Search

Gas leaks from subsea oil and gas facilities could cause significant ocean environment damage. Such leaks can cause fire and explosion, for example, a fire on the ocean surface west of Mexico's Yucatan peninsula. Detecting a gas leak is critical in managing fire and explosion risks. This study proposes using autonomous underwater vehicles -robotic fish- for gas leak plume detection. The robotic fish is equipped with advance two well-known deep learning models, Faster RCNN and YOLOV4. A physical experiment system of various sizes of underwater gas leaks is used to generate the benchmark dataset. The results demonstrated the YOLOV4 model has a stronger online real-time capability. It is 43 times faster than the Faster RCNN model with the same level of accuracy. This study verifies the feasibility of integrating deep learning models with the mobile vehicle for real-time autonomous gas leak detection. This contribution will enable the development of a safe and reliable digital twin of subsea emergency management. [ABSTRACT FROM AUTHOR]

• Scheduling and routing of autonomous mobile vehicles in robotic mobile fulfillment systems. • Integrated assignment and sequencing of inventory pods and orders to picking stations. • Development of MIP models for both the sequential and integrated solution approaches. • Investigation of impact of the chosen performance metric in the optimization process. • An integrated approach leads to more efficient fulfillment systems. In robotic mobile fulfillment systems (RMFS), mobile robots carry inventory shelves autonomously from the storage area to picking stations and back. The scheduling of these robots and the order picking activities can be modeled as a collection of interrelated optimization problems. In this paper, we focus on the following optimization problems: the order allocation to picking stations, order sequencing, the inventory pod selection, and the robot scheduling. We present new mixed-integer programming (MIP) models for these decision problems and extended on existing models from the literature. To improve the models further, we include interstation travel which means that inventory racks can be transported from one picking station straight to another station without returning it to the storage area in between, hence reducing the overall travel distance. Moreover, In previous research on RMFS, only some of these decision problems are integrated. Therefore, we developed an integrated model to study the interdependencies between the decision problems. The models are validated through simulations and different performance metrics are analyzed such as the number of pod visits, the total distance travelled, and the system makespan. Moreover, we introduce a cost metric to facilitate the objective evaluation of the system performance. From the computational experiments we conclude that the integration of the decision problems results in significantly better performing systems compared to sequentially optimizing them. However, this comes at the cost of more computational effort. Furthermore, including interstation visits can further improve the overall system performance. [ABSTRACT FROM AUTHOR]

Technological advances have significantly fostered the use of robotics in neurosurgery. Due to their novelty, there is a need to develop training methods within neurosurgical residency programs that provide trainees the skills to utilize these systems in their future practices safely and effectively. We describe a detailed curriculum for trainees with significant responsibilities in the operating room, as well as hands-on and theoretical didactics. The curriculum for robot-assisted stereotactic electroencephalography (SEEG) and deep brain stimulation (DBS) electrode implantation technique and assessment tool has been designed based on Accreditation Council for Graduate Medical Education's (ACGME's) milestone requirement for surgical treatment of epilepsy and movement disorders. Residents were surveyed to assess their use of robotics in their surgical training. Since 2019, more than 100 patients have undergone robot-assisted SEEG and DBS depth electrode implantations at our institution. Residents and fellows were involved in all aspects of surgical planning and execution and were encouraged to take an active role during procedures. Didactic sessions led by experienced faculty are emphasized as important learning tools prior to hands-on experience in the operating room. The results of the survey show that residents receive more training intraoperatively as compared to training sessions, yet trainees would benefit from more instruction on informative cadaveric simulation sessions. Our curriculum was developed to become a structured tool for assessment of robotic education in neurosurgical training. This curriculum based on ACGME milestone requirements serve as a template for resident and fellow education in robotics in neurosurgery. [ABSTRACT FROM AUTHOR]

Background: The inframesocolic approach to the uncinate process of the pancreas has been rarely described in literature. To the best of our knowledge, no robotic cases have been reported. Methods: The case of a 74-year-old woman, with a 43-mm branch-duct intraductal papillary mucinous neoplasm with worrisome features within the uncinate process of the pancreas, is described. Results: After diagnostic work-up, due to the uncertain potential of malignancy and the strong motivation of the patient to undergo surgery, we performed a robotic enucleation through an inframesocolic approach. The neoplasm was more than 1 cm from the main pancreatic duct. Final pathological diagnosis revealed a low-grade dysplasia branch-duct intraductal papillary mucinous neoplasm. Conclusions: The inframesocolic approach could represent an easy way to access the uncinate process of the pancreas, allowing safe limited resection in selected cases such as small branch-duct IPMN or pancreatic neuroendocrine tumors. [ABSTRACT FROM AUTHOR]

