgeon proficiency in manual techniques, which may influence training programs in the future. Surgeons should weigh patient goals, expectations, outcomes, and costs when choosing to perform robotic TJA.

Piezotronic Strain Sensor with Uniform and Switchable Sensitivity by Conductivity Transformation

Piezotronic strain sensors that convert mechanical deformation into electrical signals are becoming increasingly important in artificial intelligence, human-machine interfaces, and robotic technologies. These applications require piezotronic sensor with the integration of high-sensitivity, high-stability, and versatile-functionality, which are limited by the single conductivity mechanism. In this study, we propose a piezotronic strain sensor with uniform and switchable sensitivity in a short channel field effect junction.The strain-induced piezo-potential can be used to switch the conductivity between Schottky and Ohmic regime, leading to an exponential (linear) piezotronic modulation in Schottky (Ohmic) conductivity elucidated by Fermi occupation theory. Local gauge factor reaches a high value of 1330 in Schottky conductivity and a low value of 320 in Ohmic regime, yielding a higher ratio of 4.2. The stable conductivity makes these high and low sensitivity uniform over a wide strain range. This study gives a deep insight into the correlation of strain-sensing performance and conductive mechanism in piezotronic sensors, and offers a new avenue to develop multifunctional and high-sensitivity sensors.

Reliability Analysis and Numerical Simulation of the Five-Robot System with Early Warning Function

The rapid advancement of robotic technologies has demonstrated the significant potential of Multi-Robot Systems (MRS) for application across various fields, particularly in automation, manufacturing, and rescue operations. However, enhancing the reliability of Multi-Robot Systems, particularly in critical applications, has emerged as a primary focus of research. A mathematical model of a five-robot system, equipped with early warning capabilities, is developed using Markov process theory and the supplementary variable method in this paper. A model of an abstract Cauchy problem system is developed, employing semigroup theory to investigate the well-posedness of solutions for this five-robot system. The stability of the system is verified using analytical methods, confinal correlation theory, and modern functional analysis techniques. Several key reliability indicators are presented using the eigenvector method. Numerical simulations and comparative methods effectively demonstrate the efficacy of the proposed eigenvector method. Firstly, the innovation of this paper lies in the combination of qualitative and quantitative analyses to improve and enrich the theory and methods of repairable systems. Secondly, mathematical analysis methods and the mathematical software are employed to provide both analytical and numerical solutions for the system.

Smart Force Sensing in Robot Surgery Utilising the Back Electromotive Force

Since the introduction of robot-assisted laparoscopic surgery, efforts have been made to incorporate force sensing technologies to monitor critical components and to provide force feedback. The advanced laparoscopic robotic system (AdLap RS) is a robotic platform that aims to make robot technology more sustainable through the use of the fully reusable shaft-actuated tip-articulating (SATA) instruments. The SATA instrument driver features electronics and sensors exposed to the sterile environment, which complicate the sterilisation process. The aim of this study was to develop and validate smart sensing in stepper motors using the back electromotive force in a newly developed Smart SATA Driver (SSD), eliminating the need for sensors in the sterile environment. Methods: The stepper drivers were equipped with TMC2209 ICs featuring StallGuard technology to measure back EMF. The tip was actuated up until a set StallGuard threshold value was reached, at which the resulting tip force was measured. This cycle was repeated ten times for a range of threshold levels. A regression analysis with a power series model was used to determine the quality of the fit. Results: The SSD is capable of exerting tip forces between 2.4 and 8.2 N. The back EMF force test demonstrated a strong correlation between obtained StallGuard values and measured tip forces. The regression analysis showed an R-squared of 0.95 and a root Mean squared error of 0.4 N. Discussion: The back EMF force test shows promise for force feedback, but its accuracy limits real-time use due to back EMF fluctuations. Future improvements in motor stability and refining the back EMF model are needed to enable real-time feedback. Conclusion: The strong correlation during the back EMF force test shows its potential as a low-budget method for detecting motor stalls and estimating tool–tissue forces without the need for sensors in laparoscopic instruments.

