Application Of Robotic System Research Articles

Modeling and Experimental Validation of High‐Flow Fluid‐Driven Membrane Valves for Hyperactuated Soft Robots

Herein, the design, modeling, and validation of high‐flow, fluid‐driven, membrane valves tailored specifically for applications in soft robotic systems are described. Targeting the piping problem in hyper‐actuated soft robots, two fluid‐driven membrane valve designs that can admit flows of up to while weighing less than are introduced. A mathematical model to predict fluid flow by representing the displacement of the membrane as a scalar quantity influenced by the balance of pressures applied across the valve's ports is established. The model incorporates six parameters with direct physical relevance, enhancing its usefulness in valve design and system integration. In an experimental validation, flow rates with deviations within 4% are predicted and the onset of flow is correctly identified with an error rate of less than 1%. In addition, applications of these valves for flow amplification and for the creation of a fluid‐driven oscillator are experimentally demonstrated. This research contributes to the advancement of soft robotics by providing a tool for designing, optimizing, and controlling fluid‐driven systems and it lays the groundwork for the future development of embedded, fluid‐controlled valve networks that can be used to realize hyper‐actuated soft robotic systems.

Graph-based meta-learning for context-aware sensor management in nonlinear safety-critical environments

This study introduces a novel framework for optimizing energy efficiency and computational load in safety-critical robotic systems operating in nonlinear domains. Leveraging Graph Attention Networks for state awareness and decision-making, the framework employs adaptive sensor and filter toggling strategies to dynamically manage system resources through real-time inferential processes. Our framework maintains continuous robot operation in the presence of sensor noise and environmental disturbances by activating additional sensors, thus preventing system shutdowns or stalls. Few-shot meta-learning techniques further augment the model's adaptability, allowing it to generalize and make real-time decisions across varying operational conditions. An extensive evaluation reveals a reduced average energy consumption, compared to ‘always-on’ configurations, by 13.71% and CPU utilization by 29.07%, without compromising system performance and safety. We also introduce Matching Networks and Siamese Networks with different loss functions to assess the system's capability to adapt to different levels of criticality. Our experiments demonstrate that the system prioritizes performance and safety in high-critical scenarios while maximizing energy efficiency in less critical situations. The framework's real-time decision-making capability is particularly crucial in human–robot environments and holds significant implications for future applications in nonlinear control systems and resilient robotic systems.

Design and Simulation of Autonomous Up-and-down System for Transmission Line Live Operation Robot with the UAV Assistance

Aiming at the current bottleneck problem that restricts the practical application of robot system engineering in transmission line live operation, in order to improve the on-line and off-loading efficiency of live working robots on transmission lines and improve the practical level of the robot system as a whole, this paper proposes a design and operation method of autonomous on-line and off-loading mechanism of double-arm live working robots based on UAV assistance. Firstly, the realization process of autonomous up-and-down of the robot was analyzed, based on which the structure of the auxiliary hook of the up-and-down system and the lifting winch were designed. Then, the force analysis was carried out on the up-and-down process of the robot, and the appropriate winch drive motor was selected through theoretical calculation. In order to ensure the rationality of the up-and-down process, the operation motion planning was carried out on the up-and-down of the robot. Stress analysis and simulation were carried out on the maximum load bearing point of the auxiliary hook and the walking wheel mechanical arm under different states, and the results showed that the hook and the robot arm could meet the requirements of the robot loading and unloading, and the whole robot loading and unloading system was designed reasonably. Compared with the traditional loading and unloading system designed in this paper, the loading and unloading efficiency could be improved. It has important theoretical significance for the development of the physical system of robot uplink and down.

Dual-Event-Triggered Intelligence Security Control for Multiagent Systems Against DoS Attacks With Applications in Mobile Robot Systems

The intelligence security control problem of multiagent systems in the presence of actuator faults is investigated against denial-of-service (DoS) attacks. Noticing that the DoS attacks usually make the actuator signal not available for distributed control protocols, a novel intelligence dual-eventtriggered control strategy is developed to eliminate the impact of DoS attacks. This control strategy includes two related event triggers respectively set on different channels, in which the second event-triggered condition is related to the triggering sequence of the previous event. Through the coordination between two nested event triggers set on different channels, the controller is avoided by being affected by wrong signals caused by DoS attacks. The developed distributed control protocol can not only guarantee the realization of the tracking control task but also ensure that no ‘Zeno behavior” occurs in the process of the event triggering. Moreover, the obtained results are applied to the mobile robot systems. Finally, a numerical simulation example and a mobile robot system application are given to verify the feasibility and effectiveness of the proposed control strategy.