To describe the robotic full-thickness rectal excision using a transanal circular stapler in rectovaginal endometriosis nodule infiltrating the rectum >3 cm. Surgical video article. The local institutional review board was consulted, and the requirement for institutional review board approval was waived because the video describes a technique and the patient cannot be identified whatsoever. Written informed consent was obtained from the patient for the use of personal data. A tertiary referral center. Patients undergoing excision of rectal endometriosis. Standardized technique of full-thickness disk excision of large rectovaginal endometriosis nodules employing a combined robotic and trans anal approach. Feasibility of the technique. The technique is designed for deep rectovaginal nodules infiltrating middle and low-rectum up to 3 to 5 cm in length. The procedure was performed with robotic assistance. The 7-degree freedom mobility of the robotic scissors allows for a deep rectal shaving, with the goal of progressive reduction of the thickness of rectal wall. The scissors follow the rectal wall tangentially and leave behind a thin rectal wall which can be bent and pushed into the trans anal stapler's jaws. We employed end-to-end, 33 mm-diameter, circular trans anal staplers to increase the area of rectal wall to be caught into the stapler. A stitch was placed on the superior and the inferior limits of the shaved area, then the shaved area was bent and pushed into the stapler by tying a suture. The general surgeon closed and fired the stapler, then the stapler was removed together with a rectal patch of 4- to 6-cm diameter. The procedure ended in the bubble test which checked the integrity of the stapled line. Supplementary stitches may be placed to reinforce the suture, if required. The preliminary rectal shaving represents the real keystone of this procedure, and our experience suggests that the robotic assistance improves its feasibility in large nodule responsible for intrarectal protrusion. [ABSTRACT FROM AUTHOR]

Background: Left hepatic trisectionectomy consists of the removal of liver segments 2, 3, 4, 5, and 8. This difficult surgical procedure may be required when the left liver and right anterior sector (segments 5 and 8) are involved. We present a video of a robotic anatomic left trisectionectomy with Glissonian approach to the left and right anterior sector pedicles. Methods: A 77-year-old man presented at a routine ultrasound with a large liver mass. Magnetic resonance imaging showed a bulky hepatocellular carcinoma occupying liver segments 2, 3, 4, 5, and 8, with a portal tumor thrombus in the right anterior sector. The patient had multiple comorbidities, including obesity, diabetes, hypertension, and coronariopathy. The multidisciplinary team decided to use immunotherapy with atezolizumab and bevacizumab. After 12 cycles, the patient showed an objective response, and left trisectionectomy was indicated. A robotic approach was proposed and consent was obtained. The Glissonian approach was used for anatomic control of the left and right anterior sector pedicles. Results: The operative time was 390 min with an estimated blood loss of 410 ml, and no transfusion was required during or after the procedure. Recovery was uneventful and the patient was discharged on postoperative day 8. No bile leak was observed, and the drain was removed on postoperative day 8. Conclusions: Robotic left trisectionectomy is safe and feasible. The Glissonian approach is useful for anatomic left liver trisectionectomy. This video can help gastrointestinal surgeons perform this complex procedure in a minimally invasive manner. [ABSTRACT FROM AUTHOR]

Background: Robotic distal gastrectomy (RDG) is performed increasingly; a sound understanding of the learning curve in robotic distal gastrectomy has taken on great importance METHODS: Data on the 67 RDG cases were collected from one surgeon with rich experience in laparoscopic gastrectomy. Cumulative sum (CUSUM) analysis based on operative time was used to identify inflexion points.Results: Thirty-five cases, on average, were needed to complete operation time-learning curve sufficiently. A reduction in surgical time was observed in the last 32 cases compared with the first 35 cases (185.3 ± 25.3 min vs. 225.1 ± 49 min, P < 0.01).Conclusions: Surgeons with extensive laparoscopic experience can master robotic distal gastrectomy techniques after some training. [ABSTRACT FROM AUTHOR]

