Robot Programming Research Articles

Preparation of Personalized Medicines through Collaborative Robots: A Hybrid Approach to the End-User Development of Robot Programs

Galenic formulations are personalized medicines prepared by pharmacists in their laboratories. They are produced in small batches considering single patients’ characteristics, such as age, gender, allergies, and the like, thus contributing to responsible health. The production process is performed manually with the support of mechanic machines. This activity is time-consuming, prone to errors, and subject to quality variations. In this paper, we propose the integration of collaborative robots into the galenic formulation process to obtain several advantages, such as increased productivity, reduced variability, improved accuracy, and minimized risks associated with human error. Additionally, the use of robots can alleviate the physical burden on human operators, allowing them to focus on higher-level tasks that require critical thinking and decision-making. To achieve this goal, a software application, called PRAISE (Pharmaceutical Robotic and AI System for End users), has been developed; it is meant to support end users (i.e., pharmacists) in defining robot programs suitable to the case at hand. This application is conceived as an End-User Development (EUD) environment, which implements a hybrid interaction approach based on a natural language interface leveraging Large Language Models and a graphical interface to check and possibly revise the user-created robot programs. A user study carried out with 9 pharmacists demonstrates the validity of the approach.

Toward the Automation of the 3D Robotic Coreless Filament Winding Process for High‐Performance Composite Materials With Multiple Reinforcement Levels

ABSTRACTRobotic coreless filament winding (CFW) of high‐performance materials in 3D geometries presents a promising avenue for advancing lightweight and civil engineering. However, the unique challenges posed by CFW necessitate the development of novel path planning algorithms. Traditional slicing techniques, commonly used in regular 3D printing, are inadequate due to the complexities of filament winding processes and the utilization of materials with exceptional mechanical properties. In this article, we propose an innovative approach to automate 3D robotic CFW. The key focus of our work lies in overcoming the limitations of conventional algorithms and addressing the specific boundary conditions associated with diverse applications. Our method builds upon Hierholzer's algorithm that is then expanded to accommodate the intricate constraints of CFW. We achieve a comprehensive path planning framework capable of navigating complex 3D geometries while optimizing the utilization of high‐performance materials. This approach allows efficient and precise CFW, preserving the excellent mechanical properties of the materials. Furthermore, the generated path is automatically converted into a robot program. The procedure to automatically convert a designed part to an executable robot program can be used in various sectors, including aerospace, automotive, and construction industry. This facilitates the utilization of high‐performance fiber composites in lightweight engineering applications in the future.

BHSeP15 Setting up a Successful Robotic Abdominal Wall Service at Golden Jubilee National Hospital, Glasgow; A Tertiary Referral Centre for complex hernia patients

Abstract Background The establishment of a successful Robotic Abdominal Wall Service (AWS) at Golden Jubilee National Hospital (GJNH) in Glasgow, underscores the necessity of a dedicated abdominal wall service for superior patient outcomes. The current landscape of hernia surgery is characterised by inconsistent surgical practices, long wait times, and variable care delivery. Robotic-assisted hernia surgery, well-established across Europe, has demonstrated significant advantages, including reduced blood loss, fewer wound complications, shorter hospital stays, and decreased post-operative pain. However, setting up a robotic programme presents challenges, such as the need for surgeon training, competency assessments and training for theatre teams. These elements are critical and time-consuming. Methods Interviews were conducted with the team that established the robotic programme, and a retrospective review of a prospectively maintained database of all patients undergoing robotic hernia repair was performed. Results The first surgeon was proctored on 30th of October 2023. To date, 67 robotic-assisted hernia surgeries have been performed; including 32 bilateral inguinal hernias, 15 unilateral inguinal hernias, 11 ventral TAPPs, and 9 TARUPs. Conclusions The creation of a dedicated AWR team has proven highly beneficial, with regular theatre lists exclusively for hernia cases. The hernia RAS programme has fostered strong industry links and enhanced collaboration between different Health Boards and surgeons. Collaboration, team work and compassion help achieve favourable outcomes and we, as a dedicated AWR team, are looking forward to publishing our results & developing the service further to become a beacon of excellence in Scotland.