Progress of Research on the Application of Robotic Surgical System in Gynecologic Malignancies

Progress of Research on the Application of Robotic Surgical System in Gynecologic Malignancies

Concurrent Learning-Based Adaptive Control of Underactuated Robotic Systems With Guaranteed Transient Performance for Both Actuated and Unactuated Motions.

With the wide applications of underactuated robotic systems, more complex tasks and higher safety demands are put forward. However, it is still an open issue to utilize "fewer" control inputs to satisfy control accuracy and transient performance with theoretical and practical guarantee, especially for unactuated variables. To this end, for underactuated robotic systems, this article designs an adaptive tracking controller to realize exponential convergence results, rather than only asymptotic stability or boundedness; meanwhile, unactuated states exponentially converge to a small enough bound, which is adjustable by control gains. The maximum motion ranges and convergence speed of all variables both exhibit satisfactory performance with higher safety and efficiency. Here, a data-driven concurrent learning (CL) method is proposed to compensate for unknown dynamics/disturbances and improve the estimate accuracy of parameters/weights, without the need for persistency of excitation or linear parametrization (LP) conditions. Then, a disturbance judgment mechanism is utilized to eliminate the detrimental impacts of external disturbances. As far as we know, for general underactuated systems with uncertainties/disturbances, it is the first time to theoretically and practically ensure transient performance and exponential convergence speed for unactuated states, and simultaneously obtain the exponential tracking result of actuated motions. Both theoretical analysis and hardware experiment results illustrate the effectiveness of the designed controller.

Merging Minds and Machines: The Role of Advancing AI in Robotics

The relentless pursuit of creating intelligent robotic systems has led to a symbiotic relationship between human inventiveness and artificial intelligence (AI). Artificial intelligence is a theory. It is the development of computer systems that are able to perform tasks that would require human intelligence. This abstract explores the pivotal role that AI plays in advancing the capabilities and applications of robotic systems. The integration of AI algorithms and machine learning techniques has launched robotics beyond mere automation, enabling machines to modify, alter, adjust, learn, and interact with the world in ways previously deemed science fiction. Design fictions that vividly imagines future scenarios of AI or robotics in use offer a means both to explain and query the technological possibilities. Examples of these tasks are visual perception, speech recognition, decision-making, and translation between languages. The three key dimensions of AI’s role in robotics are Cognitive Augmentation, Human-Robot Collaboration, and Autonomous Intelligence. The abstract also discusses the societal implications of this AI-driven advancement in robotic systems, including ethical considerations, job market impacts, and the democratization of access to advanced technology. The convergence of human intellect and artificial intelligence in robotics marks a transformative era where machines become not just tools, but companions, collaborators, and cognitive extensions of human capabilities. Researchers are taking inspiration from the brain and considering alternative architectures in which networks of artificial neurons and synapses process information with high speed and adaptive learning capabilities in an energy-efficient, scalable manner. The indispensable role of AI in shaping the future of robotic systems and bridging the gap between human potential and machine capabilities is highlighted. The major impact of this synergy reverberates across industries, promising the world where robots become not just mechanical contraptions / defective apparatus but intelligent partners in our journey of progress.

Evaluation of an ultrashort pulsed laser robot system for flexible and large-area micromachining

We report for the first time on the realization, characterization and application of an ultrashort pulsed laser robot system for flexible and large-area 2D and 3D laser micromachining with 6 articulated axes. To characterize the dynamic positioning of the laser beam during and after axes movement, CMOS image sensors were integrated into the beam path. A method introduced for the alignment of the optical axes allows a reduction of the deviations in laser beam positioning to less than 141.8 ± 92.9 μm within a 110° rotation range of axis 4. In addition, a high laser beam positioning repeatability of less than 102.2 μm is demonstrated over a total period of 14 h for a movement of axis 5 within a range of 0° to 90°. Initial laser cutting, laser structuring and laser marking applications on automotive dashboards and glass substrates are presented for flexible and large area 2D and 3D manufacturing. By applying a special laser cutting strategy for processing AF 32 eco thin glass, high cutting quality is achieved with a taper of up to 96.3% without the generation of cracks, demonstrating the innovative potential of the high-precision laser robot system. Nonetheless, different identified inherent influences of each axis 1–5 during robot axis movement demand for an innovative beam stabilization concept to achieve high precision in laser beam positioning.