This paper overviews and discusses the relationship between Reinforcement Learning (RL) and the recently developed Dual Control for Exploitation and Exploration (DCEE). It is argued that there are two related but quite distinctive approaches, namely, control and machine learning, in tackling intractability arising in optimal decision making/control problems. In the control approach, the original problems (of an infinite horizon) are approximated by finite horizon problems and solved online by taking advantage of the availability of computing power. In the machine learning approach, the optimal solutions are approximated through iterations, or (offline) training through trials when models are not available. When dealing with unknown environments, DCEE as a technique developed from the control approach could potentially solve similar problems as RL while offering a number of advantages, most notably, coping with uncertainty in environment/tasks, high efficiency in learning through balancing exploitation and exploration, and potential in establishing its formal properties like stability. The links between DCEE and other relevant methods like dual control, Model Predictive Control and particularly Active Inference in neuroscience are discussed. The latter provides a strong biological endorsement for DCEE. The methods and discussions are illustrated by autonomous source search using a robot. It is concluded that DCEE provides a promising, complementary approach to RL, and more research is required to develop it as a generic theory and fully realise its potential. The relationships revealed in this paper provide insights into these relevant methods and facilitate cross fertilisation between control, machine learning and neuroscience for developing autonomous control under uncertain environments. [ABSTRACT FROM AUTHOR]

We describe the first five robot-assisted radical prostatectomies (RARPs) performed with the new Hugo RAS system (Medtronic, Minneapolis, MN, USA) in Europe. The five patients underwent RARP ± lymph node dissection at OLV Hospital (Aalst, Belgium). All procedures were completed, with no need for conversion or for placement of additional ports. No intraoperative complication or technical failure of the system was recorded. The median operative time was 170 min (interquartile range [IQR]: 140–180) and the median console time was 120 min (IQR: 110–150). Median length of stay was 3 d (IQR: 2–4). System start-up and docking of the robotic arms were straightforward and rapid processes for a properly trained surgical team. Awaiting future investigations in larger series, this study proves the safety and feasibility of RARP with the Hugo RAS system and provides relevant data that may be of help to early adopters of this surgical platform. [ABSTRACT FROM AUTHOR]

Objective: To describe a novel single-port, endorobotic technique for harvesting rectal mucosa grafts (RMGs) for urethral reconstruction.Methods: A 57-year-old man with prior transurethral procedures developed recurrent obstructive voiding symptoms. Urethrography revealed a panurethral stricture, meatus to the bulbomembranous junction. It was decided to proceed with surgical repair. Owing to the stricture's length, available oral mucosa would require additional material, and repair with a single rectal mucosa graft was offered instead. Single-port (SP) endorobotic approach offered ideal access for transanal harvest. With the patient placed in a modified lithotomy position, a GELPOINT Path transanal access platform was inserted through the anal canal. Pneumorectum was established at 12 mmHg with an AirSeal CO2 insufflator. A Da Vinci SP surgical system was docked, equipped with Maryland bipolar forceps and a monopolar spatula. After injection of ORISE gel, endorobotic submucosal dissection began posteriorly proximal to the dentate line.Results: Proceeding cranially through the rectal submucosa, a 21 ✕ 3 cm strip of mucosa was obtained with appropriate hemostasis. The resulting graft was thoroughly thinned. The robot was undocked, and the patient repositioned to high lithotomy. The patient underwent a penis-inverting, dorsolateral approach augmentation urethroplasty. With an indwelling catheter placed, the patient was discharged on postoperative day 2 with no postoperative complications.Conclusion: Transanal rectal mucosal dissection with a single-port endorobotic approach can be an enticing and minimally invasive harvesting technique to provide substitution grafts for long-segment urethral reconstruction. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the oncologic outcomes of pT0 after robot-assisted radical cystectomy (RARC).Methods: A retrospective review of the International Robotic Cystectomy Consortium database was performed. Patients with pT0 after RARC were identified and analyzed. Data were reviewed for demographics and pathologic outcomes. Kaplan-Meier curves were used to depict recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS). Multivariate stepwise Cox regression models were used to identify variables associated with RFS and OS.Results: Four hundred seventy-one patients (18%) with pT0 were identified. Median age was 68 years (interquartile range (IQR) 60-73), with a median follow up of 20 months (IQR 6-47). Thirty-seven percent received neoadjuvant chemotherapy and 5% had pN+ disease. Seven percent of patients experienced disease relapse; 3% had local and 5% had distant recurrence. Most common sites of local and distant recurrences were pelvis (1%) and lungs (2%). Five-year RFS, DSS, and OS were 88%, 93%, and 79%, respectively. Age (hazards ratio [HR] 1.05, 95% confidence interval [CI] 1.01-1.09, P = 0.02), pN+ve (HR 11.48, 95% CI 4.47-29.49, P < .01), and reoperations within 30 days (HR 5.53, 95% CI 2.08-14.64, P < .01) were associated with RFS. Chronic kidney disease (HR 3.24, 95% CI 1.45-7.23, P < .01), neoadjuvant chemotherapy (HR 0.41, 95% CI 0.18-0.92, P = .03), pN+ve (HR 4.37, 95% CI 1.46-13.06, P < .01), and reoperations within 30 days (HR 2.64, 95% CI, 1.08-6.43, P = .03) were associated with OS.Conclusions: Despite pT0 status at RARC, 5% had pN+ disease and 7% of patients relapsed. Node status was the variable strongest associated with RFS and OS in pT0. [ABSTRACT FROM AUTHOR]

