Use Of Robotic Systems Research Articles

Basic performance of domestic surgical robot and the safety and effectiveness of integrated energy equipment.

Surgical robot system has broken the limitation of traditional surgery and shown excellent performance in surgery, and has been widely used in minimally invasive treatment in most areas of surgery. This study aims to verify the basic performance of the domestic surgical robot system and the safety and effectiveness of the integrated bipolar electrocoagulation and ultrasonic knife. The basic performance of the domestic surgical robot system was evaluated by completing the square knot and surgical knot, vertical and horizontal perforation and right ring perforation and suture, as well as picking up beans. Compared with laparoscopy, the safety and effectiveness of the domestic surgical robot after integrated interconnection bipolar electrocoagulation and ultrasonic scalpel were evaluated by detecting the vascular closure performance and the degree of histopathological damage in animals. Compared with freehand knotting, domestic robot knotting speed and circumference were slightly worse, but better than laparoscopic knotting. There was no statistical significance in the tension difference of the surgical knots among the 3 methods (P>0.05), but the tension of the square knots made by the freehand and the domestic surgical robot was greater than that of the laparoscopy (P<0.05). The space required for both the left and right forceps heads of knots was smaller than that of laparoscopy (P<0.001), which successfully completed the 4 quadrant suture tasks, and the time of picking up beans was significantly less than that of laparoscopy (P<0.05). There was no significant difference in the temperature of the liver tissue after the bipolar electrocoagulation between the interconnected domestic surgical robot and the laparoscopy (P>0.05), and the acute thermal injury was observed under the light microscope. The temperature of the liver tissue treated by the domestic robotic ultrasound knife was higher than that of the laparoscopic ultrasound knife (P<0.05). Domestic surgical robots are obviously superior to laparoscopy in suturing, knotting, and moving objects, and domestic surgical robots' interconnect bipolar electrocoagulation and ultrasonic knife have achieved success in animal experiments, and hemostasis is considered to be safe and effective.

Adaptive Dynamic Programming-Based Cross-Scale Control of a Hydraulic-Driven Flexible Robotic Manipulator

This paper focuses primarily on adaptive dynamic programming (ADP)-based tracking control of the hydraulic-driven flexible robotic manipulator system (HDFRMS) with varying payloads and uncertainties via singular perturbation theory (SPT). Firstly, the dynamics is derived using a driven Jacobin matrix, which represents the coupling between the hydraulic servo-driven system and rigid–flexible manipulator established using the assumed mode method and Lagrange principle. Furthermore, the whole dynamic model of the manipulator system is decoupled into a second slow subsystem (SSS), a second fast subsystem (SFS) and a first fast subsystem (FFS). The three subsystems can describe a large range of movement, flexible vibration and electro-hydraulic servo control, respectively. Hereafter, an adaptive dynamic programming trajectory tracking control law with a critic-only policy iteration algorithm is presented in the second slow timescale, while both robust optimal control (ROC) in the second first timescale and adaptive sliding mode control (ASMC) in the first fast timescale are also designed using the Lyapunov stability theory. Finally, the numerical simulations are carried out to illustrate the rightness and robustness of the singular perturbation decomposition and proposed composite control algorithm.

Robust adaptive control design of nonlinear systems with input delay

AbstractIn this paper a robust adaptive controller for a class of uncertain nonlinear systems is developed with additive disturbance and unknown time‐varying input delay. The control law is composed of a Desired Compensation Adaptation Law (DCAL) based feedforward term for the adaptation of uncertain plant parameters and two robust feedback terms based on the integral of the past control to compensate the input delay. The sufficient conditions on controller gains and limit on delay bound are derived through stability analysis by choosing suitable Lyapunov‐Krasovskii functionals which gives a globally uniformly ultimately bounded (GUUB) tracking. The performance of the controller is evaluated by performing simulation on a two link robot manipulator system for different values of the input delay.