Flexible tactile sensor with an embedded-hair-in-elastomer structure for normal and shear stress sensing

Endowing robots with multi-directional tactile sensing capabilities has long been a challenging task in the field of flexible electronics and intelligent robots. This paper reports a highly sensitive, flexible tactile sensor with an embedded-hair-in-elastomer structure, which is capable of decoupling normal stress and shear stress. The flexible tactile sensor is fabricated on a thin polyimide substrate and consists of four self-bending piezoresistive cantilevers in a cross-shaped configuration, which are embedded in an elastomer. The sensor can decouple the tactile information into a normal stress and a shear stress with simple summation and differencing algorithms, and the measurement error is kept within 3%. Moreover, the sensitivity and detection threshold of the sensor can be adjusted by simply changing the elastic material. As a demonstration, the flexible tactile sensor is integrated into a robotic manipulator to precisely estimate the weight of the grasped objects, which shows great potential for application in robotic systems.

Research into the Beetle Antennae Optimization-Based PID Servo System Control of an Industrial Robot

Industrial robot speed control remains a critical aspect for efficient operations, especially given the challenges of nonlinearity and multivariable characteristics inherent to servo motor control systems, as well as energy inefficiencies due to a lack of automatic speed control. This study refines an existing control algorithm, beetle antennae optimization (BAO), by integrating elements of particle swarm optimization (PSO) and a beetle antennae search algorithm (BAS), further enhanced by chaos mapping and an adaptive weighting factor. These modifications aim to improve the algorithm’s search capabilities and mitigate the risks of settling into local optima. Unlike previous iterations, this study includes rigorous dynamic and stability analyses focusing on key performance metrics such as settling time, overshoot, and steady-state error. Comparative Simulink/MATLAB modeling demonstrates that the enhanced BAO algorithm significantly outperforms traditional PID control, BAS, and adaptive weighted-PSO in reducing static error, overshoot, and adjustment time under various conditions, including scenarios with external disturbances. Our results indicate a 60% improvement in the optimization performance of speed curve metrics, confirming the enhanced efficacy and robustness of the robotic control system. This research offers valuable insights into the advantages of the refined BAO algorithm, providing a comprehensive basis for its practical application in industrial robotic control systems.

Parametric configuration and programming of flexible robotic cells producing biotechnology experiments

In the last decades there has been an ever-increasing trend of accelerated scientific research in the biotechnology sector and the COVID-19 pandemic has further emphasized the need for high-throughput yet flexible processes. Even though automation has gained significant popularity to deal with this problem, the focus has been placed at laboratory level and for specific type of processes only. The aim of these automation applications is to offset the increasing costs of clinical trials, automate tedious laboratory work, run experiments in parallel and make scientific testing efficient and programmable. This paper investigates the application of robotic systems at a larger scale that will allow the automated execution of even custom defined biotechnology experiments so that these can be offered as a service to any interested stakeholder. A digital model of the proposed robotic biotechnology workcell is developed and the necessary methodology is investigated to showcase the feasibility of this approach. The goal is to use the digital model for evaluating different design considerations, such as equipment layout and robot types, as well as for planning and simulating the robot operation under various offline programming strategies. To achieve this goal, the Damped Least Squares Method is used for inverse kinematics control and a robotic arm trajectory planner is developed parametrically using characteristic intermediate points so as to create a motion plan that can be used for any robotic manipulator and any experiment within a family of facility setups. Two experiments are simulated using a SCARA robot and an articulated arm robot, each with an alternative cell layout to show the flexibility and robustness of the proposed approach. The obtained results show that the trajectory planner can consistently generate appropriate motion plans.