Robotic-assisted percutaneous coronary intervention (PCI) has emerged as an alternative to manual PCI to mitigate the risk of occupational hazards for operators, and to increase precision of device placement. Previous studies have reported the safety and efficacy of robotic-assisted PCI in simpler lesions, and recently the safety and efficacy of robotic-assisted chronic total occlusion PCI have been reported. Herein, we report two cases with three-vessel disease, including total occlusions, successfully treated robotically utilizing newer guidewire and device automation. [ABSTRACT FROM AUTHOR]

Background: Surgeons are among the most at risk of work-related musculoskeletal health decline because of the physical demands of surgery, which is also associated with cognitive fatigue. Minimally invasive surgery offers excellent benefits to patients but the impact of robotic or laparoscopic surgery on surgeon well-being is less well understood. This work examined the musculoskeletal and cognitive demands of robot-assisted versus standard laparoscopic surgery.Methods: Medline, Embase and Cochrane databases were systematically searched for 'Muscle strain' AND 'musculoskeletal fatigue' AND 'occupational diseases' OR 'cognitive fatigue' AND 'mental fatigue' OR 'standard laparoscopic surgery' AND 'robot-assisted laparoscopic surgery'. Primary outcomes measured were electromyographic (EMG) activity for musculoskeletal fatigue and questionnaires (NASA-TLX, SMEQ, or Borg CR-10) for cognitive fatigue. A systematic review was conducted in accordance with the Synthesis Without Meta-analysis (SWiM) Guidelines. The study was preregistered on Prospero ID: CRD42020184881.Results: Two hundred and ninety-eight original titles were identified. Ten studies that were all observational studies were included in the systematic review. EMG activity was consistently lower in robotic than in laparoscopic surgery in the erector spinae and flexor digitorum muscles but higher in the trapezius muscle. This was associated with significantly lower cognitive load in robotic than laparoscopic surgery in 7 of 10 studies.Conclusions: Evidence suggests a reduction in musculoskeletal demands during robotic surgery in muscles excluding the trapezius, and this is associated with most studies reporting a reduced cognitive load. Robotic surgery appears to have less negative cognitive and musculoskeletal impact on surgeons compared to laparoscopic surgery. [ABSTRACT FROM AUTHOR]

Fusing LiDAR and high definition (HD) maps is a feasible way to achieve global localization in GNSS-denied environments, which is necessary in driverless vehicle and robotic technologies. This paper proposes a single-shot global localization solution that uses only a single current scan of a rotating multiple-beam LiDAR sensor to locate its own location and pose with respect to an HD map in the form of georeferenced point clouds. This single-shot global localization solution estimates the state of the current moment without the previous moment state and thus avoids the nonconvergence problems that plague filter-based methods. The proposed solution allows HD maps from diverse LiDAR sensors to be used for global localization and is more robust than existing methods. The proposed solution consists of two procedures: offline preprocessing and online global localization. In the offline procedure, diverse HD maps are preprocessed to construct a global prior map for the localization process. The online global localization procedure includes two elements: place recognition, location and pose estimation. A novel Cross-Section Shape Context (CSSC) descriptor that is highly descriptive and rotation-invariant is proposed for subsequent processes. Two strategies, two-stage similarity estimation and Nearest Cluster Distance Ratio (NCDR), based on the CSSC descriptor are proposed to improve place recognition precision. A Selective Generalized Iterative Closest Point (SGICP) algorithm is proposed to calculate location and pose accurately using the CSSC descriptor. Comprehensive experiments were performed to evaluate this solution. A comparison of the precision-recall curve of multiple scenes, particularly under changed viewpoint scenes, shows that the CSSC descriptor is more robust than existing descriptors. Experimental analysis also confirms that the proposed strategies, two-stage similarity estimation and NCDR, improve place recognition precision. Also, compared to the generalized iterative closest point algorithm, the SGICP algorithm achieved better accuracy by 31% and efficiency by 60%. The proposed solution achieves a mean relative translation error (RTE) improvement of 27% over the OneShot algorithm on the KITTI dataset. The proposed solution had an average 77% improvement over 1σ RTE relative to the benchmark in tests with the long-term localization NCLT dataset. The mean RTE of the proposed solution was 0.13 m using HD maps from different LiDAR sensors. Our code is available at: https://github.com/Dongxu05/CSSC. [ABSTRACT FROM AUTHOR]

