Use Of Robotic Systems Research Articles

Robotic manipulation of cardiomyocytes to identify gap junction modifiers for arrhythmogenic cardiomyopathy.

Arrhythmogenic cardiomyopathy (ACM) is a leading cause of sudden cardiac death among young adults. Aberrant gap junction remodeling has been linked to disease-causative mutations in plakophilin-2 (PKP2). Although gap junctions are a key therapeutic target, measurement of gap junction function in preclinical disease models is technically challenging. To quantify gap junction function with high precision and high consistency, we developed a robotic cell manipulation system with visual feedback from digital holographic microscopy for three-dimensional and label-free imaging of human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). The robotic system can accurately determine the dynamic height changes in the cells' contraction and resting phases, microinject drug-treated healthy and diseased iPSC-CMs in their resting phase with constant injection depth across all cells, and deposit a membrane-impermeable dye that solely diffuses between cells through gap junctions for measuring the gap junction diffusion function. The robotic system was applied toward a targeted drug screen to identify gap junction modulators and potential therapeutics for ACM. Five compounds were found to dose-dependently enhance gap junction permeability in cardiomyocytes with PKP2 knockdown. In addition, PCO 400 (pinacidil) reduced beating irregularity in a mouse model of ACM expressing mutant PKP2 (R735X). These results highlight the utility of the robotic cell manipulation system to efficiently assess gap junction function in a relevant preclinical disease model, thus providing a technique to advance drug discovery for ACM and other gap junction-mediated diseases.

Distributed leader-following bipartite consensus for one-sided Lipschitz multi-agent systems via dual-terminal event-triggered mechanism

This article analyses leader-following bipartite consensus for one-sided Lipschitz multi-agent systems by dual-terminal event-triggered output feedback control approach. A distributed observer is designed to estimate unknown system states by employing relative output information at triggering time instants, and then an event-triggered output feedback controller is proposed. Dual-terminal dynamic event-triggered mechanisms are proposed in sensor–observer channel and controller–actuator channel, which can save communication resources to a great extent, and the Zeno behavior is ruled out. A new generalized one-sided Lipschitz condition is proposed to handle the nonlinear term and achieve bipartite consensus. Some stability conditions are presented to guarantee leader-following bipartite consensus. Finally, one-link robot manipulator systems are introduced to demonstrate the availability of the designed scheme. The results demonstrate that the agents of the robot manipulators can track the reference trajectories bi-directionally, and effectively reduce communication resources by 61.22% and 68.04% at the sensor–observer and controller–actuator channels, respectively.

Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions

The global demand for button mushrooms has surged in recent years, driven by their health benefits, creating a significant challenge for the mushroom industry in meeting this increasing demand. The increasing reliance on human labor, which is becoming unsustainable due to labor shortages and rising wage costs, highlights the urgent need for automated harvesting solutions. This review examines the integration of automated systems in button mushroom harvesting, delving into the key components such as robots, mechanisms, machine elements, programming, and algorithms. It offers a thorough analysis of the performance, design, operational mechanisms, and advantages and limitations of robotic systems, comparing the different methods employed in automated harvesting. This paper compares the performance of all the mushroom harvesters, including the commercially available ones with manual harvesting, and identifies their potential and limitations. The commercial harvesters are shown to pick 2000 mushrooms per hour on average, which is similar to how much a skilled worker picks at the same time. However, commercial automation harvesting has a relatively low success rate, high initial cost, high operating cost, and energy consumption, identifying areas for future research and challenges. This paper serves as a valuable resource for researchers and industry professionals striving to advance automated harvesting technology and improve its efficiency in meeting the rising demand for button mushrooms.

Disturbance observer based adaptive predefined‐time sliding mode control for robot manipulators with uncertainties and disturbances

AbstractThis article develops a predefined‐time sliding mode control approach for systems with external disturbances and uncertainties through a nonlinear disturbance observer (DO). For addressing predefined‐time stabilization problem of robotic manipulator system, a predefined‐time sliding mode surface is proposed, ensuring system states converge to origin within a predefined‐time once sliding mode surface is attained. Compared to conventional fixed‐time and finite‐time control strategies, a distinctive advantage of this scheme is that system settling time can be explicitly chosen in advance and independent of system states. To achieve predefined‐time performance, a disturbance observer is introduced to generate the disturbance estimate, which can be incorporated into controller to counteract disturbance. To address the systems uncertainty, an adaptive law is employed to estimate the unknown upper boundary of system uncertainties. Finally, the effectiveness and performance of the proposed scheme are illustrated by simulation and experiment.