Switched Controller Design for Robotic Manipulator via Neural Network-Based Sliding Mode Approach

In this brief, a novel adaptive switched controller of uncertain industry robotic manipulator (IRM) systems with switching loads is designed for trajectory tracking by utilizing switching neural network-based sliding mode control (SNNSMC) scheme. However, the error accumulation and system instability of the IRM may be caused by switching loads, while the existing single adaptive controller may not effectively deal with this issue. To do so, the IRM with switching loads is modeled as a switched system with multi-modal. Besides, the individual sub-controller with adaptive gain algorithm is devised to replace the single adaptive controller with fixed-gain for multi-modality system. Then, the radial basis function NN (RBFNN) is utilized for approaching to the plant, which avoids the limitation of the accurate model for the IRM system with switching loads. The SMC gain is designed as an adaptive adjustment value, which can be adjusted in real time to enhance the robustness of the system in spite of unknown disturbances and uncertainties. Subsequently, based on average dwell time (ADT) principle, the trajectory tracking error being close to zero is verified by the multi-Lyapunov function method. Finally, simulation results show that the provided method can both track the preset trajectory accurately, and attenuate chattering and high-speed switching of the plant effectively.

A novel fixed‐time sliding mode control for nonlinear manipulator systems based on adaptive disturbance observer

AbstractConsidering that in the trajectory tracking control of a nonlinear robotic manipulator system, the control effect is easily limited by the initial state of the system, and the system has modeling error, unknown disturbance, and friction in the actual control, to overcome the above problems, a fixed‐time sliding mode control (SMC) strategy based on adaptive disturbance observer (ADO) is developed in this paper. Firstly, feedforward compensation of the system is achieved by developing an ADO to accurately estimate the compound disturbances in the system. Second, a new fixed‐time sliding mode (SM) surface is presented to overcome the singularity issue and accelerate the error convergence. In addition, to enhance the performance of the reaching phase, a variable exponential power reaching law (VEPRL) is developed, which can effectively adjust the convergence rate. Through rigorous theoretical analysis, it is shown that the system state can be stabilized at a fixed time, and an upper bound on the convergence time is also given. Finally, the effectiveness of the control method is verified by comparing it with different control schemes in simulation.

Study on the Relevance of Robotics Technology

Introduction. Robotic technologies serve as an important indicator of the technological and economic state of the country, they also affect the lives of individuals. Their development is a promising and urgent task affecting many aspects of the life of modern society. Currently, robotics is going through another stage of development, which has its own characteristics, new directions. The work aims at analyzing the situation and ways of development of this industry in the world and in our country, as well as the attitude of people to the use of robotic systems and their willingness to improve them. The topic under consideration is understudied. Materials and Methods. In the presented work, the historical aspects of the formation of unique robotic technologies are defined, the ratio of the number of manufacturers and consumers of robotic products in the world is specified, the areas of the robotic technology application are named. The central place in the research is given to the results of an online survey conducted by the authors. Its statistical analysis made it possible to study, using specific data, the factors that influence the spread of robotic systems and robotic technologies and contribute to them. Results. The survey results, on the one hand, showed a high assessment of the prospects of robotic systems given by the respondents, and confirmed that young people have an interest in robot-making technologies. On the other hand, they allowed us to note the high level of knowledge in the field of robotic technologies among engineering students and the presence of more than elementary knowledge of these technologies among representatives of other special fields. In the opinion of the authors, there are all prerequisites for the further successful development of these technologies. Discussion and Conclusions. The analysis of the use of robotics in the world and in our country, as well as the results of the survey conducted by the authors, enable to conclude that these technologies are developing and will continue to develop actively, and the interest of current students in this, confirmed by the answers to the questionnaire, will contribute to the wider introduction of robots into the lives of future generations.

Advancing Condition Assessment of Reinforced Concrete Bridge Elements Through Automation, Visualization, and Improved Interpretation of Multi-NDE Technology Data

Economic bridge management requires accurate information about the condition of bridges in the network. Nondestructive evaluation (NDE)has shown high potential in providing accurate condition assessment and, through periodic surveys, development of accurate deterioration, predictive, and life-cycle cost models. To achieve wide adoption by transportation agencies, further advances should be made that would lead to the accuracy of NDE-based condition assessment, reduced costs and traffic interruptions, and minimized risk to transportation workers. The paper discusses the following areas of improvement: increased speed and safety of data collection through the use of robotic systems, and improved data interpretation through visualization and joint analysis of data collected by multiple NDE technologies.

