Search Results (20,038)

Background: The Hugo™ Robotic-Assisted Surgery (Hugo™ RAS) system represents a novel advancement in robotic surgical technology. Despite this, there remains a scarcity of data regarding extraperitoneal robot-assisted radical prostatectomy (eRARP) using this system. Methods: We conducted a prospective study at Ospedale Regionale “F. Miulli” from June 2023 to January 2024, enrolling consecutive patients diagnosed with prostate cancer (PCa) undergoing eRARP ± lymph node dissection. All procedures employed a modular four-arm setup performed by two young surgeons with limited prior robotic surgery experience. This study aims to evaluate the safety and feasibility of eRARP using the Hugo™ RAS system, reporting comprehensive preoperative, intraoperative, and postoperative outcomes in the largest reported cohort to date. Results: A total of 50 cases were analyzed, with a mean patient age of 65.76 (±5.57) years. The median operative time was 275 min (Q1–Q3 150–345), and the console time was 240 min (Q1–Q3 150–300). The docking time averaged 10 min (Q1–Q3 6–20). There were no intraoperative complications recorded. Two major complications occurred within the first 90 days. At the 3-month mark, 36 patients (72%) achieved undetectable PSA levels (<0.1 ng/mL). Social continence was achieved by 66% of patients, while 40% maintained erectile function. Conclusions: eRARP utilizing the Hugo™ RAS system demonstrated effectiveness and safety in our study cohort. However, more extensive studies with larger cohorts and longer follow-up periods are necessary to thoroughly evaluate long-term outcomes. Full article

This study presents the design and a thorough analysis of a six-bar crank-driven leg mechanism integrated with a skew pantograph, developed for walking robots. The mechanism’s dimensions were optimized using a rigorous dimensional synthesis process in GIM software (version 2024). Subsequently, a detailed kinematic analysis was performed in GIM to simulate the leg’s motion trajectory, velocity, and acceleration. In parallel, kinematic equations were formulated using the vector loop method, implemented in MATLAB (version R2013-b), and compared with the GIM results for validation, demonstrating the strong agreement between both tools. These results confirm the mechanism’s ability to generate a compact, high-lift foot trajectory while maintaining system stability and energy efficiency. An inverse dynamic analysis was carried out to determine the actuator’s driving torque, ensuring efficient operation under expected load conditions. Furthermore, topology optimization conducted in SOLIDWORKS (version 2021) significantly reduced the weight of the ground-contacting link while preserving its structural integrity. A subsequent stress analysis validated the mechanical viability of the optimized design, supporting its feasibility for real-world implementation. This research provides a robust foundation for the development of a functional prototype. Its potential applications include mobile robots for sectors such as agriculture and all-terrain vehicles, where efficient, reliable, and adaptive locomotion is crucial. The proposed mechanism strikes an optimal balance between mechanical simplicity, cost-effectiveness, and high performance, making it well-suited for challenging operational environments. Full article

The tuning of fractional-order proportional-integral-derivative (FOPID) controllers for automatic voltage regulator (AVR) systems presents a complex challenge, necessitating the solution of real-order integral and differential equations. This study introduces the Dumbo Octopus Algorithm (DOA), a novel metaheuristic inspired by machine learning with animal behaviors, as an innovative approach to address this issue. For the first time, the DOA is invented and employed to optimize FOPID parameters, and its performance is rigorously evaluated against 11 existing metaheuristic algorithms using 23 classical benchmark functions and CEC2019 test sets. The evaluation includes a comprehensive quantitative analysis and qualitative analysis. Statistical significance was assessed using the Friedman’s test, highlighting the superior performance of the DOA compared to competing algorithms. To further validate its effectiveness, the DOA was applied to the FOPID parameter tuning of an AVR system, demonstrating exceptional performance in practical engineering applications. The results indicate that the DOA outperforms other algorithms in terms of convergence accuracy, robustness, and practical problem-solving capability. This establishes the DOA as a superior and promising solution for complex optimization problems, offering significant advancements in the tuning of FOPID for AVR systems. Full article