Robotics: Road Ahead for Mechanical and Physical Necessities, Bridging Artificial Intelligence, and Integration Challenges

Abstract On behalf of the ASME Robotics Technology Group and its Working Groups, this paper provides a preview of their recently published 2024 ASME Robotics Roadmap. The onset of Industry 5.0 and its coexistence with Industry 4.0 are moving robots and manufacturing from traditional automation into the “smart manufacturing” realm. With conflicting priorities of productivity and safety, building mutual “trust” between robots and humans will require bridging the sensing and artificial intelligence (AI) with the mechanical-n-physical necessities to successfully augment the robot's awareness and intelligence. To this end, the ASME Robotics Technology Group convened several working groups to produce a robotics roadmap that embodies the mechanical engineering aspects. The roadmap was written with the aim (1) to embody mechanical and physical necessities and bridge the gaps between AI and integration challenges; (2) to identify critical trends, top-of-market challenges, and knowledge gaps; and (3) to identify opportunities in the journey path to get there and in strengthening the mechanical engineering footprint in robotics technology. While robotics cuts across many technological areas, industry segments, and applications for the greater good, the focus is on identifying what are the enabling technologies and science needed for: human–robot physical interaction and beyond physical; robot-to-robot and robot-to-(autonomous) machine collaboration; increasing robot repeatability and accuracy; space-based robotic manufacturing and assembly; human-centered manufacturing; risk and safety aspects of robotics; and what should ASME, government, and industry do to advance robotics. The roadmap is to serve as a guidepost for ASME robotics activities, its membership, and the robotics community-at-large.

System Integration Failures and Their Impact on Patient Safety in Critical Care Settings

Robotic surgery systems offer precision and minimally invasive procedures, enhancing surgical outcomes in critical care settings. However, these systems' complexity, with integrated hardware and software, can lead to integration failures, compromising patient safety. This paper examines the system integration challenges in robotic surgery, explores their effects on patient safety, and suggests solutions to overcome them. Rigorous testing strategies during development and continuous monitoring of system performance are crucial in detecting failures early and ensuring reliable system operation. Keywords: Robotic Surgery Systems, System Integration, Patient Safety, Software Failures, Hardware Failures, Critical Care, Integration Testing, Failover Mechanisms, Robotic Arms, Sensor Calibration, Real-Time Data Processing, Medical Robotics, Surgical Precision, Failure Detection, Stress Testing, Fault-Tolerant Design, Error Handling, Modular Design, Quality Assurance, ISO 13485, Robotic Surgery Failures, Calibration Errors, Mako System, Da Vinci Surgical System, CyberKnife System, Automated Testing, Robotic Surgery Technology.

Robotic Technology Enabled Gait Training Methods: A Boon for Gait Rehabilitation in Incomplete Tetraplegia

Robotic Technology Enabled Gait Training Methods: A Boon for Gait Rehabilitation in Incomplete Tetraplegia

Unlocking robotic perception: comparison of deep learning methods for simultaneous localization and mapping and visual simultaneous localization and mapping in robot

Abstract Simultaneous Localization and Mapping (SLAM) and Visual SLAM are crucial technologies in robotics, allowing autonomous systems to navigate and comprehend their environment. Deep learning (DL) has become a powerful tool in driving progress in these areas, providing solutions that improve accuracy, efficiency, and resilience. This article thoroughly analyzes different deep learning techniques designed explicitly for SLAM and Visual SLAM applications in robotic systems. This work provides a detailed overview of DL roles in SLAM and VSLAM and emphasizes the differences between these two fields. Five powerful DL methods are investigated: Convolutional Neural Networks in extracting features and understanding meaning, Recurrent Neural Network in modeling temporal relationships, Deep Reinforcement Learning in developing exploration strategies, Graph Neural Network in modeling spatial relationships, and Attention Mechanisms in selectively processing information. In this research, we will examine the advantages and disadvantages of each approach in relation to robotic applications, taking into account issues such as real-time performance, resource restrictions, and adaptability to various situations. This article seeks to guide researchers and practitioners in selecting suitable deep learning algorithms to improve the capabilities of SLAM and Visual SLAM in robotic systems by combining ideas from recent research and actual implementations. The popular types of each concerned DL will be synthesized with the discussion of pros and cons.