Review of intelligent control methods for manipulators with rigid links

Robotic manipulators, which are mechanical devices with electronic control and several links, used to perform specific tasks under certain circumstances are considered. A detailed review of intelligent control methods implemented in robotic manipulator systems is presented. Keywords systems, control, robotics, automation, neural network, fuzzy logic, intelligent control

Robotic Manipulation System Design and Control for Non-Contact Remote Diagnosis in Otolaryngology: Digital Twin Approach

Since the COVID-19 pandemic, the development of a non-contact medical robot that prevents infection is increasingly required due to the medical field’s heavy workload and emotional stress. To reduce workload and prevent infection, we propose a non-contact robotic diagnostic system that can be applied to conventional otolaryngology clinics. A digital twin model was used to leverage the initial design optimisation of the diagnostic robotic system. The proposed system was developed based on a master-slave robot architecture. The slave robot comprises a flexible endoscope manipulation robot and a parallel robot arm that controls other medical instruments. In contrast to the conventional approach that employs two hands, our flexible endoscope manipulation robot employs a novel 4 degrees of freedom (DOF) control mechanism to deal with an endoscope with a single robotic arm. Phantom experiments on the flexible endoscope system were performed to confirm the effectiveness of the proposed system. The experimental results demonstrate the usability of the proposed system in terms of three evaluation metrics (i.e. diagnosis completion time, NASA task load index (NASA-TLX), and subjective risk score).

Integration of Robotic Vision and Automatic Tool Changer Based on Sequential Motion Primitive for Performing Assembly Tasks

Studies on intelligent robotic manipulation systems have typically focused on the programming efficiency, adaptive control of robotic arms, motion planning of robotic arms, and action diversity of grippers. In this study, a decision tree and visual recognition incorporate into a robotic arm to help it learn complex tasks. This study employed a task tree for the automatic planning of complex tasks, in which a decision-making model was used to generate complex task sets from a pre-built motion dataset in real-time performance. Moreover, the model can analyze the rationality of model steps, introduce new tasks, and perform object analysis. This work applies a support vector machine to identify the state of an object. The model selects a suitable gripper with a rapid gripper switch process by considering the characteristics of the targeting object. This study demonstrated the effectiveness of the proposed approach with suitable intelligence through the assembly task.

Practical Prescribed-Time Control with Time Varying Gains for Nonlinear Systems

Since convergence times of finite-time stabilization and fixed-time stabilization depend on the initial values, prescribed-time control plays a very significant role in practical applications. However, most of the existing control methods to achieve prescribed-time stabilization do not take the convergence accuracy into account. To this end, in this paper, a practical prescribed-time controller is proposed to guarantee that the tracking error of the nonlinear system can converge to prescribed accuracy within a prescribed time. Meanwhile, the convergence time is independent of the initial conditions. Combined with sliding mode control and time-varying gains, it is shown that the tracking error is guaranteed to be within the prescribed accuracy after the prescribed time. The developed control method is supported by some simulations of the tracking control of robot manipulator systems.

Towards the use of robotic systems at nuclear facilities

Abstract The article addresses the matter of the mobile robotic systems (MRS) application at nuclear industry and nuclear energy facilities. Three main types of tasks solved with the help of MRS in order to ensure safety and reduce the radiation load on personnel are described. The scope of application and general requirements for such MRS, typical technological operations, control modes and human-robot interaction are discussed. It is shown that highly specialized robots are mainly used and that their design doesn’t imply a prompt replacement of the payload and adaptation to various tasks. It is noted that most of the modern MRS designed to operate at radiation-hazardous facilities are still teleoperated. The necessity of creating multifunctional modular reconfigurable MRS of various standard-size groups for use at nuclear facilities and increasing the level of their autonomy is substantiated. Key words Mobile robotic system, UGV, nuclear facility, teleoperation, supervisory control. Acknowledgements This work was carried out within the framework of the state assignment of the Ministry of Education and Science of Russia No. 075-00913-21-00 dated December 25, 2020 «Development of proposals for improving the state scientific, technical and industrial policy and popularizing achievements in the field of robotics based on an analysis of trends in the development of robotics in Russia and abroad».

The feasibility of robot-assisted radical cystectomy: an experimental study.

