Search Results (109)

(1) Minimally invasive techniques are standard in colorectal surgery, though complete mesocolic excision (CME) with central lymphadenectomy remains technically demanding. Robotic systems may address these challenges. While the DaVinci system is well established, the modular Dexter system allows rapid switching between laparoscopy and robotics. (2) This prospective single-surgeon study compared right hemicolectomy with CME and intracorporeal anastomosis using Dexter, DaVinci, and conventional laparoscopy in 75 patients (25 per group) at a German high-volume center. Outcomes assessed included operative time, complications, lymph node yield, and CME quality. (3) Mean operative time was longest with DaVinci (190.5 min) versus Dexter (164.8 min) and laparoscopy (152.6 min). Intracorporeal anastomosis was more frequent in robotic groups. No significant differences were found in lymph node yield, CME quality, postoperative complications, length of stay, or survival. (4) The ability to convert briefly to laparoscopy during Dexter procedures helped manage challenging steps, especially during the learning curve. The results suggest that Dexter is a safe, feasible alternative to established robotic and laparoscopic techniques, with the added benefits of flexibility and integration into existing workflows. Full article

Multi-fingered dexterous hands provide superior dexterity in complex manipulation tasks due to their high degrees of freedom (DOFs) and biomimetic structures. Inspired by the anatomical structure of human tendons and muscles, numerous robotic hands powered by pneumatic artificial muscles (PAMs) have been created to replicate the compliant and adaptable features of biological hands. Nonetheless, PAMs have inherent nonlinear and hysteresis behaviors that create considerable challenges to achieving real-time control accuracy and stability in dexterous hands. In order to address these challenges, this paper proposes a hierarchical control architecture that employs a fuzzy PID strategy to optimize the nonlinear control of pneumatic artificial muscles (PAMs). The FPGA-based hardware integrates a multi-channel digital-to-analog converter (DAC) and a multiplexed acquisition module, facilitating the independent actuation of 20 PAMs and the real-time monitoring of 20 joints. The software implements a fuzzy PID algorithm that dynamically adjusts PID parameters based on both the error and the error rate, thereby effectively managing the nonlinear behaviors of the hand. Experimental results demonstrate that the designed control system achieves high precision in controlling the angle of a single finger joint, with errors maintained within ±1°. In scenarios involving multi-finger cooperative grasping and biomimetic motion demonstrations, the system exhibits excellent synchronization and real-time performance. These results validate the efficacy of the fuzzy PID control strategy and confirm that the proposed system fulfills the precision and stability requirements for complex operational tasks, providing robust support for the application of PAM-driven multi-fingered dexterous hands. Full article

Real-time computational capability for simultaneous grasping force and displacement determination constitutes a critical enabler for stable and reliable grasping performance in dexterous robotic grasping. To accelerate the computational efficiency of LCP in the dexterous grasping problem, as well as to ensure the stability and reproducibility of the algorithm’s output, the NSNMGK method, which incorporates sequential projection iterations across all greedy-selected active constraint rows within each NSNGRK framework iteration cycle, is developed. In each NSNMGK iteration, sequential projection operations are systematically applied to all active constraint rows, satisfying the greedy criterion. This processing strategy ensures the full utilization of qualifying constraints within the greedy subset through a same generalized Jacobian evaluation per iteration cycle. The methodology effectively mitigates inherent limitations of conventional randomized row selection, including unpredictable iteration counts and computational overhead from repeated Jacobian updates, while maintaining deterministic convergence behavior. The method’s convergence theory is rigorously established, with benchmark analyses demonstrating marked improvements in computational efficiency over the NSNGRK framework. Experimental validation in dexterous robotic grasping scenarios further confirms enhanced convergence rates through reduced iteration counts and shortened computational durations relative to existing approaches. Full article

Background and Clinical Significance: Robotic surgery reduces the need for extensive surgical approaches and lowers perioperative complications. In particular, it offers enhanced dexterity, three-dimensional visualization, and improved precision in confined anatomical spaces. Pelvic masses pose significant challenges due to their close relationship with critical neurovascular structures, making traditional open or laparoscopic approaches more invasive and potentially riskier. Robot-assisted resection, combined with intraoperative neurophysiological monitoring, may therefore offer a safe and effective solution for the management of complex pelvic lesions. Case Presentation: An 18-year-old woman was incidentally diagnosed with an 11 cm asymptomatic pelvic mass located anterior to the sacrum. Initial differential diagnoses included neurofibroma, teratoma, and myelolipoma. Histopathological examination confirmed a ganglioneuroma. Following multidisciplinary discussion, the patient underwent a robot-assisted en bloc resection using the Da Vinci Xi multiport system. Preoperative planning was aided by 3D modeling and intraoperative navigation. Conclusions: Surgery lasted 322 min. Preoperative and postoperative eGFR values were 145.2 mL/min and 144.0 mL/min, respectively. The lesion measured 11 cm × 9 cm × 8 cm. The main intraoperative complication was a controlled breach of the iliac vein due to its close adherence to the mass. No major postoperative complications occurred (Clavien-Dindo Grade I). The drain was removed on postoperative day 3, and the bladder catheter on day 2. The patient was discharged on postoperative day 5 without further complications. Presacral ganglioneuromas are rare neoplasms in a surgically complex area. A multidisciplinary approach using robotic-assisted laparoscopy with nerve monitoring enables safe, minimally invasive resection. This strategy may help avoid open surgery and reduce the risk of neurological and vascular injury. Full article

