Robotic Platform Research Articles

Algorithm-Driven Robotic Discovery of Polyoxometalate-Scaffolding Metal-Organic Frameworks.

The experimental exploration of the chemical space of crystalline materials, especially metal-organic frameworks (MOFs), requires multiparameter control of a large set of reactions, which is unavoidably time-consuming and labor-intensive when performed manually. To accelerate the rate of material discovery while maintaining high reproducibility, we developed a machine learning algorithm integrated with a robotic synthesis platform for closed-loop exploration of the chemical space for polyoxometalate-scaffolding metal-organic frameworks (POMOFs). The eXtreme Gradient Boosting (XGBoost) model was optimized by using updating data obtained from the uncertainty feedback experiments and a multiclass classification extension based on the POMOF classification from their chemical constitution. The digital signatures for the robotic synthesis of POMOFs were represented by the universal chemical description language (χDL) to precisely record the synthetic steps and enhance the reproducibility. Nine novel POMOFs including one with mixed ligands derived from individual ligands through the imidization reaction of POM amine derivatives with various aldehydes have been discovered with a good repeatability. In addition, chemical space maps were plotted based on the XGBoost models whose F1 scores are above 0.8. Furthermore, the electrochemical properties of the synthesized POMOFs indicate superior electron transfer compared to the molecular POMs and the direct effect of the ratio of Zn, the type of ligands used, and the topology structures in POMOFs for modulating electron transfer abilities.

DESIGN FEATURES OF THE MANIPULATOR-MOTOR FOR MULTI-PURPOSE ROBOTIC PLATFORMS

It has been established that the use of multi-purpose robotic platforms (MPRP), as one of the types of ground robotic complexes, is an important factor that allows preserving the life and health of servicemen on the battlefield. It was found that the movement of MPRP, and as a result, the performance of assigned tasks, is often limited (loss of stability and passability) as a result of difficult terrain areas, or as a result of destruction caused by enemy fire. The well-known approaches that allow to ensure the multi-functionality of individual parts of the MPRP to improve their stability and passability were analyzed and it was established that they do not realize in their design features the use of multi-functional manipulators, which, if necessary, can perform the functions of a stepping or wheel drive. A new approach is proposed, which consists in the application of multi-functional manipulators (manipulators-movers) on the MPRP, which, in the event of extreme movement conditions, can, in addition to the manipulator function (grab, transfer of objects), perform the function of a walking or wheeled mover (ensuring movement over areas of the terrain). The results of designing and modeling in the Solid Works software environment are presented. An analysis of the design features of the manipulator-mover for MPRP was carried out and it was established that the multi- functionality of the manipulator-mover is implemented by introducing new elements and connections, in particular, in the manipulator mode, with minimal mass-dimensional parameters, an increase in the available space is achieved with the possibility of endless rotational movements of the captured object in any direction, and in the driver mode, when four manipulators-drivers are located on top of the MPRP in pairs, symmetrically on both sides, the functions of stepping or wheel drivers are provided.

Current status, evolution, and future perspectives in robotic platform systems for prostate cancer treatment: a narrative review.

Robotic surgery has contributed greatly to the shift from traditional surgery to minimally invasive surgery. Urology is the major field of application of robotic surgery. Several urological procedures, especially radical prostatectomy, benefit from the use of robotic surgery. Non-systematic research of the literature was performed using "Robot-assisted radical prostatectomy" and "Robotic platforms" as keywords to understand the actual situation and the future perspectives of this technology in prostate cancer treatment. The robotic platform landscape is constantly evolving. DaVinci has always been the mainstay in this field, particularly after the advent of the new single port platforms. New platforms are emerging, providing an alternative option to the well-known DaVinci system. Since in literature, few studies compare the use of different robotic platforms, their application in urological procedures is not yet widely used, for both oncological and non-oncological procedures. Furthermore, artificial intelligence begins to play a role in this landscape and could be useful for future developments. So further studies are warranted to give a full comprehension of the whole scenario. This review aims to analyze the current state of the use of robotic platforms in urology, particularly in radical prostatectomy, and to understand the evolution.