Stiffness tuning is crucial in developing many advanced engineering systems such as morphing aircraft, soft robotics, etc. Achieving a significant stiffness change while maintaining a high load-bearing capacity in compression is a challenging task. This study addressed this challenge by exploiting the in-plane buckling and bistability of asymmetric carbon fiber composite laminates. Due to the inverted curvatures of these laminates at their two stable states, they showed two distinct in-plane compression behaviors— stiff response where the laminate behaved like a thin curved column (with a stiffness coefficient near 68 N mm−1), and compliant response where the laminate acted like a soft non-linear spring (with stiffness near 0.7 N mm−1). As a result, a simple snap-though between these two stable states offered a significant stiffness change, with a high/low stiffness ratio of ≈ 97. By using extensive finite element simulations, experimentation, and a semi-empirical curved plate buckling model, this study examined the mechanics underpinning the laminate's 3-step buckling and the influences of asymmetric boundary contacts with friction. Additionally, three strategies to enhance the laminate's performance—adjusting the aspect ratio, applying lateral loads, and parallel stacking were examined. It also demonstrated cellular structure concepts using these bistable laminates. Remarkably, by simply adding a small lateral in-plane force (e.g., a rubber band), one could substantially increase the stiffness ratio of the composite laminates to ≈ 200 and load-bearing capacity at the stiff configuration, outperforming the state-of-art variable stiffness methods. • Buckling of Bistable Composite Laminates reveal two distinct stiffness behaviors, high/low stiffness variation of 97 : 1. • New insight: buckling of laminate in stiff state follows a 3-step process, explained through experiment and FEA. • Parametric study: three strategies for increasing stiffness variation to ≈ 200 and better load-bearing capacities. • Novel sandwich architecture with switachable stiffness cores is proposed. [ABSTRACT FROM AUTHOR]

• Developed a numerical analysis method that couples multiple physics (Magnetic-Fluid-Solid). • Investigated the locomotion modes of two exemplary magnetically propelled miniature soft bionic robots: bionic midge larvae and bionic jellyfish. • Uncovered two major driving mechanisms of flexible microrobots induced by a magnetic field: deformation and rotation. • Proposed a theoretical model of a bionic fish tail fin robot that depends solely on a rotating magnetic field for propulsion. • Presented a comparative analysis of swimming efficiency, flexibility, stability, and relative advantages of these three robots operating in liquid environments. Magnetic field-driven soft microrobots have widespread applications in biomedical science, microfluidic chips, nanoengineering, and various other domains. However, the existing methods for designing such magnetic-driven flexible robots largely rely on experimental trial and error or steady-state numerical simulated results, which fall short of meeting the intricate requirements for magnetic field editing and magnetic domain distribution. Addressing this issue, a multi-physics coupling numerical analysis method encompassing Magnetic-Fluid-Solid mechanics was developed. A complete process for transient numerical simulation resolution has been achieved. Utilizing this analysis method, the motion patterns of two typical prototypes of magnetic-driven miniature soft bionic robots, a bionic midge larvae robot, and a bionic jellyfish robot, have been analyzed. The central role of the magnetic field in driving these robotic designs—causing deformation in microrobots by designing magnetic domains and spinning the microrobots by controlling the magnetic field—has been revealed. We developed a theoretical model of a bionic fish tail fin robot driven solely by a rotating magnetic field and conducted comparative studies on the swimming efficiency, flexibility, stability, and relative advantages of the three types of robots. The impact of different magnetic domain distribution rules and different magnetic field driving methods on a robot's performance is analyzed, validating that higher swimming efficiency can be achieved by designing a robot's magnetic domains so that they undergo magnetic displacement under magnetic torque resulting in deformation. This innovative analysis method holds potential to provide valuable references for designing motion patterns of magnetically driven microrobots within liquid environments, thereby deepening our understanding toward various complex gait mechanisms involved in biological swimming. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper proposes a complex number representation method for dynamically recording robot positions and develops an optimization strategy for measuring and minimizing inter-robot distances in real-time. Based on this, the optimization strategy is transformed into finding the theoretical solution of the complex time-varying matrix equation by combining the leader–follower frameworks, and a complex zeroing neural dynamics (CZND) model is proposed for this objective. Meanwhile, the effectiveness of the proposed CZND model and the ideal trajectories of the follower robots are ensured and given through rigorous theoretical analyses. Subsequently, these theoretical findings are validated by two numerical experiments (four cases). In addition, the superiority of the CZND model for inter-robot management tasks is comparatively analyzed from three perspectives: initial positions, number of follower robots, and design parameters. Compared to conventional gradient-based neural network (CGNN) methods, the proposed CZND model, based on a dynamic error function (which is not required to be non-negative as in CGNN), demonstrates improved handling of the dynamic behavior of the system by utilizing the first-order time derivatives of matrices, resulting in lower residual errors in the implementation of inter-robot management. Specifically, for the same inter-robot management task, the steady-state error implemented by CZND is on the order of 1 0 − 4 , whereas for CGNN, it is on the order of 1 0 − 1 , indicating a significantly larger lagging error. [ABSTRACT FROM AUTHOR]