Industrial inspection is a well-known application area for unmanned aerial vehicles (UAVs), but are modern market-available drones fully suitable for inspections of larger-scale industrial facilities? This review summarizes the pros and cons of aerial large-scale facility inspection, distinguishing it from other inspection scenarios implemented with drones. Moreover, based on paper analysis and additionally performed experimental studies, it reveals specific issues related to modern commercial drone software and demonstrates that market-available UAVs (including DJI and Autel Robotics) more or less suffer from the same problems. The discovered issues include a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) shift, an identification of multiple images captured from the same point, limitations of custom mission generation with external tools and mission length, an incorrect flight time prediction, an unpredictable time of reaching a waypoint with a small radius, deviation from the pre-planned route line between two waypoints, a high pitch angle during acceleration/deceleration, an automatic landing cancellation in a strong wind, and flight monitoring issues related to ground station software. Finally, on the basis of the paper review, we propose solutions to these issues, which helped us overcome them during the first autonomous inspection of a 2400 megawatts thermal power plant. Full article

Robotic needle steering has become a topic of interest in intervention surgery. Yet, this surgical procedure poses challenges due to external disturbances and tissue movement. To address these challenges, several novel steering algorithms have been developed to guide the needle precisely from the entry point to the target point. However, some of these algorithms may cause additional trauma to patients. In this paper, we present a 3D optimal control algorithm for a curvature-controllable steerable (CCS) needle, aiming to achieve effective operations with minimal trauma. We derive a kinematics without duty cycle control strategy (needle shaft spin), propose a novel intraoperative motion planner for path replanning, and design a full-state feedback controller for accurate path tracking. A dynamic environment was simulated, and the optimal controller showed a better result (0.01 ± 0.01 mm) than the case (3.86 ± 1.32 mm) using a full-state feedback controller. The demonstration indicates that the optimal control system can safely, effectively, and accurately steer the needle to the target point in a dynamic environment. Full article

Robotic grasping is a common operation scenario in industry and agriculture, in which the force sensing function is a significant factor to achieve reliable grasping. Existing force sensing methods of flexible grippers require intelligent materials or force sensors embedded in the flexible gripper, which causes such problems of higher manufacturing requirements and contact surface properties changing. In this paper, a novel rope-driven force sensing flexible gripper is designed based on the fin-shaped gripper structure, which can realize the grasping sensing functions of contact nodes and contact forces without the need for force sensors. Firstly, the rope-driven force sensing flexible gripper is designed, including the driving unit, the transmission part, the gripper unit, and the force sensing unit. The force sensing unit and the gripper unit are connected by rope, and the prototype of the rope-driven force sensing flexible gripper is completed. Secondly, a force sensing algorithm and control system based on finite element method and grasping geometric relationship are designed to realize the rope-driven force sensing flexible gripper grasping control and sensor data acquisition and processing. Finally, the experimental system of the rope-driven force sensing flexible gripper is built, and the grasping experimental tests of objects with different diameters and different contact nodes are carried out to verify the force sensing function of the rope-driven force sensing flexible gripper. The force sensing flexible gripper designed in this paper can provide a new idea for the design and force sensing method of intelligent robotic grasping system in robotic teaching, scientific research, and industrial applications. Full article

Minimally invasive interventional surgery is commonly used for diagnosing and treating breast cancer, but the high fluidity and deformability of breast tissue reduce intervention accuracy. This study proposes a bionic breast interventional robot that mimics the scorpion’s predation process, actively manipulating tissue deformation to control target displacement and enhance accuracy. The robot’s structure is designed using a modular method, and its kinematics and workspace are analyzed and solved. To address the nonlinear breast tissue deformation problem, a hierarchical tissue method is proposed to simplify the three-dimensional problem into a two-dimensional one. A two-dimensional tissue deformation solver is established based on the minimum energy method for quick resolution. The problem is treated as quasi-static, deriving the displacement relationship between external manipulation points and internal tissue targets. The method of active manipulation of tissue deformation is simulated using MATLAB (2019-b) software to verify the feasibility of the method. Results show maximum errors of 1.7 mm for prostheses and 2.5 mm for in vitro tissues in the X and Y directions. This method improves intervention accuracy in breast surgery and offers a new solution for breast cancer diagnosis and treatment. Full article