To investigate the feasibility of the avatera system for performing robot-assisted radical cystectomy (RARC) followed by intracorporeal reconstruction of ileal neobladder. Six anesthetized female pigs were used to perform RARC and formation of the intracorporeal orthotopic ileal neobladder. Three surgeons with different level of expertise in the use of robotic systems performed RARC after a short instructional lecture and a 2-h hands-on training. The urinary diversion phase was performed only by the experienced surgeon. The successful completion of the procedure, operative time for demolitive and reconstructive phases, intraoperative blood loss and malfunction of the robotic system were evaluated. An improvement of the operative time was observed for each surgeon. The experienced surgeon, the fellow and the resident improved their time by 11, 15, and 22min, respectively, between the first and the second RARC performed. The most significant time difference was demonstrated by the resident, who had no previous experience in using robotic systems. The time difference between the first and the last orthotopic ileal neobladder creation for the experienced surgeon was 51min. The feasibility of avatera system for performing complex surgical procedure such as RARC with intracorporeal neobladder formation was demonstrated by this experimental study. No major bleeding or severe malfunctions were observed during the procedures. Significant improvement in operative time was demonstrated with the increasing experience from 1st to 6th case.

Warehouse Drone: Indoor Positioning and Product Counter with Virtual Fiducial Markers

The use of robotic systems in logistics has increased the importance of precise positioning, especially in warehouses. The paper presents a system that uses virtual fiducial markers to accurately predict the position of a drone in a warehouse and count items on the rack. A warehouse scenario is created in the simulation environment to determine the success rate of positioning. A total of 27 racks are lined up in the warehouse and in the center of the space, and a 6 × 6 ArUco type fiducial marker is used on each rack. The position of the vehicle is predicted by supervised learning. The inputs are the virtual fiducial marker features from the drone. The output data are the cartesian position and yaw angle. All input and output data required for supervised learning in the simulation environment were collected along different random routes. An image processing algorithm was prepared by making use of fiducial markers to perform rack counting after the positioning process. Among the regression algorithms used, the AdaBoost algorithm showed the highest performance. The R2 values obtained in the position prediction were 0.991 for the x-axis, 0.976 for the y-axis, 0.979 for the z-axis, and 0.816 for the γ-angle rotation.

Practically Robust Fixed-Time Convergent Sliding Mode Control for Underactuated Aerial Flexible JointRobots Manipulators

The control of an aerial flexible joint robot (FJR) manipulator system with underactuation is a difficult task due to unavoidable factors, including, coupling, underactuation, nonlinearities, unmodeled uncertainties, and unpredictable external disturbances. To mitigate those issues, a new robust fixed-time sliding mode control (FxTSMC) is proposed by using a fixed-time sliding mode observer (FxTSMO) for the trajectory tracking problem of the FJR attached to the drones system. First, the underactuated FJR is comprehensively modeled and converted to a canonical model by employing two state transformations for ease of the control design. Then, based on the availability of the measured states, a cascaded FxTSMO (CFxTSMO) is constructed to estimate the unmeasurable variables and lumped disturbances simultaneously in fixed-time, and to effectively reduce the estimation noise. Finally, the FxTSMC scheme for a high-order underactuated FJR system is designed to guarantee that the system tracking error approaches to zero within a fixed-time that is independent of the initial conditions. The fixed-time stability of the closed-loop system of the FJR dynamics is mathematically proven by the Lyapunov theorem. Simulation investigations and hardware tests are performed to demonstrate the efficiency of the proposed controller scheme. Furthermore, the control technique developed in this research could be implemented to the various underactuated mechanical systems (UMSs), like drones, in a promising way.

Anthropomorphic Grasping of Complex-Shaped Objects Using Imitation Learning

This paper presents an autonomous grasping approach for complex-shaped objects using an anthropomorphic robotic hand. Although human-like robotic hands have a number of distinctive advantages, most of the current autonomous robotic pickup systems still use relatively simple gripper setups such as a two-finger gripper or even a suction gripper. The main difficulty of utilizing human-like robotic hands lies in the sheer complexity of the system; it is inherently tough to plan and control the motions of the high degree of freedom (DOF) system. Although data-driven approaches have been successfully used for motion planning of various robotic systems recently, it is hard to directly apply them to high-DOF systems due to the difficulty of acquiring training data. In this paper, we propose a novel approach for grasping complex-shaped objects using a high-DOF robotic manipulation system consisting of a seven-DOF manipulator and a four-fingered robotic hand with 16 DOFs. Human demonstration data are first acquired using a virtual reality controller with 6D pose tracking and individual capacitive finger sensors. Then, the 3D shape of the manipulation target object is reconstructed from multiple depth images recorded using the wrist-mounted RGBD camera. The grasping pose for the object is estimated using a residual neural network (ResNet), K-means clustering (KNN), and a point-set registration algorithm. Then, the manipulator moves to the grasping pose following the trajectory created by dynamic movement primitives (DMPs). Finally, the robot performs one of the object-specific grasping motions learned from human demonstration. The suggested system is evaluated by an official tester using five objects with promising results.