Sustainable Crop Protection via Robotics and Artificial Intelligence Solutions

Agriculture 5.0 refers to the next phase of agricultural development, building upon the previous digital revolution in the agrarian sector and aiming to transform the agricultural industry to be smarter, more effective, and ecologically conscious. Farming processes have already started becoming more efficient due to the development of digital technologies, including big data, artificial intelligence (AI), robotics, the Internet of Things (IoT), and virtual and augmented reality. Farmers can make the most of the resources at their disposal thanks to this data-driven approach, allowing them to effectively cultivate and sustain crops on arable land. The European Union (EU) aims to make food systems fair, healthy, and environmentally sustainable through the Green Deal and its farm-to-fork, soil, and biodiversity strategies, zero pollution action plan, and upcoming sustainable use of pesticides regulation. Many of the historical synthetic pesticides are not currently registered in the EU market. In addition, the continuous use of a limited number of active ingredients with the same mode of action scales up pests/pathogens/weed resistance potential. Increasing plant protection challenges as well as having fewer chemical pesticides to apply require innovation and smart solutions for crop production. Biopesticides tend to pose fewer risks to human health and the environment, their efficacy depends on various factors that cannot be controlled through traditional application strategies. This paper aims to disclose the contribution of robotic systems in Agriculture 5.0 ecosystems, highlighting both the challenges and limitations of this technology. Specifically, this work documents current threats to agriculture (climate change, invasive pests, diseases, and costs) and how robotics and AI can act as countermeasures to deal with such threats. Finally, specific case studies and the application of intelligent robotic systems to them are analyzed, and the architecture for our intelligent decision system is proposed.

Combined Application of a Novel Robotic System and Exoscope for Microsurgical Anastomoses: Preclinical Performance

Abstract Background The concept of robotic microsurgery is becoming increasingly known as several robotic systems tailored to the specific needs of microsurgery are being introduced. Training with these devices is essential to draw conclusions about their potential clinical utility. This study describes the training and learning curve of experienced microsurgeons and complete novices using such a robotic surgical system in combination with an exoscope. Methods Four experienced microsurgeons and three complete novices performed a total of 62 manual and robot-assisted anastomoses. The time for anastomosis completion and surgeon's satisfaction with the anastomosis and with the robotic system were recorded. The anastomoses' quality was assessed using the Structured Assessment of Microsurgery Skills (SAMS) and the Anastomosis Lapse Index (ALI). The Rapid Entire Body Assessment (REBA) was used for ergonomics evaluation. Results All expert microsurgeons and novices improved their performance during training. The average anastomosis time decreased significantly, while satisfaction with the anastomosis and robotic system increased significantly over time. Multiple SAMS score parameters increased significantly throughout robotic but not manual training and the ALI score demonstrated more errors in the manual group. The REBA score displayed a significantly lower risk for musculoskeletal disorders in the robotic group. Conclusion Currently, the first clinical applications of robotic surgical systems specifically designed for microsurgery are being reported. The introduction of such systems into clinical practice can be expected to have a steep learning curve, as demonstrated in our study. Meanwhile, robotic systems for microsurgical procedures may hold great potential for improvement of surgical quality and ergonomics.

Synthesis of a parametrically invariant servo drive using the model parameters recovery method