Proficiency in bariatric surgery may shorten the learning curve for minimally-invasive D2 gastrectomy.

Evidence from Asian studies suggests that minimally-invasive gastrectomy achieves equivalent oncological but improved perioperative outcomes compared to open surgery. Oncological gastric resections are less frequent in European countries. Index procedures may play a role for the learning curve of minimally-invasive gastrectomy. The aim of our study was to evaluate if skills acquired in bariatric surgery allow a safe and oncologically adequate implementation of minimally-invasive gastrectomy in a cohort of european patients. In this single-center retrospective study, all patients who received primary bariatric surgery between January 2015 and December 2018 and minimally-invasive surgery for gastric cancer treated from June 2019 to January 2023 were evaluated. Primary endpoints were operation time, lymph node yield and lymph node fractions. Secondary endpoints included postoperative complications and oncological outcomes. Learning curves for two surgeons with 350 bariatric procedures and 44 minimally-invasive gastrectomies were analyzed. For bariatric surgery, the mean operation time decreased from initially 82 ± 27 to 45 ± 21min and 118 ± 28 to 81 ± 36min for sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB), while the complication rate remained within the international benchmark. For laparoscopic gastrectomy (n = 30), operation times decreased but then remained stable over time. Operation times for the robotic platform were longer (302 ± 60 vs. 390 ± 48min; p < 0.001) with the learning curve remaining incomplete after 14 procedures. R0 status was achieved in 95.5% of patients; the mean number of lymph nodes retrieved was 37 ± 14 with no differences between the groups. Complete mesogastric excision was more frequently achieved during the later laparoscopic cases whereas it occurred earlier for the robotic group (p = 0.004). Perioperative morbidity was comparable to the European benchmark. Textbook outcome was achieved in 54.4% of the cases. In summary, we could demonstrate a successful skill transfer from bariatric surgery to minimally-invasive laparoscopic oncological gastric surgery enabling safe and oncologically adequate minimally-invasive D2 gastrectomy in a central European patient collective.

Evaluation of the da Vinci single-port system in colorectal cancer surgery: a scoping review.

Minimally invasive surgery for the treatment of colon and rectal cancer has gained popularity due to its association with reduced postoperative pain, shorter hospital stays, and quicker recovery. The Da Vinci Single-Port (SP) System combines single-port laparoscopy with robotic assistance. This scoping review aims to evaluate the safety and short-term postoperative outcomes of utilizing the Da Vinci SP platform in colorectal cancer surgery. A scoping review was conducted adhering to the PRISMA-ScR guidelines. Data were collected from PubMed, Embase, and the Web of Science Library as of December 22, 2023. Studies were screened and selected based on predefined criteria, focusing on the application of the SP robotic system in colorectal procedures. Data extraction included demographics, surgical details, intraoperative and postoperative outcomes. A narrative summary of the results was provided due to the heterogeneity in study designs. From an initial 2312 articles, 22 studies were selected for analysis, encompassing 465 patients undergoing robotic SP colorectal surgeries. Of these, 384 (82.6%) had a cancer diagnosis. The median age was 65years, with approximately 60% being male. The median operative time was 225min, with docking times averaging 12-20min. Conversion to multi-port laparoscopy occurred in 4.2% of cases, with no conversions to open surgery. Mean intraoperative blood loss ranged from 50 to 150ml. The mean number of lymph nodes retrieved ranged from 15 to 28. A diverting ileostomy was constructed in 20.3% of patients. Median times to flatus and soft diet were 2.5 and 3days, respectively, with hospital stays ranging from 3 to 11days. Perioperative complications occurred in 15.1% of patients, including wound infections (5.1%), anastomotic leakage (3.7%), and postoperative ileus (2.8%). Negative margin status (R0 resection) was achieved in 95% of cases. The Da Vinci SP robotic platform demonstrates promising safety and effectiveness in colorectal cancer surgery. It achieves high rates of successful oncological resection, adequate lymph node retrieval, and minimal intraoperative blood loss. Postoperative outcomes indicate quicker recovery times and manageable complication rates. However, longer follow-up studies are necessary to fully assess recurrence rates and long-term survival benefits associated with this innovative surgical approach.