Background/Objective: Parkinson’s disease (PD) is characterized by motor and cognitive impairments that significantly affect quality of life. Robotic-assisted therapies, such as the AMADEO^® system, have shown potential in rehabilitating upper limb function but are underexplored in PD. This study aimed to assess the effects of Robotic-Assisted Therapy (RAT) compared to Conventional Physical Therapy (CPT) on cognitive, motor, and functional outcomes in PD patients. Methods: A single-blind, randomized controlled trial was conducted with PD patients allocated to RAT or CPT. Participants were assessed at baseline (T0) and post-intervention (T1) using measures including MoCA, FAB, UPDRS-III, 9-Hole Peg Test, FMA-UE, FIM, and PDQ-39. Statistical analyses included ANCOVA and regression models. Results: RAT led to significant improvements in global cognition (MoCA, p < 0.001) and executive functioning (FAB, p = 0.0002) compared to CPT. Motor function improved, particularly in wrist and hand control (FMA-UE), whereas changes in fine motor dexterity (9-Hole Peg Test) were less consistent and did not reach robust significance. No significant improvements were observed in broader quality of life domains, depressive symptoms, or memory-related cognitive measures. However, quality of life improved significantly in the stigma subdomain of the PDQ-39 (p = 0.0075). Regression analyses showed that baseline motor impairment predicted cognitive outcomes. Conclusions: RAT demonstrated superior cognitive and motor benefits in PD patients compared to CPT. These results support the integration of robotic rehabilitation into PD management. Further studies with larger sample sizes and long-term follow-up are needed to validate these findings and assess their sustainability. Full article

As manufacturing transitions into the era of Industry 5.0, the demand for systems that are not only intelligent but also human-centric, resilient, and sustainable is becoming increasingly critical. This paper introduces the Human and Humanoid-in-the-Loop (HHitL) ecosystem, a novel framework that integrates both humans and humanoid robots as collaborative agents within cyber–physical manufacturing environments. Building on the foundational principles of Industry 5.0, the paper presents a 6P architecture that includes participation, purpose, preservation, physical assets, persistence, and projection. The core features of this ecosystem, including anthropomorphism, perceptual intelligence, cognitive adaptability, and dexterity/locomotion, are identified, and their enablers are also introduced. This work presents a forward-looking vision for next-generation manufacturing ecosystems where human values and robotic capabilities converge to form adaptive, ethical, and high-performance systems. Full article

Tendon-driven robots offer advantages in terms of their compliance, lightweight design, and remote actuation, making them ideal for applications requiring dexterity and safety. However, existing tendon-driven actuators often suffer from low integration and inaccurate proprioceptive sensing due to their complex pulley-based tension sensors and bulky angle sensors. This paper presents the design and control of a compact and proprioceptive modular tendon-driven actuator. The actuator features a simplified single-pulley tension sensing mechanism and a novel maze-slot fixation method, minimizing friction and maximizing the structural integrity. A 3D Hall effect sensor is employed for accurate estimation of the tendon length with minimal space usage. A feedforward PID controller and a model-based tendon length observer are proposed to enhance the dynamic performance and sensing accuracy. Bench tests demonstrate that the actuator achieves a high power density (0.441 W/g), accurate closed-loop tension control, and reliable tendon length estimations. The proposed design provides a practical and high-performance solution for tendon-driven robots, enabling more agile, compact, and robust robotic systems. Full article

To reduce weight and improve dexterity performance, dexterous robot fingers usually use tendons for transmission, which may lead to complex nonlinear control problems. In order to improve tracking performance in joint space, this paper proposes an anti-interference controller, which synthesizes the nonsingular fast terminal sliding mode technique. A flexible joint dynamic model is established considering the flexibility of the cable-driven mechanism. A nonlinear disturbance observer is adopted to estimate and compensate the system uncertainties and various disturbances, and global fast terminal sliding mode is used to ensure good control performance in both the reaching phase and the sliding mode phase. Furthermore, symmetry is used to simplify dynamic modeling and control design, and the stability of the controller is proven with Lyapunov theory. Finally, the effectiveness of the controller is verified through simulation experiments. The simulation results demonstrate that the proposed controller achieves a steady state in 0.3 s, higher tracking accuracy than the other controllers through quantitative analysis of MAE and MSE metrics, and stronger anti-interference capability, which can satisfy the requirements of finger dexterity operation. Full article