RELEVANCE. Servo drives operate with a law of change of the setting action unknown in advance and provide reproduction of this law by the output coordinate. Servo drives find application in robotic and mechatronic systems, machine tools, systems of automatic control and remote transmission of information, radar stations, guidance units, etc. The operation of servodrives often proceeds in conditions of instability parameters and characteristics elements of the electric drive. Corrective devices synthesized by classical methods of automatic control theory cannot cope with providing the specified accuracy of reproduction of the input signal and the required quality of transients. THE PURPOSE. In this regard, an important and urgent task is the synthesis of an active correction system with a non-stationary controller that provides the required quality and accuracy of the control process due to the coefficient self-tuning algorithm. METHODS. When solving this problem, methods for identifying parameters based on the gradient algorithm and numerical integration of the object of study dynamics equations, implemented by means of the MatLab software environment, were used. RESULTS. The paper solves the problem of synthesizing the self-tuning algorithm for the coefficients of the servo drive corrective device based on the identification approach. The parameters are identified by a searchless gradient algorithm while minimizing the discrepancy between the object of study and its inverse model, as well as restoring the coefficients of differential equations using integration and the corresponding computational procedures. An servo drive with negative position feedback is tuned to the modular optimum with a proportional controller whose coefficients are completely determined by the parameters to be identified. The self-tuning algorithm consists in calculating the correction factor of the non-stationary P-controller and forming a multiplicative channel of the active correction closed loop. CONCLUSION. The simulation of the electric drive in the MatLab software environment showed high accuracy and quickness of the process identifying parameters in a wide range of their change. When forming an active correction contour, a necessary requirement is to distinguish between the identification cycle and the self-tuning cycle. This makes it possible to avoid singular perturbations and reduce resonant facts during the operation of a parametrically invariant electric drive. The developed method of active correction with a priori known and unchanged structure of the object model of study makes it possible to maintain the required accuracy and quality of the operation of the electric drive under conditions of parametric disturbances with permissible deviations of accuracy and quality indicators. Implementation of the method does not require additional equipment, organization of special test signals, significant computational costs. The method of synthesizing a parametrically invariant electric drive can be used to develop robust control systems for non-stationary objects, including when the hypothesis of quasi-stationarity is not fulfilled.

Motor controlled system development with force-assistance using force/torque sensor for four-axis ceiling suspension system

Multi-axis force and torque sensors are type of transducers. They can measure force and torque in three dimensions. These sensors have multiple applications in robotic systems. The majority of these type of sensors use the strain measurement in any elastic structure. There are multiple approaches for strain measurement which include resistive strain gauge, piezo-electric and capacitive technologies. The evaluation of such a system can be done on various parameters, maximizing the acceptable sensing range while minimizing measurement errors/crosstalk is the main design challenge. In future, because force and torque sensors are necessary for service machines to interact with people in different situations, they will be widely deployed. However, it is challenging to find an appropriate force/torque sensor, and the cost is very high, because of certain design concerns and needs. This paper discusses an application based on the multi-axis force/torque sensor. A force-assisted control system has been proposed with simultaneous motorized action using force as feedback. The sensor needs and machine performance are reviewed after a thorough analysis of relevant data. This thorough investigation will benefit in the interfacing of specialized force/torque sensors to reduce crosstalk and aid in broadening the scope of service machines in which they can be used.

Design and Modeling of Fabric-Shelled Pneumatic Bending Soft Actuators

In recent years, the use of fabric to manufacture and integrate soft actuators and sensors into wearable robots elucidates the benefits of soft robotic technology, closing the gap between humans and machines. However, many limitations in terms of ease of manufacturing, customization, modeling, output force and control remain unsolved and reduce the number of practical applications of fabric-based soft robotic systems. To address these limitations, we propose the use of laser-cut fabric shells laminated onto thermoplastic polyurethane as a pneumatic bending soft actuator. In this paper the design, fabrication, modeling, and control of a fabric shelled bending actuator is presented. Our experiments suggest that the fabric-shelled actuators are highly tunable and usable for high force applications. Moreover, the finite element model developed can predict the behavior of different fabric shelled actuator designs. Finally, we show how precise control of the actuators can be achieved by the implementation of a feedback curvature control system.

Robotic Hysterectomy with Ureter Identification and Uterine Artery Ligation for Benign Gynecological Conditions: An Early-Year Single-Center Experience.

The use and application of robotic systems with a high-definition, three-dimensional vision system and advanced EndoWrist technology have become widespread. We sought to share our clinical experience with ureter identification and preventive uterine artery ligation in robotic hysterectomy. The records of patients undergoing robotic hysterectomy between May 2014 and December 2015, including patient preoperative characteristics, operative time, and postoperative outcomes, were analyzed. We evaluated the feasibility and safety of using early ureteral identification and preventive uterine artery ligation in robotic hysterectomy in patients with benign gynecological conditions. Overall, 49 patients diagnosed with benign gynecological conditions were evaluated. The mean age of the patients and mean uterine weight were 46.2 ± 5.3 years and 348.7 ± 311.8 g, respectively. Robotic hysterectomy achieved satisfactory results, including a short postoperative hospital stay (2.7 ± 0.8 days), low conversion rate (n = 0), and low complication rate (n = 1; 2%). The average estimated blood loss was 109 ± 107.2 mL. Our results suggest that robotic hysterectomy using early ureteral identification and preventive uterine artery ligation is feasible and safe in patients with benign gynecological conditions.