In the paper, we study a digital topological complexity of a digital map and its properties. Firstly, we discuss a strong digital homotopy which allows iterative algorithms based on our previous work. As a generalization of the topological complexity in terms of the strong digital homotopy (we call it strong digital topological complexity), we next study the strong digital f -sectional category of a strong digital fibration. Then we investigate estimates of the upper and lower bounds for the strong digital topological complexity of digital maps. We also reveal the difference between the strong digital topological complexity and the ordinary digital ones. It has shown that the strong digital topological complexity is more similar to the classical continuous case than the ordinary digital ones. Moreover, arising from practical considerations in robotics, we consider the naive digital topological complexity of digital maps. [ABSTRACT FROM AUTHOR]

[Display omitted] • CB/CNT hybrids and elastic TPU are combined to assemble resistive strain sensor. • The hierarchical conductive network endows the sensor with desirable sensing capabilities. • CB/CNT@TPU strain sensor shows great application in personalized health monitoring. • Multiple sensors are integrated for robotic arm control by capturing human motions. Flexible strain sensors are strongly demanded in fields of personal healthcare monitoring and human–machine interface, as they can accurately perceive external action to produce electrical output signals. Aiming at the existing problems, such as the balanced sensitivity and sensing range, durability as well as expanded application, this paper proposes a flexible strain sensor prepared based on electrospun thermoplastic polyurethane (TPU) membrane, followed by anchoring mixed conductive materials (carbon black (CB)/carbon nanotubes (CNT)) on its surface via an effective ultrasonic-assisted method. The resulting CB/CNT@TPU strain sensor delivers a desirable integration of sensing performances: good sensitivity, wide work range, fast response speed (<150 ms) and reliable cyclic stability (5000 cycles). Ascribed to the sensing behavior and additional breathability, strain sensor can be applied well to monitor human health, including human joints movements (fingers and wrists) and physiological signals (pulse and laryngeal vibration recognition). Finally, a multiaxial robotic arm control system is constructed, which provides a feasible strategy for intelligent robotic arms. [ABSTRACT FROM AUTHOR]

Robotic assistance can improve the learning of complex motor skills. However, the assistance designed and used up to now mainly guides motor commands for trajectory learning , not dynamics learning. The present study explored how a complex motor skill involving the right arm can be learned without suppressing task dynamics, by means of an innovative device with robotic guidance that allows a torque versus motion profile to be learned with admittance control. In addition, we assessed how concurrent visual feedback on this profile can enhance learning without creating dependency, by means of a fading procedure (i.e., feedback reduction across trials). On Day 1, a Control group performed an acquisition session (6 blocks) featuring concurrent visual feedback, while a Fading group performed the session with a gradual reduction in feedback (from 100% to 0% over the 6 blocks). On Day 2, both groups performed a block first without feedback (i.e., Transfer test), then with feedback (i.e., Retention test). Results revealed that on Day 1, movement rehearsal induced a significant improvement in spatiotemporal parameters for the Control group, compared with the Fading group. On Day 2, the opposite was found when this visual feedback was removed, as the Fading group performed significantly better than the Control group on the Transfer test. Vision allows a relationship to be established between the required torque and the motion profile. Its suppression then forces the processing of more intrinsic information, leading to the development of a stable internal representation of the task. [ABSTRACT FROM AUTHOR]