Does Robotic-Assisted Unicompartmental Knee Arthroplasty Improve Alignment and Outcomes?

Unicompartmental knee arthroplasty (UKA) continues to increase in popularity as an excellent option for patients with single compartment disease. Robotic-assisted UKA has emerged as an optional tool with hopes for improvement in component placement, limb alignment, and patient outcomes. Furthermore, as patients continue to educate themselves, robotic assistance will become increasingly prevalent. There are now various robotic platforms on the market, each with varying differences, and more published data is emerging on alignment and outcomes. The literature to date largely concludes that robotic-assisted UKA provides more accuracy when compared to manual UKA. Short to mid-term outcomes may be improved with robotic UKA, but definitive differences in outcomes is uncertain. Survivorship with robotic UKA is non-inferior to reported manual UKA survivorship rates, and more long-term data is needed to fully elucidate this point. Orthopaedic surgeons should weigh these potential advantages against the drawbacks including cost and operative time when making a decision about whether robotic technology is right for their practice.

LQR-based control strategy for improving human-robot companionship and natural obstacle avoidance

In the dynamic and unstructured environment of human–robot symbiosis, companion robots require natural human–robot interaction and autonomous intelligence through multimodal information fusion to achieve effective collaboration. Nevertheless, the control precision and coordination of the accompanying actions are not satisfactory in practical applications. This is primarily attributed to the difficulties in the motion coordination between the accompanying target and the mobile robot. This paper proposes a companion control strategy based on the Linear Quadratic Regulator (LQR) to enhance the coordination and precision of robot companion tasks. This method enables the robot to adapt to sudden changes in the companion target’s motion. Besides, the robot could smoothly avoid obstacles during the companion process. Firstly, a human–robot companion interaction model based on nonholonomic constraints is developed to determine the relative position and orientation between the robot and the companion target. Then, an LQR-based companion controller incorporating behavioral dynamics is introduced to simultaneously avoid obstacles and track the companion target’s direction and velocity. Finally, various simulations and real-world human–robot companion experiments are conducted to regulate the relative position, orientation, and velocity between the target object and the robot platform. Experimental results demonstrate the superiority of this approach over conventional control algorithms in terms of control distance and directional errors throughout system operation. The proposed LQR-based control strategy ensures coordinated and consistent motion with target persons in social companion scenarios.

Comparative analysis of robotic and laparoscopic techniques in hiatal hernia and crural repair: a review of current evidence and outcomes.

The purpose of this narrative review is to evaluate the implementation of robotic surgery in hiatal hernia and crural repair, based on the existing literature and to compare this approach to other established techniques. We performed a non- systematic literature search of PubMed and MEDLINE on February 25, 2024 for papers published to date focusing on the surgical repair of hiatal hernias using the robotic platform. After eliminating publications based on eligibility criteria, 13 studies were selected for analysis. Robotic surgery is increasingly utilized in hiatal hernia repair due to its enhanced ergonomics and superior visualization capabilities. Operative times vary, with some studies indicating longer durations for robotic surgery (e.g., Giovannetti et al. demonstrated median operative time of 196min for robotic compared to 145min for laparoscopic) while others report shorter times (e.g., Lang F et al. demonstrated 88min for robotic versus 102min for laparoscopic). Recurrence rates between robotic and laparoscopic repairs are comparable, with reported recurrence rates of 1.8% for robotic and 1.2% for laparoscopic approaches by Benedix et al. Robotic surgery offers potential advantages, including reduced intraoperative blood loss (e.g., Giovannetti et al. mentioned median blood loss of 20ml for robotic versus 50ml for laparoscopic). The length of hospital stay and postoperative complication rates also vary, with some studies suggesting shorter stays and fewer complications for robotic surgery as surgeons become more proficient. Soliman et al. reported a statistically significant reduction in complication rates with robotic surgery (6.3% versus 19.2%). Robotic surgery presents promising results regarding the length of hospital stay, conversion rate to open surgery and postoperative complication rates when compared to laparoscopy based on the existing literature. Despite the lack of striking differences, robotic hiatal hernia repair is a valid and evolving approach.