A retrospective study on two different surgical robots to assist total knee arthroplasty

To compare early clinical and imaging results of domestic HURWA and imported Brainlab Knee3 surgical robot-assisted knee replacement. A retrospective analysis was performed on 93 patients with knee osteoarthritis (KOA) who underwent robot-assisted descending total knee arthroplasty(TKA) from January 2021 to July 2023, and they were divided into BRATKA group and HRATKA group according to use of robotic system. There were 40 patients in BRATKA group, including 16 males and 24 females, aged from 55 to 90 years old with an average of (64.3±7.0) years old;27 patients with grade Ⅲ and 13 patients with grade Ⅳ according to Kellgren-Lawrence(K-L);18 patients on the right side and 22 patients on the left side;the courses of disease ranged from 1 to 30 years with an average of (15.3±7.6) years;imported Brainlab Knee3 surgical robot assisted system was adopted. There were 53 patients in HRATKA group, including 18 males and 35 females, aged from 52 to 81 years old with an average of (64.4±8.5) years old;30 patients with grade Ⅲ and 23 patients with grade Ⅳ;21 patients on the right side and 32 patients on the left side;the courses of disease ranged from 1 to 32 years with an average of (16.4±7.9) years;HURWA surgical robot assisted system was adopted. Operation time, perioperative total blood loss, incision length and postoperative complications were compared between two groups. Deviation angle of hip-knee-ankle angle(HKAA) before operation and on the first day after operation was compared between two groups. Later tibal component (LTC), frontal femoral component (FFC), later femoral component (LFC) and frontal tibal component(FTC) at 1 day after on the first day after operation was compared between two groups. Knee Society score(KSS), visual analogue scale (VAS) and range of motion (ROM) of knee joint were compared between two groups before operation and on the 3rd and 90th day after operation. Both groups were followed up for 11 to 18 months with an average of (14.4±2.1) months, and the wounds of all patients healed well. Operation time and incision length of BRATKA group were (132.1±34.6) min and (12.9±1.9) cm, while (94.1±10.8) min and (14.8±2.1) cm in HRATKA group, respectively, and the differences between two groups were statistically significant(P<0.05). There were no significant difference in perioperative total blood loss and preoperative deviation angle of HKAA between two groups(P>0.05). Deviation angle of HKAA, FFC angle and LFC angle in BRATKA group were (1.90±0.91) °, (87.90±1.51) ° and(9.00±3.2) °, respectively;while (0.93±1.04) °, (89.03±0.96) ° and (7.63±0.59) ° in HRATKA group, respectively, and the differences between two groups were statistically significant (P<0.05). There were no significant differences in FTC and LTC between two groups(P>0.05). There were no significant differences in VAS of knee rest and exercise, KSS score and ROM of knee joint between two groups before operation and 3 days and 90 days after operation(P>0.05). There was no significant difference in complications between two groups (P>0.05). Postoperative imaging of two robot systems showed good lower limb force line. The domestic HRATKA group had better LFC, FFC angle and HKA deviation angle than the imported BRATKA group, but there were no significant difference in postoperative knee function and pain relief.

Automated robotic systems in surgical practice

The use of robotic systems has long gone beyond experimental medicine. More than 200 thousand operations per year are carried out with the use of just the most popular robotic complex Da Vinci. Further development of robotics will contribute to improvement of quality and accuracy of surgical interventions. Even now, it enables reduction of postoperative complications to almost zero. This review presents the analysis of the results of introducing robots into surgery. An overview of the data presented in PubMed, Cochrane Library, Science Direct and eLIBRARY was performed.

Robotic solutions for precision agriculture

The robotic system is a key component in modern agriculture. The article aims to expand robotic solutions for precision agriculture and improve productivity, resource utilization, decision-making, sustainability, and farmworker safety. It aims to automate repetitive tasks, gather real-time data, promote sustainable practices, and reduce risks for farmworkers. They offer numerous potential solutions to issues related to the growing global population, changing demographics, and economic status. This article investigates how robotic systems can be significant to precision agriculture. Traditional farming is facing issues such as climate change, resource depletion, labour shortage, etc. The use of robotic systems makes precision agriculture achievable and it provides a sustainable solution. This study examined the importance of robotics in agricultural processes such as planting, seeding, weeding, and harvesting. The potential benefits of robotic solutions, such as increased efficiency, reduced labour costs, and improved crop yield, are explored. The article identifies key challenges and opportunities associated with robotic implementation in agriculture. The research aids in creative effective agriculture techniques by imaging the future use of robotics in agriculture.