To solve the problem of the lack of physical properties of pedicels and the changing pattern for designing the end-effector of tomato harvesting robot and different harvesting modes, research was conducted on the physical properties of tomato pedicels and their change patterns. Using a Universal TA texture analyzer, tensile, three-point bending, and shearing tests were performed on tomato pedicels in the early firm-ripening stage. The tomato variety used was Syngenta Spectrum, cultivated seasonally with two crops per year. Spring crop tomatoes were used in this study. The experimental results provide a theoretical basis for designing tomato harvesting robots across three harvesting modes. Tensile tests measured the pull-off force and tensile strength of the abscission zone with varying diameters. These results are crucial for designing robots using a tensile harvesting mode. The location of the tomato pedicel significantly affects the shearing force. A one-way test was conducted on the shearing part. The results showed that the shearing force and energy required for the proximal pedicel are significantly greater than for the distal pedicel. To reduce the shearing force and energy needed by the end-effector’s shearing mechanism on distal pedicels, a response surface test was conducted. Three factors were examined: shearing speed, angle, and distal pedicel diameter. Design–Expert software optimized these factors to minimize shearing energy and force, leading to the best shearing parameters for different distal pedicel diameters. From the three-point bending tests, the average maximum bending breaking force, bending modulus, and bending strength of the tomato abscission zone were determined. These findings offer a theoretical basis for designing tomato harvesting robots with a bending-type harvesting mode. Full article

Swarm Intelligence (SI) represents a paradigm shift in artificial intelligence, leveraging the collective behavior of decentralized, self-organized systems to solve complex problems. This study provides a comprehensive review of SI, focusing on its application to multi-robot systems. We explore foundational concepts, diverse SI algorithms, and their practical implementations by synthesizing insights from various reputable sources. The review highlights how principles derived from natural swarms, such as those of ants, bees, and birds, can be harnessed to enhance the efficiency, robustness, and scalability of multi-robot systems. We explore key advancements, ongoing challenges, and potential future directions. Through this extensive examination, we aim to provide a foundational understanding and a detailed taxonomy of SI research, paving the way for further innovation and development in theoretical and applied contexts. Full article

In the domain of bionic walking control for biped robots, optimizing the parameters of the central pattern generator (CPG) presents a formidable challenge due to its high-dimensional and nonlinear characteristics. The traditional particle swarm optimization (PSO) algorithm often converges to local optima, particularly when addressing CPG parameter optimization issues. To address these challenges, one improved particle swarm optimization algorithm aimed at enhancing the stability of the walking control of biped robots was proposed in this paper. The improved PSO algorithm incorporates a spiral function to generate better particles, alongside optimized inertia weight factors and learning factors. Evaluation results between the proposed algorithm and comparative PSO algorithms were provided, focusing on fitness, computational dimensions, convergence rates, and other metrics. The biped robot walking validation simulations, based on CPG control, were implemented through the integration of the V-REP (V4.1.0) and MATLAB (R2022b) platforms. Results demonstrate that compared with the traditional PSO algorithm and chaotic PSO algorithms, the performance of the proposed algorithm is improved by about 45% (two-dimensional model) and 54% (four-dimensional model), particularly excelling in high-dimensional computations. The novel algorithm exhibits a reduced complexity and improved optimization efficiency, thereby offering an effective strategy to enhance the walking stability of biped robots. Full article

Flexible grippers are a promising and pivotal technology for robotic grasping and manipulation tasks. Remarkably, magnetorheological (MR) materials, recognized as intelligent materials with exceptional performance, are extensively employed in flexible grippers. This review aims to provide an overview of flexible robotic grippers and highlight the application of MR materials within them, thereby fostering research and development in this field. This work begins by introducing various common types of flexible grippers, including shape memory alloys (SMAs), pneumatic flexible grippers, and dielectric elastomers, illustrating their distinctive characteristics and application domains. Additionally, it explores the development and prospects of magnetorheological materials, recognizing their significant contributions to the field. Subsequently, MR flexible grippers are categorized into three types: those with viscosity/stiffness variation capabilities, magnetic actuation systems, and adhesion mechanisms. Each category is comprehensively analyzed, specifying its unique features, advantages, and current cutting-edge applications. By undertaking an in-depth examination of diverse flexible robotic gripper types and the characteristics and application scenarios of MR materials, this paper offers a valuable reference for fellow researchers. As a result, it facilitates further advancements in this field and contributes to the provision of efficient gripping solutions for industrial automation. Full article