A mechanics-based approach to realize high-force capacity electroadhesives for robots.

Materials with electroprogrammable stiffness and adhesion can enhance the performance of robotic systems, but achieving large changes in stiffness and adhesive forces in real time is an ongoing challenge. Electroadhesive clutches can rapidly adhere high stiffness elements, although their low force capacities and high activation voltages have limited their applications. A major challenge in realizing stronger electroadhesive clutches is that current parallel plate models poorly predict clutch force capacity and cannot be used to design better devices. Here, we use a fracture mechanics framework to understand the relationship between clutch design and force capacity. We demonstrate and verify a mechanics-based model that predicts clutch performance across multiple geometries and applied voltages. On the basis of this approach, we build a clutch with 63 times the force capacity per unit electrostatic force of state-of-the-art electroadhesive clutches. Last, we demonstrate the ability of our electroadhesives to increase the load capacity of a soft, pneumatic finger by a factor of 27 times compared with a finger without an electroadhesive.

Command filter‐based adaptive neural network controller design for uncertain nonsmooth nonlinear systems with output constraint

SummaryThis paper investigates the command filter‐based adaptive neural network tracking control problem for uncertain nonsmooth nonlinear systems. First, an integral barrier Lyapunov function is introduced to deal with the symmetric output constraint and make the output comply with prescribed restrictions. Second, by the Filippov's differential inclusion theory and approximation theorem, the considered nonsmooth nonlinear system is converted to an equivalent smooth nonlinear system. Third, the Levant's differentiator is used to deal with the “explosion of complexity” problem. An error compensation mechanism is established to attenuate the effect of the filtering error on control performance. Then, an adaptive neural network controller is set up by resorting to the backstepping technique. It is strictly mathematically proved that the tracking error can converge to an arbitrarily small neighborhood of the origin and all the signals in the closed‐loop system are semi‐globally uniformly ultimately bounded. Finally, a numerical example and an application example of the robotic manipulator system are provided to demonstrate the availability of the proposed control strategy.

Adaptive predefined‐time observer design for generalized strict‐feedback second‐order systems under perturbations

AbstractThis article studies the predefined‐time observation problem of generalized strict‐feedback second‐order systems commonly encountered in practice. A nonaffine control input, a matched perturbation, and nonlinear functions satisfying the Hölder growth condition are considered. A class of adaptive predefined‐time observers (PTOs) is designed to reconstruct unknown states and lumped perturbations based on a barrier function and specific time‐varying functions. Distinct from existing observers for strict‐feedback systems, the PTO accomplishes a complete reconstruction in a predefined time with trivial peaking observation errors. Furthermore, the upper bound of the settling time of the PTO is tightly and explicitly predefined by only one design parameter, and is irrelevant to the initial conditions. Hence, tuning procedures for the physically realizable temporal demands become less conservative and more straightforward. Moreover, the adaptive coefficients of the PTO are self‐adjusted along with the magnitude of perturbation, efficiently improving the transient performance under wide‐range perturbations. Finally, numerical simulations on a robot manipulator system demonstrate the effectiveness of the PTO.

Observer-based free-will arbitrary time sliding mode control for uncertain robotic manipulators

This paper studies a free-will arbitrary time sliding mode control (FATSMC) based on the predefined-time sliding mode observer (PTSMO) for tracking control of robotic manipulators. First, a PTSMO is constructed to estimate the coupled uncertainty of the robotic manipulator system in a preset time. Then, a FATSMC scheme is proposed to realize the free-will arbitrary time tracking control for uncertain robotic manipulators and preset the upper bound on the settling time in the reaching phase. The proposed control strategy has high tracking accuracy and smooth control torque, while the convergence time of the system is nonconservative. The stability of the FATSMC and the PTSMO are rigorously demonstrated using the Lyapunov stability theory. Finally, a three-degree-of-freedom uncertain manipulator is utilized for numerical simulation. The effectiveness and superiority of the proposed control strategy are demonstrated by comparing it with several control strategies.