Event‐triggered optimal control based on prescribed performance for a two‐link robotic manipulator

SummaryIn this article, an event‐triggered optimal control method based on prescribed performance is investigated for a constrained two‐link robotic manipulator system. To satisfy the performance constraints of the system, an equated error model of the dynamics model is established by means of two auxiliary functions and the prescribed performance technique. A predictive controller based on an adaptive event triggering mechanism is obtained by solving a constraint optimization problem for this transformed error model, and the triggering threshold can be adjusted based on real‐time changes of the system. This controller realizes the tracking of the joint angle with the desired angle and meets the prescribed performance conditions. Finally, the control algorithm is shown to be an effective method for improving control performance through numerical simulations and comparison with other prescribed performance functions.

Observer-Based Finite-Time Prescribed Performance Sliding Mode Control of Dual-Motor Joints-Driven Robotic Manipulators with Uncertainties and Disturbances

Considering system uncertainties (e.g., gear backlash, unmodeled dynamics, nonlinear friction and parameters perturbation) coupling disturbances weaken the motion performance of robotic systems, an observer-based finite-time prescribed performance sliding mode control with faster reaching law is proposed for robotic manipulators equipped with dual-motor joints (DMJs). In the case where the backlash information is completely unknown, the backlash is maximally eliminated using a simple but efficient dual-motor adaptive anti-backlash strategy. Thus, the design of position tracking controllers for DMJs can be simplified. Then, to deal with the influence of disturbances and residual uncertainties (excluding backlash), a novel finite-time adaptive sliding mode disturbance observer (ASMDO) is proposed to practically estimate the lumped uncertainties where their upper bounds are assumed to be unknown. Finally, a finite-time composite fast non-singular terminal sliding mode (TSM) controller, integrated with the prescribed performance principle, is proposed in this paper. To enhance the convergence rate, a novel TSM-type reaching law has been developed. The controller ensures that the tracking error is not only stabilized within a finite-time convergence rate but also adheres to a predefined maximum transient-steady-state error. The proposed scheme is implemented through simulation and experimental results, demonstrating its superior performance.

Adaptive estimator-based nonsingular fast terminal sliding mode control of robotic manipulator systems under FDI attacks and actuator failure

This paper is focused on the adaptive estimator-based super-twisting nonsingular fast terminal sliding mode control of manipulator systems with false data injection (FDI) attacks and actuator failure. First, a novel mathematical model is established for the robotic manipulator systems with parameter perturbation, FDI attacks, actuator failure, external disturbance, and joint friction. As the dynamics of FDI attacks are often unknown or unpredictable, a novel adaptive fixed-time estimator-based defense method is designed to actively eliminate the negative effects induced by the FDI attacks. Meanwhile, an adaptive super-twisting nonsingular fast terminal sliding mode controller (NFTSMC) is designed to realize rapid convergence and desirable tracking precision. After that, the fixed-time stability of the manipulator systems are proved using the Lyapunov function. Finally, the comparative simulation verifies the superiority of the proposed control strategy over the current control methods.

A novel affordable user interface for robotic surgery training: design, development and usability study.

The use of robotic systems in the surgical domain has become groundbreaking for patients and surgeons in the last decades. While the annual number of robotic surgical procedures continues to increase rapidly, it is essential to provide the surgeon with innovative training courses along with the standard specialization path. To this end, simulators play a fundamental role. Currently, the high cost of the leading VR simulators limits their accessibility to educational institutions. The challenge lies in balancing high-fidelity simulation with cost-effectiveness; however, few cost-effective options exist for robotic surgery training. This paper proposes the design, development and user-centered usability study of an affordable user interface to control a surgical robot simulator. It consists of a cart equipped with two haptic interfaces, a VR visor and two pedals. The simulations were created using Unity, which offers versatility for expanding the simulator to more complex scenes. An intuitive teleoperation control of the simulated robotic instruments is achieved through a high-level control strategy. Its affordability and resemblance to real surgeon consoles make it ideal for implementing robotic surgery training programs in medical schools, enhancing accessibility to a broader audience. This is demonstrated by the results of an usability study involving expert surgeons who use surgical robots regularly, expert surgeons without robotic surgery experience, and a control group. The results of the study, which was based on a traditional Peg-board exercise and Camera Control task, demonstrate the simulator's high usability and intuitive control across diverse user groups, including those with limited experience. This offers evidence that this affordable system is a promising solution for expanding robotic surgery training.