Task-oriented robotic rehabilitation for back mobility and functioning in a post-intensive care unit obese patient: A case report

Background Intensive care unit (ICU) acquired weakness is a detrimental condition characterized by muscle weakness, difficulty in weaning from mechanical ventilation, impaired mobility, and functional limitations, severely affecting overall quality of life. Obese patients face additional challenges due to obesity-related factors that exacerbate the negative effects of immobilization. Rehabilitation interventions have emerged as a crucial component of post-ICU care, but the rehabilitation management of obese patients remains challenging. Objective to present the impact of implementing Walker View 3.0 SCX technology in post-intensive care unit rehabilitation of obese patient. Methods A 69-year-old Caucasian man with a BMI of 44.8 kg/m2 was referred to the Cardiopulmonary Rehabilitation Unit at Alessandria Hospital, Italy, following an ICU admission for pneumonia. After a comprehensive multidisciplinary evaluation, the patient underwent an intensive rehabilitation program including physical exercises and a personalized dietary plan. A task-oriented robotic rehabilitation was added, utilizing the Walker View 3.0 SCX, for 30 min/day, 5 days/week. The robotic rehabilitation program focused on sit-to-stand mobility with weight support initially and progressed to a weight-supported robotic treadmill. Results The patient showed clear improvements in physical function, muscle strength, and independence in activity of daily living (Barthel Index improved from 15 to 70, De Morton Mobility Index improved from 8 to 39, Medical Research Council Strength improved from 17 to 40, Functional Ambulation Classification score improved from 0 to 3, Handgrip Strength Test improved from 8.8 kg to 39.4 kg). Managed by a single physiotherapist, this approach showed positive results in enhancing functional outcomes, with potential benefits in reducing operator time and assistance costs. Conclusions Integrating task-oriented robotic rehabilitation with Walker View 3.0 SCX showed promising outcomes for obese patients post-ICU. Personalized interventions with weight-bearing capabilities and real-time feedback optimized post-ICU care, enhancing functional outcomes, and potentially reducing operator time and assistance costs. Further research with larger samples is needed to validate the broader applicability and cost-effectiveness of robotic rehabilitation technologies in obese patients with ICU-acquired weakness.

Advancing Robotics-Enabled STEM Education in K-12: Frameworks for Comprehensive Learning Outcomes Assessment and Inclusivity

This systematic literature review examines the integration of robotics in K-12 STEM education, focusing on assessment frameworks and inclusive teaching practices. Through analysis of empirical studies from 2006 to 2021, we identify key themes in educational robotics implementation, including technical skill development, cognitive growth, and social-emotional learning. The review synthesizes findings from 15 selected papers, highlighting the effectiveness of constructivist and constructionist approaches in robotics education. Particular attention is given to assessment methodologies that promote inclusive learning environments and support diverse student populations. The findings reveal that successful robotics integration requires comprehensive assessment frameworks that balance technical competency with broader educational outcomes. We present structured frameworks for evaluating student engagement, learning outcomes, and program effectiveness, while emphasizing the importance of adaptable teaching strategies that accommodate different learning styles and abilities. This review contributes to the growing body of knowledge on educational robotics by providing evidence-based recommendations for implementing inclusive, assessment-driven robotics programs in K-12 education. Keywords Educational robotics, Assessment frameworks, Systematic review, K-12 STEM education, Inclusive education, Learning outcomes, Constructivist learning, Student engagement, Robotics integration, Educational technology

Comment on “Implementation of Robotic Coronary Surgery after Established Mitral Robotic Program”

Comment on “Implementation of Robotic Coronary Surgery after Established Mitral Robotic Program”

Robotic Surgery from a Gynaecological Oncology Perspective: A Global Gynaecological Oncology Surgical Outcomes Collaborative Led Study (GO SOAR3).

For healthcare institutions developing a robotic programme, delivering value for patients, clinicians, and payers is key. However, the impact on the surgeon, training pathways, and logistics are often overlooked. We conducted a study on the impact of robotic surgery on surgeons, access to robotic surgical training, and factors associated with developing a successful robotic programme. In our international mixed-methods study, a customised web-based survey was circulated to gynaecological oncologists. The Wilcoxon rank-sum test and Fisher's exact test, tested the hypothesis of the differences in continuous and categorical variables. Multiple linear regression was used to model the effect of variables on outcomes adjusting for gender, age, and postgraduate experience. Outcomes included situational awareness, surgeon fatigue/stress, and the surgical learning curve. Qualitative data were collected via in-depth semi-structured interviews using an inductive theoretical framework to explore access to surgical training and logistical considerations in the development of a successful robotic programme. In total, 94%, 45%, and 48% of survey respondents (n = 152) stated that robotic surgery was less physically tiring/mentally tiring/stressful in comparison to laparoscopic surgery. Our data suggest gender differences in the robotics learning curve with men six times more likely to state robotic surgery had negatively impacted their situational awareness in the operating theatre (OR = 6.35, p ≤ 0.001) and 2.5 times more likely to state it had negatively impacted their surgical ability due to lack of haptic feedback in comparison to women (OR = 2.62, p = 0.046). Women were more risk-averse in case selection, but there were no self-reported differences in the intra-operative complication rates between male and female surgeons (OR = 1, p = 0.1). In total, 22/25 robotically trained surgeons interviewed did not follow a structured curriculum of learning. Low and middle income country centres had less access to robotic surgery. The success of robotic programmes was measured by the number of cases performed per annum, with 74% of survey respondents stating that introducing robotics increased the proportion of surgeries performed by minimal access surgery. There was a distinct lack of knowledge on the environmental impact of robotic surgery. Whilst robotic surgery is considered a landmark innovation in surgery, it must be responsibly implemented through effective training and waste minimisation, which must be a key metric in measuring the success of robotic programmes.