Advancing Agriculture with AI-Powered Robotic Harvesting Systems for Legume Crops

Background: The integration of Artificial Intelligence (AI) in agricultural practices has witnessed substantial advancements, with a focus on enhancing efficiency and sustainability. This research explores the application of AI-powered robotic harvesting systems for legume crops, aiming to revolutionize traditional harvesting methods. By leveraging machine learning algorithms and robotic technology, this study investigates the feasibility and performance of such systems in terms of precision, speed and resource optimization. Methods: This research focussed on creating and implementing a robotic harvesting system that applies artificial intelligence to precisely identify and harvest legume crops. The system’s design relies on a combination of robotic technology, computer vision and machine learning algorithms to achieve optimal performance. In this work, a 4-layer CNN model is used to detect dandelion and soybean. Result: The findings provide valuable insights into the potential benefits and challenges associated with the adoption of AI in legume crop harvesting, contributing to the ongoing discourse on sustainable agriculture. The 4-layer CNN model shows a good overall accuracy of 99.71%. Confusion matrix and classification report are presented for evaluation of the model.

Robot-Assisted Versus Conventional Harvesting of DIEP and Latissimus Dorsi Flaps for Breast Reconstruction in Post-Mastectomy Women: A Systematic Review and Meta-Analysis

Background/Objectives: Robotic breast reconstruction is an innovative surgical technique that integrates robotic technology into breast reconstruction procedures, offering several advantages over conventional approaches. These benefits include enhanced visualization, increased surgical dexterity, and superior cosmetic outcomes. This study aims to comprehensively compare robotic-assisted and conventional breast reconstruction approaches in terms of complication profiles and operation-related measurements. Methods: A comprehensive search was conducted in PubMed, Embase, Scopus, Web of Science, Google Scholar, CENTRAL, and VHL from inception to October 2024 to identify relevant studies. Risk ratios for the following complications were calculated between the groups: donor site seroma, hematoma, infection, and unplanned reoperation. Mean differences were also calculated for the duration of surgery, length of postoperative hospital stays, and opioid use. Results: A meta-analysis was performed on 9 studies including a total of 1094 patients. No significant differences were found between the groups in the risk ratios for reoperation, seroma formation, delayed healing, infections, and hematomas. Similarly, there were no significant differences in postoperative opioid use. The duration of surgery was longer in the robot-assisted reconstruction group, whereas the duration of hospital stay was shorter compared to the conventional group. Meta-regression analysis for the duration of surgery model showed that none of the moderators had a statistically significant effect on this outcome. ROBINS-I assessment indicated that all the included studies had a serious risk of bias. Conclusions: Our results suggest that using a robot-assisted approach is associated with a shorter duration of hospital stay and a longer duration of surgery.

Does technology readiness matter? Investigating employee psychological well-being through the lens of social robot anthropomorphism

PurposeSocial robot anthropomorphism is widely recognized for performing crucial tasks in supporting the service industry and enhancing the employee experience. This study elucidates how social anthropomorphic robots can influence the psychological well-being of employees in the hotel industry. The study further examines the impact of social robot warmth, psychological ownership and technology readiness in the above relationship.Design/methodology/approachThe research utilized an online survey questionnaire for data collection and analysis. Data were collected from Indian hotel employees (N = 275). Structural equation modeling was applied to validate the conceptual model. The study examines the results using AMOS v25 and the SPSS PROCESS Macro.FindingsThe study empirically finds that social robot anthropomorphism positively impacts the psychological well-being of hotel employees. Further, it substantiates that despite low technology readiness, social robot anthropomorphism enhances psychological well-being through psychological ownership. Additionally, anthropomorphizing social robots emits warmth, which enhances employees' psychological well-being in the hotel industry.Originality/valueThese empirical results and theory suggest a novel dimension of anthropomorphism in social robot interactions, significantly impacting the employees' psychological well-being. A moderated mediation relationship is established, which confirms social robot warmth and psychological ownership as mediators and technology readiness as a moderator of the social robot anthropomorphism and employee psychological well-being relationship. The results validate the conceptual model of research that is grounded in the theory of psychological ownership and the realism maximization theory.

National survey on data governance and digital surgery: Challenges and opportunities for surgeons in the era of artificial intelligence.