Teleoperation enables robots to perform tasks in dangerous or hard-to-reach environments on behalf of humans, but most methods lack operator immersion and compliance during grasping. To significantly enhance the operator’s sense of immersion and achieve more compliant and adaptive grasping of objects, we introduce a novel teleoperation method for dexterous robotic hands. This method integrates finger-to-finger force and vibrotactile feedback based on the Fuzzy Logic-Dynamic Compliant Primitives (FL-DCP) controller. It employs fuzzy logic theory to identify the stiffness of the object being grasped, facilitating more effective manipulation during teleoperated tasks. Utilizing Dynamic Compliant Primitives, the robotic hand implements adaptive impedance control in torque mode based on stiffness identification. Then the immersive bilateral teleoperation system integrates finger-to-finger force and vibrotactile feedback, with real-time force information from the robotic hand continuously transmitted back to the operator to enhance situational awareness and operational judgment. This bidirectional feedback loop increases the success rate of teleoperation and reduces operator fatigue, improving overall performance. Experimental results show that this bio-inspired method outperforms existing approaches in compliance and adaptability during teleoperation grasping tasks. This method mirrors how human naturally modulate muscle stiffness when interacting with different objects, integrating human-like decision-making and precise robotic control to advance teleoperated systems and pave the way for broader applications in remote environments. Full article

Robots are essential for carrying out tasks, for example, in a nuclear industry, where direct human involvement is limited. However, present-day nuclear robots are not versatile due to limited autonomy and higher costs. This research presents a merely teleoperated Dexter^TM nuclear robot’s transformation into an autonomous manipulator for nuclear sort and segregation tasks. The Dexter^TM system comprises a arm client manipulator designed to operate in extreme radiation environments and a similar single/dual-arm local manipulator. In this paper, initially, a kinematic model and convex optimization-based dynamic model identification of a single-arm Dexter^TM manipulator is presented. This model is used for autonomous Dexter^TM control through Robot Operating System (ROS). A new integration framework incorporating vision, AI-based grasp generation and an intelligent radiological surveying method for enhancing the performance of autonomous Dexter^TM is presented. The efficacy of the framework is demonstrated on a mock-up nuclear waste test-bed using similar waste materials found in the nuclear industry. The experiments performed show potency, generality and applicability of the proposed framework in overcoming the entry barriers for autonomous systems in regulated domains like the nuclear industry. Full article

Robotic manipulation of objects with diverse shapes, sizes, and properties, especially deformable ones, remains a significant challenge in automation, necessitating human-like dexterity through the integration of perception, learning, and control. This study enhances a previous framework combining YOLOv8 for object detection and LSTM networks for adaptive grasping by introducing Gaussian Processes (GPs) for robust grasp predictions and Transformer models for efficient multi-modal sensory data integration. A Random Forest classifier also selects optimal grasp configurations based on object-specific features like geometry and stability. The proposed grasping framework achieved a 95.6% grasp success rate using Transformer-based force modulation, surpassing LSTM (91.3%) and GP (91.3%) models. Evaluation of a diverse dataset showed significant improvements in grasp force modulation, adaptability, and robustness for two- and three-finger grasps. However, limitations were observed in five-finger grasps for certain objects, and some classification failures occurred in the vision system. Overall, this combination of vision-based detection and advanced learning techniques offers a scalable solution for flexible robotic manipulation. Full article

Background/Objectives: Robotic systems offer enhanced precision, dexterity, and visualization, which are essential in addressing the complex nature of plastic surgery procedures. Despite widespread adoption in other surgical specialties, such as urology and gynecology, their application in plastic surgery remains underexplored. This review examines the use of robotic systems in plastic and reconstructive surgery with a focus on clinical outcomes. Methods: A literature search was conducted using PubMed, Embase, Scopus, and Web of Science. Search terms included (“robotic surgery” OR “surgical robots”) AND (“plastic surgery” OR “reconstructive surgery”). Studies on clinical outcomes and biomimetic innovations published between 1980 and 2024 were included, while non-English, cadaver-based, and animal studies were excluded. Data were systematically extracted using Covidence and analyzed. Results: Twenty-nine studies were identified that evaluated the clinical outcomes of robotics in areas including breast reconstruction, microsurgery, and craniofacial procedures. Robotic systems like the Da Vinci and Symani platforms offer motion scaling, tremor elimination, and enhanced depth perception. In nipple-sparing mastectomies, they reduced skin necrosis rates from 8% to 2%, while in DIEP flap reconstruction, they enabled smaller fascial incisions (2.67 ± 1.13 cm vs. 8.14 ± 1.69 cm) and faster recovery with fewer complications. In microsurgery, they achieved 100% patency for vessels under 0.3 mm and a 25.2% limb volume reduction in lymphedema patients in 3 months. Conclusions: Robotic systems show significant promise, particularly in procedures such as nipple-sparing mastectomies, and have the potential to overcome challenges including surgeon fatigue. However, challenges such as longer operating times, high costs, and limited haptic feedback remain barriers to their adoption. Full article