A Novel Robot Platform With Decoupled Stiffness Control for Endoscopic Surgery

A Novel Robot Platform With Decoupled Stiffness Control for Endoscopic Surgery

A multi-docking strategy for robotic LAR and deep pelvic surgery with the Hugo RAS system: experience from a tertiary referral center.

In June 2023, our institution adopted the Medtronic Hugo RAS system for colorectal procedures. This system's independent robotic arms enable personalized docking configurations. This study presents our refined multi-docking strategy for robotic low anterior resection (LAR) and deep pelvic procedures, designed to maximize the Hugo RAS system's potential in rectal surgery, and evaluates the associated learning curve. This retrospective analysis included 31 robotic LAR procedures performed with the Hugo RAS system using our novel multi-docking strategy. Docking times were the primary outcome. The Mann-Kendall test, Spearman's correlation, and cumulative sum (CUSUM) analysis were used to assess the learning curve and efficiency gains associated with the strategy. Docking times showed a significant negative trend (p < 0.01), indicating improved efficiency with experience. CUSUM analysis confirmed a distinct learning curve, with proficiency achieved around the 15th procedure. The median docking time was 6min, comparable to other robotic platforms after proficiency. This study demonstrates the feasibility and effectiveness of a multi-docking strategy in robotic LAR using the Hugo RAS system. Our personalized approach, capitalizing on the system's unique features, resulted in efficient docking times and streamlined surgical workflow. This approach may be particularly beneficial for surgeons transitioning from laparoscopic to robotic surgery, facilitating a smoother adoption of the new technology. Further research is needed to validate the generalizability of these findings across different surgical settings and experience levels.

Quantitative evaluation of pyramid belt wear using light-reflection characteristic of agglomerate coating and image processing

As a new generation of coated abrasive products with a unique pyramid-shaped structure, pyramid belt offers finer and more uniform grinding effects, and has been increasingly applied in precision grinding. However, belt wear occurs inevitably, challenging the control of shape accuracy and surface quality of ground components. Current research on pyramid belt wear is relatively limited, still lacking a universal, rapid method for observing and evaluating belt wear. In response, this paper proposes a quantitative evaluation method for pyramid belt wear using light-reflection characteristic of agglomerate coating and image processing. Firstly, a detailed analysis of the wear types and characteristics of pyramid belt is conducted. Then, focusing on the agglomerate flat wear, a method utilizing the light-reflection characteristic of agglomerate coating for observing belt wear morphology is proposed, enabling clear image capture of belt wear contours. Based on this, a wear coefficient based on the geometric characteristics of agglomerate is defined, and a quantitative evaluation method for belt wear using image processing is further proposed. The wear experiment of the belt is conducted on a robotic belt grinding platform, and experimental results show that the evaluation deviation of the proposed method from the evaluation results based on optical profilometer is within 5% at a 95% confidence interval, demonstrating the effectiveness of this method. Additionally, this method has advantages such as high computational efficiency, low cost, and good usability. Finally, the temporal evolution processes and spatial distribution patterns of belt wear and material removal rate are investigated and analyzed.

3D Imaging Reconstruction and Laparoscopic Robotic Surgery Approach to Disseminated Peritoneal Leiomyomatosis