This study evaluates the knowledge of Spanish surgeons regarding data governance and Digital Surgery, their usage, errors, and training deficiencies, as well as differences in knowledge between those who perform robotic surgery and those who do not. A descriptive study was conducted using a closed survey promoted by the Minimally Invasive Surgery and Technological Innovation Section of the Spanish Association of Surgeons, directed at its members between February and March 2024. Out of 1,086 surgeons contacted, 396 responded (36.46%). While 98.3% use electronic medical records, 45.4% lack organized data recording systems. Although 55.9% have access to robotic technology, 70.6% do not use it directly. Surgeons who perform robotic surgery show a higher general knowledge of Digital Surgery terms (7.2 vs 6.619; p = 0.215) and specifically of "Artificial Intelligence" (57.14% compared to less than 25% for other terms), although these differences are not significant. However, there are significant differences in interest in digital technology between those who perform robotic surgery and those who do not (p = 0.023). Additionally, surgeons who support training in digital technology during residency are nearly twice as likely to research Digital Surgery. Surgeons are crucial for the digital transformation in surgery, but many are poorly prepared and insufficiently involved. Those who practice robotic surgery have more knowledge and interest in digital technology, highlighting the need for improved training and governance frameworks to better integrate surgeons into the digital era.

User Action Recognition Using a Fabric Pressure Distribution Sensor in an Electric Wheelchair for Soil Cultivation

Advancements in robotics technology and information and communication technology have significantly affected agriculture, especially systems that aid workers. Soil cultivation often requires prolonged bending, which results in physical strain. A commercially available work wheelchair reduces back strain, but lacks power assistance and relies on leg movements. In a previous study, we developed an electric wheelchair with a fabric pressure-distribution sensor on the seat. This sensor, combined with a fuzzy controller, aids movement by measuring the center of pressure fluctuations and reducing leg strain. However, it does not recognize user’s actions, such as standing or sitting, and requires the wheelchair to stop. This study introduced a user-action recognition system using a wheelchair seat pressure sensor. This system accurately recognizes user actions through deep learning time-series data. We evaluated data augmentation and multi-user data to enhance the performance. The results show improved prediction accuracy for action recognition in the test scenarios.

Study on the quality of drug vial recognition and grasping and opening for pharmacy intravenous admixture robot based on nanophotonic sensing.

With the continuous progress of medical technology, traditional medicine bottle identification and management methods have problems such as low efficiency and large errors, and innovative solutions are urgently needed. Due to its high sensitivity and rapid response characteristics, this study aims to develop a robot system for intravenous infusion based on nanophotonics sensing to realize accurate identification, grasping and opening of medicine bottles in a dynamic environment, so as to improve the safety and efficiency of intravenous infusion. In this paper, an intelligent robot system with nanophotonics sensor is designed, which uses nanomaterials to produce high sensitivity sensor, so as to realize the information recognition of medicine bottle labels. The robot arm is set up to grasp and open the medicine bottle automatically through visual recognition system and feedback control algorithm. We conducted several rounds of experiments in a simulated environment to evaluate the recognition rate, grab success rate, and opening speed of the system. The experimental results show that the recognition system based on nano photonic sensor in bottle of accuracy and grab the success rate is higher, open time is greatly shortened, compared with traditional methods, the new system in recognition and operation were showed a significant advantage. The study demonstrates the potential of nanophotonics sensing technology in intravenous infusion robots. By optimizing the process of bottle recognition, grasping and opening, the system not only improves the safety and efficiency of medical operations, but is also expected to play a role in future medical automation. Future work will focus on further perfecting the system integration and optimization algorithm, so as to adapt to more complex clinical environment.

Task-oriented robotic rehabilitation for back mobility and functioning in a post-intensive care unit obese patient: A case report