P-034 ROBOTIC TAPP FOR RE-RECURRENT INGUINAL HERNIA: OUR EARLY EXPERIENCE

Abstract Aim Undoubtedly, the use of meshes has dramatically reduced recurrence rates following primary inguinal hernia repair; however, recurrences still occur with an incidence ranging from 0.5 to more than 20%, depending upon various factors that include hernia and patient characteristics, applied surgical technique and mesh characteristics, and surgeon's experience. The repair of a recurrent inguinal hernia sets a challenge for the hernia surgeon to prevent not only new recurrence, but also other complications. The challenge becomes even bigger in cases of re-recurrent inguinal hernias. The aim of this study is to present our early experience with the application of robotic system in the repair of re-recurrent inguinal hernias. Material and Methods From January to December 2022, five male patients with re-recurrent inguinal hernias (two patients with two previous repairs, one with three and one with four previous repairs) were operated in our Center using the robotic system Da Vinci X. In all cases robotic TAPP was performed and an extra-large (measuring 12×17cm) heavy-weight polypropylene mesh was implanted. Results All patients were discharged from the hospital on the first post-operative day. Their postoperative course has been uneventful, without any evidence of early or late complications, including recurrence. Conclusion Robotic-assisted repair of re-recurrent inguinal hernia is safe and feasible. The advantages of the surgical robotic system and more specifically, the magnified and three-dimensional view of the surgical field, as well as the increased maneuverability of the instruments permit the efficient treatment of this demanding subtype of inguinal hernias.

Application of hybrid robotic systems in crop harvesting: Kinematic and dynamic analysis

Harvesting of agricultural crops, especially when the ripe products have to be picked one by one, has always been a major concern of farmers and growers. Consequently, the use of robots and the mechanization of harvesting operations have been considered as the effective means of tackling such challenges. In the present research, the use of hybrid robots (which combine open-loop and closed-loop manipulators) has been proposed for this purpose; because these robots help reduce the size of the workforce and thus slash the harvesting costs, and they also boost the speed and accuracy of harvesting operations. However, using this type of robots without paying attention to some important factors (e.g., using motors of optimal power at the actuated joints of a robot and choosing appropriate lengths for robot links) can have a substantial adverse effect on the manufacturing cost, working space and the effective functioning of such robots. So the main focus of this paper is on the kinematic and dynamic analysis of hybrid robotic systems. The main advantage of this research over similar works is the generality by which it describes the mechanical behavior of these robots; so that by using the recursive algorithms presented in this paper, all kinds of robots in the hybrid class can be analyzed. However, due to the complexity in the analysis of such systems, a hybrid robot consisting of three closed loops and one open loop was considered in this paper as a sample case study, so that we could explain, visually and graphically, all the steps involved in deriving its motion equations. The simulation results for this robotic system demonstrate the efficacy of the proposed method in the kinematic and dynamic analysis of hybrid robots, as efficient and functional crop harvesting equipment.

The Folded Pneumatic Artificial Muscle (foldPAM): Towards Programmability and Control via End Geometry

Soft pneumatic actuators have seen applications in many soft robotic systems, and their pressure-driven nature presents unique challenges and opportunities for controlling their motion. In this work, we present a new concept: designing and controlling pneumatic actuators via end geometry. We demonstrate a novel actuator class, named the folded Pneumatic Artificial Muscle (foldPAM), which features a thin-filmed air pouch that is symmetrically folded on each side. Varying the folded portion of the actuator changes the end constraints and, hence, the force-strain relationships. We investigated this change experimentally by measuring the force-strain relationship of individual foldPAM units with various lengths and amounts of folding. In addition to static-geometry units, an actuated foldPAM device was designed to produce continuous, on-demand adjustment of the end geometry, enabling closed-loop position control while maintaining constant pressure. Experiments with the device indicate that geometry control allows access to different areas on the force-strain plane and that closed-loop geometry control can achieve errors within 0.5% of the actuation range.