Voronoi-based adaptive area optimal coverage control for multiple manipulator systems with uncertain kinematics and dynamics

This paper studies an adaptive area optimal coverage control method for multi-manipulator systems under the presence of both uncertain kinematics and dynamics. Initially, an objective cost function associating the voronoi tessellation is utilized to transform the optimal coverage control into the tracking control problem. Consequently, an adaptive area optimal coverage control strategy is designed by using the adaptive dynamic and kinematic programming. In this control strategy, the sliding mode control technology for each robot manipulator system is created to pursue control optimality and prescribed coverage performance simultaneously. The adaptive control algorithm using the parameter linearization properties is further deployed to directly cope with the influence caused by the parameter uncertainties of manipulator dynamics and kinematics. Theoretical analysis is given to guarantee the stability and convergence of the proposed optimal area converge controller, subject to optimal cost. Illustrative example is provided to validate the performance of the proposed framework.

Development of a compliant gripper for safe robot-assisted trouser dressing-undressing

In recent years, many countries, including Japan, have rapidly aging populations, making the preservation of seniors' quality of life a significant concern. For elderly people with impaired physical abilities, support for toileting is one of the most important issues. This paper details the design, development, experimental assessment, and potential application of the gripper system, with a focus on the unique requirements and obstacles involved in aiding elderly or hemiplegic individuals in dressing and undressing trousers. The gripper we propose seeks to find the right balance between compliance and grasping forces, ensuring precise manipulation while maintaining a safe and compliant interaction with the users. The gripper's integration into a custom-built robotic manipulator system provides a comprehensive solution for assisting hemiplegic individuals in their dressing and undressing tasks. Experimental evaluations and comparisons with existing studies demonstrate the gripper's ability to successfully assist in both dressing and dressing of trousers in confined spaces with a high success rate. This research contributes to the advancement of assistive robotics, empowering elderly, and physically impaired individuals to maintain their independence and improve their quality of life.

Trajectory Analysis of 6-DOF Industrial Robot Manipulators by Using Artificial Neural Networks.

Robot manipulators are robotic systems that are frequently used in automation systems and able to provide increased speed, precision, and efficiency in the industrial applications. Due to their nonlinear and complex nature, it is crucial to optimize the robot manipulator systems in terms of trajectory control. In this study, positioning analyses based on artificial neural networks (ANNs) were performed for robot manipulator systems used in the textile industry, and the optimal ANN model for the high-accuracy positioning was improved. The inverse kinematic analyses of a 6-degree-of-freedom (DOF) industrial denim robot manipulator were carried out via four different learning algorithms, delta-bar-delta (DBD), online back propagation (OBP), quick back propagation (QBP), and random back propagation (RBP), for the proposed neural network predictor. From the results obtained, it was observed that the QBP-based 3-10-6 type ANN structure produced the optimal results in terms of estimation and modeling of trajectory control. In addition, the 3-5-6 type ANN structure was also improved, and its root mean square error (RMSE) and statistical R2 performances were compared with that of the 3-10-6 ANN structure. Consequently, it can be concluded that the proposed neural predictors can successfully be employed in real-time industrial applications for robot manipulator trajectory analysis.

Public's Perception and Knowledge of Using Robotics in General Surgery in Eastern Region, Saudi Arabia.

Robotics in general surgery is a field that involves the use of robotic systems to assist surgeons in performing various types of surgical procedures. The objective of this study was to evaluate the perception and knowledge of robotic surgery among the Eastern Region's population. This cross-sectional study used an electronic questionnaire that was developed using Google Docs. It included males and females aged above 18 years who lived in the Eastern Province. Participants who were below 18 or above 65 years of age, or non-Saudi, or people who lived in other than the Eastern Region of Saudi Arabia were excluded from the study. A total of 500 responses were received via the Google Form, and 81 subjects of them were excluded from the study. Approximately half of the participants were aware of the existence of general robotic surgery, while the other half had no prior knowledge about it. When assessing the participants' understanding of how robotic surgery works, a significant proportion provided incorrect responses. In terms of the advantages of general robotic surgery, the most commonly recognized benefit was that it makes the doctor's life easier, followed by more accurate surgical results. However, the participants' understanding of the disadvantages of robotic surgery was not as accurate. A substantial portion of participants were unsure about the disadvantages. The general public of the Eastern Region in Saudi Arabia showed a derated level of knowledge about the use of robotics in general surgery. Furthermore, a major portion of people were unaware of the availability of robotic surgery in Saudi Arabia. Educational programs are warranted to facilitate the implantation of robotic surgery in Saudi Arabia.