The kinetostatic compliance method (KCM) models protein molecules as nanomechanisms consisting of numerous rigid peptide plane linkages. These linkages articulate with respect to each other through changes in the molecule dihedral angles, resulting in a kinematic mechanism with hyper degrees of freedom. Within the KCM framework, nonlinear interatomic forces drive protein folding by guiding the molecule’s dihedral angle vector towards its lowest energy state in a kinetostatic manner. This paper proposes a numerical integrator that is well suited to KCM-based protein folding and overcomes the limitations of traditional explicit Euler methods with fixed step size. Our proposed integration scheme is based on pseudo-transient continuation with an adaptive step size updating rule that can efficiently compute protein folding pathways, namely, the transient three-dimensional configurations of protein molecules during folding. Numerical simulations utilizing the KCM approach on protein backbones confirm the effectiveness of the proposed integrator. Full article

This article addresses the scanning path plan strategy of a rover team composed of three rovers, such that the team explores unknown dark outer space environments. This research considers a dark outer space, where a rover needs to turn on its light and camera simultaneously to measure a limited space in front of the rover. The rover team is deployed from a symmetric base station, and the rover team’s mission is to scan a bounded obstacle-rich workspace, such that there exists no remaining detection hole. In the team, only one rover, the hauler, can locate itself utilizing stereo cameras and Inertial Measurement Unit (IMU). Every other rover follows the hauler, while not locating itself. Since Global Navigation Satellite System (GNSS) is not available in outer space, the localization error of the hauler increases as time goes on. For rover’s location estimate fix, one occasionally makes the rover home to the base station, whose shape and global position are known in advance. Once a rover is near the station, it uses its Lidar to measure the relative position of the base station. In this way, the rover fixes its localization error whenever it homes to the base station. In this research, one makes the rover team fully scan a bounded obstacle-rich workspace without detection holes, such that a rover’s localization error is bounded by letting the rover home to the base station occasionally. To the best of our knowledge, this article is novel in addressing the scanning path plan strategy, so that a rover team fully scans a bounded obstacle-rich workspace without detection holes, while fixing the accumulated localization error occasionally. The efficacy of the proposed scanning and localization strategy is demonstrated utilizing MATLAB-based simulations. Full article

Extra-skeletal Ewing sarcoma (EWS) occurs in about 12% of EWS patients; at the same time, primary involvement of the kidneys remains extremely rare. Since it was first described in 1975, only a small case series have been reported worldwide. About 95% of surgically treated patients with EWS of the kidney described in the literature underwent nephrectomy, and the remaining patients only had a tumor biopsy. Nephron-sparing surgery (NSS) has not been sufficiently investigated as an alternative in the local surgical treatment of localized disease, mostly as a result of technically unfeasible provisions of negative surgical margins. In this report, we present a unique case of primary EWS of the kidney with an asymptomatic course without radiographic signs that suggest a highly aggressive disease, successfully locally treated with robotic-assisted NSS. This report showcases that robotic-assisted NSS could be a feasible alternative in treatment of localized disease yielding equally good oncological results while, at the same time, creating better prerequisites for necessary adjuvant chemotherapy. Full article

This paper presents an innovative control strategy for robot arm manipulators, utilizing an adaptive sliding mode control with stochastic gradient descent (ASMCSGD). The ASMCSGD controller significant improvements in robustness, chattering elimination, and fast, precise trajectory tracking. Its performance is systematically compared with super twisting algorithm (STA) and conventional sliding mode control (SMC) controllers, all optimized using the grey wolf optimizer (GWO). Simulation results show that the ASMCSGD controller achieves root mean squared errors (RMSE) of $0.12758$ for ${\theta }_1$ and $0.13387$ for ${\theta }_2$. In comparison, the STA controller yields RMSE values of $0.1953$ for ${\theta }_1$ and $0.1953$ for ${\theta }_2$, while the SMC controller results in RMSE values of $0.24505$ for ${\theta }_1$ and $0.29112$ for ${\theta }_2$. Additionally, the ASMCSGD simplifies implementation, eliminates unwanted oscillations, and achieves superior tracking performance. These findings underscore the ASMCSGD’s effectiveness in enhancing trajectory tracking and reducing chattering, making it a promising approach for robust control in practical applications of robot arm manipulators. Full article