This paper proposes an unknown input observer for nonlinear systems with input decoupling via system invertibility. Starting from a suitable reformulation of the model of a generic nonlinear system, obtained by merging all system uncertainties with respect to an appropriate nominal linear model into a disturbance vector, the proposed observer can asymptotically copy both the system state and unknown inputs, even in the presence of measurement noise. Formal proof of the estimate convergence is demonstrated analytically. A comparison of the proposed method with existing solutions is shown in simulation, and the method's effectiveness in real-world scenarios is demonstrated by experimental results on a soft articulated robot. • Robust κ th derivation of output vector with respect to the high-frequency and low-amplitude measurement noise. • Unknown input decoupling based on algebraic rules with large applicability. • Linear observer for nonlinear dynamic systems. [ABSTRACT FROM AUTHOR]

Nowadays, the robotic mobile fulfillment system (RMFS) has been increasingly used by online retailers. Compared with traditional picker-to-parts warehouses, the racks of RMFS do not have to return to the same location after picking, thus we can dynamically change their locations, which brings great potential to efficiently fulfill orders. Aiming at minimizing the sum of rack travel distances, the key question is how to reassign a rack to an unoccupied storage location after picking items from the rack. However, the issue involves two challenges for unidirectional RMFS, one is that huge differences may exist between the classical Manhattan distance estimation and the actual distance for the unidirectional aisles in RMFS, and the other one is that we need to account for the frequency of rack moving for multi-item orders. We thus first propose closed-form formulas to optimally estimate the cycle travel distance for each rack. Then, by overcoming the repeated counting issue for multi-item orders, we propose a novel SKU (Stock Keeping Units)-correlation-based algorithm to choose high-velocity racks, which can better fulfill multi-item orders. Finally, embedding the cycle travel distance and SKU-correlation-based velocity, we propose a Velocity-based Rack Storage Location Assignment method (VRSLA) to solve the rack storage location assignment problem by assigning high-velocity racks to the nearest storage locations. Collaborating with a large online retailer in China, we demonstrate the performance of VRSLA by using both small-scale and large-scale datasets. The computational results show that VRSLA not only can achieve near-best solutions compared with an integer programming model solved by Gurobi, but also outperforms four state-of-the-art assignment methods in literature (random, velocity-based class, shortest path, and sale-based) by reducing the rack travel distance up to 43.32%. We also found that the stronger the correlation between SKUs on the racks or the larger the size of the RMFS, the shorter the rack travel distance by the proposed VRSLA method. • Proposes the rack storage location assignment problem for the unidirectional RMFS. • Considers both sales of SKUs and correlation between SKUs for rack velocity calculation. • Proposes a novel closed-form formula for the velocity of storage locations. • Develops an efficient approach that can match high-velocity racks with nearest locations. • Demonstrates superiority of the approach over four assignment methods using real data. [ABSTRACT FROM AUTHOR]

In recent years, there has been significant progress in developing specialized 3D printing techniques that cater to various demanding applications. However, the current state of this technology is challenged when it comes to complex in situ printing scenarios, which require a controlled printing platform. The lack of a stable printing platform is a fundamental limitation of its use in in situ applications. To address this issue, we present a novel platform-independent 3D fabrication process that enables printing on platforms with non-cooperative movement. The process overcomes the challenge of high-speed tracking, motion compensation, and real-time printing by developing a closed-loop visual feedback-controlled robotic printing process. The proposed process incorporates a marker-based visual detection and tracking controller setup, which is discussed in detail. The algorithm consists of two loops running asynchronously: a high-speed inner control loop and an outer measurement loop. This setup enables precise and accurate tracking of the printing platform, compensating for any disturbances during the printing process. Our experimental results demonstrate the successful printing of simple linear geometries, even with low-disturbing platform velocities. Moreover, the tracking controllers' ability to handle measurement occlusion is validated, showing the proposed process's robustness and effectiveness. Our work provides a significant step towards enabling 3D printing in complex in situ printing scenarios. [ABSTRACT FROM AUTHOR]

Depression and loneliness are challenges facing older residents living in long-term care facilities. Social robots might be a solution as nonpharmacologic interventions. The purpose of this study was to explore the effects of concrete forms of social robots on depression and loneliness in older residents in long-term care facilities by a systematic review and meta-analysis of randomized controlled trials. This is a systematic review and meta-analysis. Older residents in long-term care facilities. Six electronic databases of PubMed, Embase, Scopus, Web of Science, MEDLINE, and CINAHL plus were searched in August 2023. Random effect models of meta-analyses, subgroup analyses, and meta-regressions were performed for statistical analyses. After evaluation, 8 studies were selected for both qualitative and quantitative synthesis. Social robot interventions had significant positive effects on decreasing depression and loneliness with large effect sizes. Group-based robot activities had a better effect on improving depression than individual-based robot activities. Longer durations of interventions produced significantly more improvement in depression. Social robots with physical manifestation provide the opportunity for older adults' social engagement and interactions with robots and others. Social robot interventions are recommended for older residents in long-term care facilities to promote psychosocial well-being in daily care routines. [ABSTRACT FROM AUTHOR]