ObjectivesThis video article explores the synergistic approach of 3D imaging reconstruction and laparoscopic robotic surgery for the management of a complex case of disseminated peritoneal leiomyomatosis (1). The primary focus lies in the capability of the reconstruction model to provide diagnostic support to identify fibroids during surgical procedures, potentially enhancing surgical precision, reducing operating times, minimizing uterine incisions, and limiting blood loss. 3D imaging reconstruction techniques were used to facilitate the identification of multiple parasitic and non-serosal myomas, which is particularly challenging when operating with a robotic surgical platform that lacks haptic feedback. SettingTertiary referral center. ParticipantsA case report design was employed, focusing on a 43-year-old nulliparous infertile woman with multiple symptomatic uterine myomas. Our institution has made a further diagnosis of disseminated peritoneal leiomyomatosis(4-5). InterventionsDue to the widespread nature of peritoneal leiomyomatosis and numerous uterine fibroids, robotic surgery was considered a preferable option based on our experience to operate within confined anatomical spaces. 3D imaging reconstruction technology was utilized for preoperative and intraoperative planning, enabling precise determination of the fibroids' location, size, and volume obtained through MRI imaging. Real-time 3D imaging guided rapid myoma localization and surgical strategy adjustment (2-3). The procedure resulted in the removal of 15 fibroids, with minimal blood loss (250 mL) and a total operative time of 120 minutes. Multilayer running hysterorraphy was performed using a barbed monofilament suture to ensure effective hemostasis, incorporating serosal introflection to reduce the risk of post-operative adhesion development. ConclusionsThe combined approach of 3D imaging reconstruction and laparoscopic robotic surgery holds significant potential for the management of disseminated peritoneal leiomyomatosis. This approach can overcome some robotic surgery limitations, particularly the absence of haptic feedback, providing accurate preoperative planning and real-time intraoperative guidance, facilitating efficient fibroid localization, minimizing uterine incisions, and reducing blood loss. Further research is needed to fully evaluate the clinical impact of this promising technology.

Learning curve in robotic liver surgery: easily achievable, evolving from laparoscopic background and team-based

PurposeLimited and heterogeneous literature data necessitate a focused examination of the learning curve in robotic liver resections. This study aims to assess the learning curve of two surgeons from the same team with differing laparoscopic backgrounds. MethodSince February 2021, San Raffaele Hospital in Milan has implemented a robotic liver surgery program, performing 250 resections by three trained console surgeons. Using cumulative sum (CUSUM) analysis, the learning curve was evaluated for a Pioneer Surgeon (PS) with around 1200 laparoscopic cases and a New Generation Surgeon (NGS) with approximately 100 laparoscopic cases. Cases were stratified by complexity (38 low, 74 intermediate, 85 high). ResultsBoth PS and NGS demonstrated a learning curve for operative time after 15 low-complexity and 10 intermediate-complexity cases, with high-complexity learning curves apparent after 10 cases for PS and 18 cases for NGS. Conversion rates remained unaffected, and neither surgeon experienced increased blood loss or postoperative complications. A “team learning curve” effect in terms of operative time emerged after 12 cases, suggesting the importance of a cohesive surgical team. DiscussionThe robotic platform facilitated a relatively brief learning curve for low and intermediate complexity cases, irrespective of laparoscopic background, underscoring the benefits of team collaboration.

Feasibility, clinical outcomes, and learning curves of robotic-assisted colorectal cancer surgery in a high-volume district general hospital: a cohort study

Introduction: Robotic-assisted surgery (RAS) is one of the most influential surgical advances with widespread clinical and health-economic benefits. West Hertfordshire Teaching Hospital NHS Trust was the first in the UK to simultaneously integrate two CMR Surgical Versius robots. This study aims to investigate clinical outcomes of RAS, explore surgeon learning curves and assess the feasibility of implementation within a district general hospital (DGH). Methods: A prospective cohort study of 100 consecutive patient data were collected between July 2022 and August 2023, including demographics, operative and clinical variables, and compared with laparoscopic surgery (LS) data from the National Bowel Cancer Audit. Surgeon learning curves were analysed using sequential surgical and console times. Results: In the RAS cohort, the median age was 70 (IQR 57–78 years) and 60% were male. Retrieval of a minimum of 12 lymph nodes significantly increased in RAS compared to LS (95% vs. 88%, P=0.05). The negative mesorectal margin rate was similar between RAS and LS (97% vs. 91%, P=0.10), as well as length of stay greater than 5 days (42% vs. 39%, P=0.27). For anterior resections performed by the highest volume surgeon (n=16), surgical time was reduced over 1 year by 35% (304.9–196.9 min), whilst console time increased by 111% (63.0–132.8 min). Conclusions: Key quality performance indicators were either unchanged or improved with RAS. There is potential for improved theatre utilisation and cost-savings with increased RAS. This study demonstrates the feasibility and easy integration of robotic platforms into DGHs, offering wider training opportunities for the next generation of surgeons.