Prospects for the Use of Robotic Systems in Combat Operations

Prospects for the Use of Robotic Systems in Combat Operations

Comparative Analysis: Fractional PID vs. PID Controllers for Robotic Arm Using Genetic Algorithm Optimization

This paper presents a comparative analysis of a fractional-order proportional–integral–derivative (FO-PID) controller against the standard proportional–integral–derivative (PID) controller, applied to a nonlinear robotic arm manipulator systems. The genetic algorithm (GA) optimization method was implemented to tune the gain parameters of the FO-PID and PID controllers. The performance of the FO-PID and PID controllers were evaluated though different cost functions, including integral of squared error (ISE), integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and integral of time-weighted squared error (ITSE). The performance of these controllers was examined via extensive simulations by using MATLAB/SIMULINK for different operating scenarios of the robotic arm manipulator system. Based on the obtained results, a comparative performance matrix is proposed, wherein cost functions ISE, IAE, ITAE, and ITSE are represented as columns while characteristic parameters (overshoot, rising time, and settling time) are represented as rows. The proposed performance matrix facilitates the selection between the PID and FO-PID controllers.

Legal and ethical facets of robotic surgery: a suggestion for a guideline

Surgical negligence being a branch of medical negligence is considered a widely extended topic in the territory of Sri Lanka. The central focus of this paper is the use of robotic systems in the performance of surgical procedures and its legal and ethical facets. ‘Robotic Surgery’ is a novel form of technology that is efficient and productive and mostly preferred in sophisticated health systems and used in different fields of medicine namely cardiology, urology, oncology etc. In this method, the robots assist the surgeon in the performance of the surgery. The assistance given by a surgical robot is useful due to the efficiency, fewer incisions, less bleeding, and the lower possibility of infections. However, there are risks as well. If the risk and the injury were a result of the surgeon’s fault, this gives rise to medical malpractice litigation. If the injury is a consequence of the malfunctioning of the surgical robot, it will create a defective product liability on the side of the manufacturer of the robotic system. If the hospital has undertaken the specific surgical venture without sufficient resources, expertise, and institutional stability, that will lead to the hospital's liability. Even, the liability of an error caused by robotic surgery can be imposed in different ways; the ultimate accused will be the health sector. The author’s attempt in this paper is to discuss the legal and ethical implications of a surgeon-patient relationship in the performance of robotic surgery and thereby recommend a guideline to address legal and ethical issues relevant to robotic surgical procedures.

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

Abstract Under an event-triggered mechanism, a non-zero-sum (NZS) game optimal tracking control method for modular robot manipulator (MRM) systems with input constraints is proposed using the adaptive dynamic programming (ADP) method based on integral reinforcement learning (IRL). First, a dynamic model of the MRM system is developed based on joint torque feedback technology, consisting of an n-joint subsystem related to interconnected dynamic coupling (IDC). Second, we design a robust compensation controller to handle the known model term and an optimal compensation controller to deal with the uncertainty term caused by the IDC and friction, respectively. In addition, a nonlinear disturbance observer is established to dispose of the negative effects caused by the uncertain sensor output disturbance. Third, based on differential game theory, we transform the optimal tracking control problem of the MRM system into an n-player NZS game problem. Then, the IRL-based ADP method is adopted, which relaxes the need for system partial unknown dynamic information, and only a critic neural network is used to solve the coupled Hamilton–Jacobi equation, so as to obtain the optimal control policy. Then, using Lyapunov theory, the tracking error of the MRM system is demonstrated to be uniformly ultimately bounded. Finally, the effectiveness and superiority of the proposed algorithm are verified through experiments.

Design and Optimization of Mechano transduction Sensors for effective analysis of proteins with Robotic interference

Méchano transduction sensors convert mechanical inputs into electrical signals, allowing robots to detect and interact with their environments in various bio-imaging applications. Current bio-sensor systems have some drawbacks that prevent them from being widely used in robotic applications. These include low sensitivity, short durability, and expensive manufacturing costs regarding picture prediction and categorization. To overcome these obstacles and improve robotic mechanotransduction sensors for efficient protein analysis, this work suggests Unique Sensor Fabrication Techniques (USFT). These methods enhance sensor performance in protein analysis while keeping costs low and scalability high via integrating complex micro- and nano-scale materials and architectures. In comparison to traditional sensor designs, comprehensive evaluations based on predefined parametric criteria show significant improvements in areas such as sensitivity, response time, and predictability in protein biomolecules. In addition, assessments of scalability and manufacturability point to the possibility of widespread use in robotic systems for protein categorization and prediction. In bioimaging applications, this study helps advance sensor technology for reliable and efficient robot-environment interaction.