Introduction: Robot-assisted minimally invasive gastrectomy (RAMIG) represents a significant advancement in the surgical management of gastric cancer, offering superior dexterity, enhanced visualization, and improved ergonomics compared to laparoscopic gastrectomy (LG). This review systematically evaluates the current evidence on perioperative outcomes, oncological efficacy, learning curves, and economic considerations, providing insights into RAMIG’s potential role in modern gastric cancer surgery. Methods: A thorough analysis of retrospective, prospective, and meta-analytic studies was conducted to compare RAMIG with LG. Key outcomes, including operative time, intraoperative blood loss, lymph node retrieval, postoperative complications, learning curve duration, and cost-effectiveness, were assessed. Emphasis was placed on both short-term and long-term oncological outcomes to determine the clinical value of RAMIG. Results: Evidence indicates that RAMIG is associated with reduced intraoperative blood loss, lower morbidity rates, and a shorter learning curve, with proficiency achieved after 11–25 cases compared to 40–60 cases for LG. The robotic platform’s articulated instruments and enhanced three-dimensional visualization enable more precise lymphadenectomy, particularly in complex anatomical regions. Despite these advantages, operative time remains longer, and costs remain higher due to system acquisition, maintenance, and consumable expenses. However, emerging data suggest a gradual narrowing of cost disparities. While short-term outcomes are favorable, further high-quality, multicenter studies are needed to validate long-term oncological efficacy and survival outcomes. Conclusion: RAMIG offers significant technical and clinical advantages over conventional LG, particularly in terms of precision and learning efficiency. However, the long-term oncological benefits and economic feasibility require further validation. Future research should focus on cost optimization, advanced technological integration such as near-infrared fluorescence and artificial intelligence, and multicenter trials to solidify RAMIG’s role as a standard approach for gastric cancer surgery. Full article

Studying the interactions between biological organisms and their environment provides engineers with valuable insights for developing complex mechanical systems and fostering the creation of novel technological innovations. In this study, we introduce a novel bio-inspired three degrees of freedom (DOF) spherical robotic manipulator (SRM), designed to emulate the biomechanical properties observed in nature. The design utilizes the transformation of spherical Complex Spatial Kinematic Pairs (CSKPs) to synthesize bio-inspired robotic manipulators. Additionally, the use of screw theory and the Levenberg–Marquardt algorithm for kinematic parameter computation supports further advancements in human–robot interactions and simplifies control processes. The platform directly transmits motion from the motors to replicate the ball-and-socket mobility of biological joints, minimizing mechanical losses, and optimizing energy efficiency for superior spatial mobility. The proposed 3DOF SRM provides advantages including an expanded workspace, enhanced dexterity, and a lightweight, compact design. Experimental validation, conducted through SolidWorks, MATLAB, Python, and Arduino, demonstrates the versatility and broad application potential of the novel bio-inspired 3DOF SRM, positioning it as a robust solution for a wide range of robotic applications. Full article

The combination of human cognitive skills and dexterity with the endurance and repeatability of robots is a promising approach to modern assembly. However, efficiently allocating tasks and planning an assembly sequence between humans and robots is a manual, complex, and time-consuming activity. This work presents a framework named “Extract–Enrich–Assess–Plan–Review” that facilitates holistic planning of human–robot assembly processes. The framework automatically Extracts data from heterogeneous sources, Assesses the suitability of each assembly step to be performed by the human or robot, and Plans multiple assembly sequence plans (ASP) according to boundary conditions. Those sequences allow for a dynamic adaptation at runtime and incorporate different human–robot interaction modalities that are Synchronized, Cooperative, or Collaborative. An expert remains in the loop to Enrich the extracted data, and Review the results of the Assess and Plan steps with options to modify the process. To experimentally validate this framework, we compare the achieved degree of automation using three different CAD formats. We also demonstrate and analyze multiple assembly sequence plans that are generated by our system according to process time and the interaction modalities used. Full article