Background Intensive care unit (ICU) acquired weakness is a detrimental condition characterized by muscle weakness, difficulty in weaning from mechanical ventilation, impaired mobility, and functional limitations, severely affecting overall quality of life. Obese patients face additional challenges due to obesity-related factors that exacerbate the negative effects of immobilization. Rehabilitation interventions have emerged as a crucial component of post-ICU care, but the rehabilitation management of obese patients remains challenging. Objective to present the impact of implementing Walker View 3.0 SCX technology in post-intensive care unit rehabilitation of obese patient. Methods A 69-year-old Caucasian man with a BMI of 44.8 kg/m2 was referred to the Cardiopulmonary Rehabilitation Unit at Alessandria Hospital, Italy, following an ICU admission for pneumonia. After a comprehensive multidisciplinary evaluation, the patient underwent an intensive rehabilitation program including physical exercises and a personalized dietary plan. A task-oriented robotic rehabilitation was added, utilizing the Walker View 3.0 SCX, for 30 min/day, 5 days/week. The robotic rehabilitation program focused on sit-to-stand mobility with weight support initially and progressed to a weight-supported robotic treadmill. Results The patient showed clear improvements in physical function, muscle strength, and independence in activity of daily living (Barthel Index improved from 15 to 70, De Morton Mobility Index improved from 8 to 39, Medical Research Council Strength improved from 17 to 40, Functional Ambulation Classification score improved from 0 to 3, Handgrip Strength Test improved from 8.8 kg to 39.4 kg). Managed by a single physiotherapist, this approach showed positive results in enhancing functional outcomes, with potential benefits in reducing operator time and assistance costs. Conclusions Integrating task-oriented robotic rehabilitation with Walker View 3.0 SCX showed promising outcomes for obese patients post-ICU. Personalized interventions with weight-bearing capabilities and real-time feedback optimized post-ICU care, enhancing functional outcomes, and potentially reducing operator time and assistance costs. Further research with larger samples is needed to validate the broader applicability and cost-effectiveness of robotic rehabilitation technologies in obese patients with ICU-acquired weakness.

Package Positioning Based on Point Registration Network DCDNet-Att

The application of robot technology in the automatic transportation process of packaging bags is becoming increasingly common. Point cloud registration is the key to applying industrial robots to automatic transportation systems. However, current point cloud registration models cannot effectively solve the registration of deformed targets like packaging bags. In this study, a new point cloud registration network, DCDNet-Att, is proposed, which uses a variable weight dynamic graph convolution module to extract point cloud features. A feature interaction module is used to extract common features between the source point cloud and the template point cloud. The same geometric features between the two pairs of point clouds are strengthened through a bottleneck module. A channel attention model is used to obtain the channel attention weights. The attention weight of each spatial position is calculated, and a rotation translation structure is used to sequentially obtain quaternions and translation vectors. A feature fitting loss function is used to constrain the parameters of the neural network model to have a larger receptive field. Compared with seven methods, including the ICP algorithm, GO-ICP algorithm, and FGR algorithm, the proposed method had rotation errors (MAE, RMSE, and Error of 1.458, 2.541, and 1.024 in the ModelNet40 dataset, respectively) and translation errors (MAE, RMSE, and Error of 0.0048, 0.0114, and 0.0174, respectively). When registering the ModelNet40 dataset with Gaussian noise, the rotation errors (MAE, RMSE, and Error) were 2.028, 3.437, and 2.478, respectively, and the translation errors (MAE, RMSE, and Error) were 0.0107, 0.0327, and 0.0285, respectively. The experimental results were superior to those of the other methods, and the model was effective at registering packaging bag point clouds.

Application Research of Vision-Guided Grinding Robot for Wheel Hub Castings

The combination of vision and robotic grinding technology provides robots with “visual perception” capabilities that enable them to accurately locate the area to be ground and perform the grinding tasks efficiently. Based on the rough grinding requirements for wheel hub burrs proposed by a casting company, this paper investigates the application of a vision-guided grinding robot in treating burrs on wheel hub castings. First, through vision system calibration, the conversion from pixel coordinate system to robot base coordinate system is implemented, thus ensuring that the subsequently extracted burr point coordinates can be correctly mapped to the robot’s operational coordinate system. Next, the images of the burrs on wheel hub castings are collected and processed. All the burr points are extracted by applying image algorithms. In order to improve grinding accuracy, a height error compensation model is established to adjust the coordinates of the 2D-pixel points; the coordinate error after compensation was reduced by 58.33%. Subsequently, the compensated burr point trajectories are optimized by utilizing an intelligent optimization algorithm to generate the shortest grinding path. Through experimental analysis of the relationship between spindle speed and surface roughness, a grinding trajectory simulation model is constructed, and the simulation results are integrated into the robot system. Finally, actual wheel hub burr grinding experiments are performed to validate the effectiveness and practicality of the proposed solution.