Achievement of international benchmark outcomes for robotic pancreaticoduodenectomy in a low volume country

BackgroundRecently, there has been an increase in the utilisation of the robotic platform to perform minimally invasive pancreaticoduodenectomy in high volume centres, with the goal of reducing morbidity and improving patient outcomes. This study reports the successful implementation of a robotic pancreaticoduodenectomy (RPD) programme in the relatively low volume setting of Australia, measured against established, internationally accepted benchmarks for low-risk open pancreaticoduodenectomy (OPD). MethodsRetrospective review of a prospectively maintained database for consecutive RPD at two Brisbane hospitals was performed, comparing data to internationally established benchmarks for low-risk OPD. A structured RPD programme was implemented by two surgeons across a study period spanning May 2017 to December 2023. ResultsOver the study period, seventy-two consecutive RPDs were performed, with 79 % for malignancy. Perioperative outcomes for transfusions, conversion rate, postoperative fistula rate, morbidity, mortality and oncological outcomes were all within established benchmark cutoffs for low-risk open pancreaticoduodenectomy (OPD), although operative time exceeded the benchmark value by 0.7hrs. ConclusionA carefully implemented RPD programme in the low volume Australian setting is feasible, with high quality outcomes achievable when compared to established benchmarks for low-risk OPD and to reported RPD series published by high volume pioneering centres.

AUTONOMOUS MONITORING ROBOT SYSTEM THAT MEASURES AIR POLLUTION

Air pollution poses a significant threat to human health, ecosystems, and climate stability, necessitating effective monitoring and control measures. Traditional air quality monitoring stations, while accurate, are static, expensive, and limited in coverage. The Autonomous Monitoring Robot System (AMRS) provides a dynamic solution, offering real-time air quality monitoring through mobile robotic platforms. Equipped with advanced sensors, these robots can measure various pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and volatile organic compounds (VOCs), across large and complex areas. By employing AI-based navigation and mapping technologies, AMRS can autonomously traverse urban and industrial environments, collecting high-resolution pollution data and creating real-time air quality maps. This approach allows for better spatial coverage, cost-efficiency, and access to hard-to-reach locations compared to traditional methods. The system can be deployed in diverse use cases, including urban air quality monitoring, industrial pollution control, and disaster management. While challenges such as battery life, sensor calibration, and data processing remain, AMRS represents a promising technological advancement in environmental monitoring and pollution control.

A multi-stage approach for desired part grasping under complex backgrounds in human-robot collaborative assembly

Human-robot collaborative assembly can leverage the unique capabilities of humans and robots to provide a flexible and efficient way for complex tasks. In this context, robots are expected to be endowed with the perception ability to collaborate with humans. For flexible processes, robotic grasping for a desired assembly part from variable multi-parts is essential but remains challenging, especially in situations of complex backgrounds including disordered placement, occlusion, similarity, and large-scale differences in size. To improve the perception and decision-making abilities of robots in collaborative assembly, firstly, a multi-stage approach integrating 2D part recognition and 6D pose estimation with perspective-focusing is proposed for desired part grasping under complex backgrounds. Secondly, to efficiently and accurately recognize a desired part from variable multi-parts under complex backgrounds, based on RGB data, an enhanced detector is adopted, which lays the foundation for the subsequent pose estimation. Thirdly, based on point cloud data, a pose estimation method based on perspective-focusing is proposed to enhance the success rate and efficiency of pose acquisition. Additionally, a modular autonomous robotic grasping system is designed. Finally, taking decelerator assembly as a case, the proposed approaches are comprehensively validated and compared on a real collaborative robot platform. Under complex backgrounds with occlusion, our approach shows stable performance with an average grasping success rate of 90.0 % and an average cycle time of 6.4175 s. Our work is expected to improve robots’ perception and decision-making capabilities while further reducing the cycle time of collaborative assembly tasks.