Robot comprehensive performance optimization is a high dimensional non-convex problem, which is of great significance in the practice of robotic intelligent manufacturing. Existing methods still face many challenges in performance evaluation, local optima, and planning efficiency. In this paper, a novel method based on accelerated gradient descent and directed-mutagenesis genetic algorithm (AGD-DGA) is proposed for efficient robotic motion planning and comprehensive performance optimization. Firstly, the time, energy consumption, and impact (T-E-I) are taken as the optimization objectives. In particular, the power loss sub-model is constructed, which is rarely considered in the previous research on motion optimization, and it is combined with the driving power sub-model to improve the evaluation accuracy. Next, a novel directed-mutagenesis genetic algorithm (DGA) is proposed to address the local optima problem. Compared with existing methods based on random sampling, the DGA further improves the success rate and efficiency by updating the probability model based on the adaptive Gaussian process. Moreover, a hierarchical weight updating mechanism is established to overcome the impact of additional performance indicators on the success rate of collision-free path planning. To improve planning efficiency, an incremental optimization mechanism and an adaptive fine-tuning strategy for population size are proposed to reduce unnecessary sampling, evaluation, and sorting. The simulation and experimental results in different scenarios demonstrate that the proposed AGD-DGA achieves better results than most of the mainstream algorithms in terms of optimization efficiency, success rate, and motion performance. In addition, it is also verified that the modified energy consumption model can achieve an average evaluation accuracy of more than 90% while maintaining the calculation efficiency. [ABSTRACT FROM AUTHOR]

• This paper introduced the functional role of robotic arms in precision agriculture. • This paper summarized the hardware and software technologies of agricultural robotic arms. • This paper presented the application of robotic arms in various agricultural environments, including greenhouses, fields, and orchards. • This paper discussed the challenges and future prospects of robotic arms in precision agriculture. In precision agriculture, robotic arms exhibit significant technical advantages, such as enhancing operational precision and efficiency, reducing labor costs, and supporting environmental sustainability. This paper provides a comprehensive overview of the application of ground-based robotic arms in precision agriculture, analyzing the hardware and software aspects and current application status across various agricultural settings, and discussing challenges and prospects in this field. First, this paper explores precision agriculture and agricultural robotic arms, highlighting their critical roles in enhancing agricultural efficiency and automation. Further, it addresses the challenges plaguing the practical applications of robotic arms and compares innovative robotic arm technologies with traditional models to establish a foundation for understanding these advancements in modern agriculture. Additionally, this paper analyses the hardware of robotic arms, including rigid and flexible manipulators, drivers, end-effectors, sensors, and controllers, emphasizing the importance of innovation and optimization for improved performance. For the software systems, this paper focused on classic workflows and advanced algorithms for perception, motion planning, and control, as these are essential for the precise and adaptable functioning of robotic arms in diverse agricultural environments. Furthermore, this paper reviews the research and application status of robotic arms across various settings, including greenhouses (e.g., ground planting, desktop planting, and vertical planting), fields (e.g., dry fields, moist, and paddy fields), and orchards (e.g., fruit tree orchards, vineyard orchards, and ground-level orchards) to demonstrate their broad applicability and efficient operational capabilities in diverse conditions. Lastly, this paper discusses the challenges and prospects of robotic arms, emphasizing the significance of integrating disciplines, such as agronomy and biomimetics, big data, artificial intelligence, digital twinning, and human–machine interaction. Advancements in these areas are pivotal for the progress of robotic arm technology and for introducing innovative, efficient solutions to precision agriculture. In summary, this review reveals the immense potential of the application of robotic arms in precision agriculture. With ongoing technological advancements, these robotic arms are expected to play an increasingly crucial role in future agricultural production, making substantial contributions to achieving more efficient, innovative, and sustainable farming practices, heralding a new era in agricultural technology. This paper will serve as a valuable guide for researchers and practitioners, offering comprehensive insights into the use of robotic arms in precision agriculture and providing essential knowledge for advancing the field. [ABSTRACT FROM AUTHOR]