Adoption of the Robotic Platform across Thoracic Surgeries.

With the paradigm shift in minimally invasive surgery from the video-assisted thoracoscopic platform to the robotic platform, thoracic surgeons are applying the new technology through various commonly practiced thoracic surgeries, striving to improve patient outcomes and reduce morbidity and mortality. This review will discuss the updates in lung resections, lung transplantation, mediastinal surgeries with a focus on thymic resection, rib resection, tracheal resection, tracheobronchoplasty, diaphragm plication, esophagectomy, and paraesophageal hernia repair. The transition from open surgery to video-assisted thoracoscopic surgery (VATS) to now robotic video-assisted thoracic surgery (RVATS) allows complex surgeries to be completed through smaller and smaller incisions with better visualization through high-definition images and finer mobilization, accomplishing what might be unresectable before, permitting shorter hospital stay, minimizing healing time, and encompassing broader surgical candidacy. Moreover, better patient outcomes are not only achieved through what the lead surgeon could carry out during surgeries but also through the training of the next generation via accessible live video feedback and recordings. Though larger volume randomized controlled studies are pending to compare the outcomes of VATS to RVATS surgeries, published studies show non-inferiority data from RVATS performances. With progressive enhancement, such as overcoming the lack of haptic feedback, and future incorporation of artificial intelligence (AI), the robotic platform will likely be a cost-effective route once surgeons overcome the initial learning curve.

Human-to-Robot Handover Based on Reinforcement Learning.

This study explores manipulator control using reinforcement learning, specifically targeting anthropomorphic gripper-equipped robots, with the objective of enhancing the robots' ability to safely exchange diverse objects with humans during human-robot interactions (HRIs). The study integrates an adaptive HRI hand for versatile grasping and incorporates image recognition for efficient object identification and precise coordinate estimation. A tailored reinforcement-learning environment enables the robot to dynamically adapt to diverse scenarios. The effectiveness of this approach is validated through simulations and real-world applications. The HRI hand's adaptability ensures seamless interactions, while image recognition enhances cognitive capabilities. The reinforcement-learning framework enables the robot to learn and refine skills, demonstrated through successful navigation and manipulation in various scenarios. The transition from simulations to real-world applications affirms the practicality of the proposed system, showcasing its robustness and potential for integration into practical robotic platforms. This study contributes to advancing intelligent and adaptable robotic systems for safe and dynamic HRIs.

Research on Indoor Automatic Path Planning Algorithm for Robots

The field of robotics has significantly advanced, especially in indoor autonomous navigation, with applications in households, offices, factories, and tourism. This research focuses on developing and optimizing algorithms for indoor autonomous path planning, aiming to enhance robots' ability to navigate efficiently and safely in various indoor environments. The research adopts a comprehensive methodological approach, beginning with a literature review to understand current state-of-the-art techniques in path planning and obstacle avoidance. Novel algorithms were designed and implemented, focusing on efficiency and real-time performance. These algorithms were tested in simulation environments and validated on actual robotic platforms. Performance metrics such as path efficiency, computational time, and obstacle avoidance success rates were used to assess the algorithms. The study evaluated global path planning algorithms like A* and Dijkstra’s, and local path planning algorithms such as Rapidly-exploring Random Trees (RRT) and Dynamic Window Approach (DWA). A* and Dijkstra's algorithms proved effective for global planning but required optimization for real-time applications. RRT excelled in complex environments but needed improvements for path optimality. DWA provided robust real-time obstacle avoidance. Hybrid approaches combining these algorithms showed enhanced performance, balancing global and local planning strengths. Machine learning techniques further improved adaptability and efficiency. The integration of advanced sensor technologies and accurate map construction methods is crucial for effective indoor navigation. LIDAR, ultrasonic sensors, and depth cameras were key in providing precise environmental perception. Hybrid maps combining grid and topological approaches offered detailed local information and efficient long-distance planning. Optimization techniques like parameter tuning and algorithm fusion, along with machine learning, significantly enhanced algorithm performance. Future research should explore intelligent algorithms, multi-robot collaboration, and human-robot interaction to further advance the field.