EzSkiROS: enhancing robot skill composition with embedded DSL for early error detection.

When developing general-purpose robot software components, we often lack complete knowledge of the specific contexts in which they will be executed. This limits our ability to make predictions, including our ability to detect program bugs statically. Since running a robot is an expensive task, finding errors at runtime can prolong the debugging loop or even cause safety hazards. This paper proposes an approach to help developers catch these errors as soon as we have some context (typically at pre-launch time) with minimal additional efforts. We use embedded domain-specific language (DSL) techniques to enforce early checks. We describe design patterns suitable for robot programming and show how to use these design patterns for DSL embedding in Python, using two case studies on an open-source robot skill platform SkiROS2, designed for the composition of robot skills. These two case studies help us understand how to use DSL embedding on two abstraction levels: the high-level skill description that focuses on what the robot can do and under what circumstances and the lower-level decision-making and execution flow of tasks. Using our DSL EzSkiROS, we show how our design patterns enable robotics software platforms to detect bugs in the high-level contracts between the robot's capabilities and the robot's understanding of the world. We also apply the same techniques to detect bugs in the lower-level implementation code, such as writing behavior trees (BTs), to control the robot's behavior based on its capabilities. We perform consistency checks during the code deployment phase, significantly earlier than the typical runtime checks. This enhances the overall safety by identifying potential issues with the skill execution before they can impact robot behavior. An initial study with SkiROS2 developers shows that our DSL-based approach is useful for finding bugs early and thus improving the maintainability of the code.

Research on Artificial Intelligence Neural Model Based on Human Neuroscience Simulation: A Case Study of Orthopedic Medical Robots and Economic Discussion

Simulation computer models of human neuroscience are widely used in artificial intelligence. The extensive use of surgical robots in China makes the simulation model of neuroscience have a broader stage in economic development. We have more usage for medical image recognition and surgical robot programming. We try to analyze the neural network model commonly used by orthopedic medical robots from the simulation of human neuroscience. The discussion is based on economic principles.

Building a Pediatric Robotic Hepatobiliary Surgery Program at Columbia University Hospital

Building a Pediatric Robotic Hepatobiliary Surgery Program at Columbia University Hospital

Robotics and artificial intelligence

The general objective of the research was to determine the advances related to the robotics and artificial intelligence. The specific objectives of the research are to determine the countries and organizations that successfully manufacture robots, identify the characteristics of the most representative robots and the software used in their design and operation. Methodology, in this research, 61 documents have been selected, carried out in the period 2018 - 2024; including: scientific articles, review articles and information from websites of recognized organizations. Results, the software used in the design and operation of the robots are: Robot operating systems (ros), industrial robot programming software, simulation software, robot vision software, autonomous navigation software, collaborative robot (cobot) software. Conclusions, artificial intelligence has been evolving and has managed to position itself as an important technology to improve various systems. The leading robotics producers are in Germany, Japan, and Switzerland. The organizations that successfully manufacture robots are ABB Group (Switzerland), Adept Technology Inc (United States) and Apex Automation and Robotics (Australia). And the software used in the design and operation of the robots are: Robot operating systems (ros), industrial robot programming software, simulation software, robot vision software, autonomous navigation software, collaborative robot (cobot) software.

Revolutionizing Manufacturing: The Role of Robotics in the 21<sup>st</sup> Century

The Industrial robotics sector is experiencing rapid growth driven by the escalating need for automation across various industries. This review systematically analyses the prevailing trends, challenges, and future directions of robotic technologies within the manufacturing sector. This review article delves into the prevailing trends, inherent challenges, and future directions of robotic technologies in the manufacturing sector. The study employed a comprehensive literature review and case analysis, focusing on key robotic methodologies such as collaborative robots (cobots), digital twin (DT) technology, and machine vision systems integrated with artificial intelligence (AI) for enhanced performance optimisation. Through the methodological approach of synthesising case studies and empirical findings from leading enterprises such as Amazon and DHL, this review leverages logistics robots, including autonomous pick-and-place systems, to enhance operational efficiency in their warehouse. Additionally, the integration of machine vision systems powered by convolutional neural networks (CNN) enables precise object recognition and positioning, fostering a collaborative human-robot interaction framework. Key findings indicate that cobots are increasingly pivotal in augmenting industrial efficiency while maintaining safety in human-robot collaboration. Furthermore, Digital Twin technology has proven instrumental in simulating real-time manufacturing environments, optimising assembly line performance and predictive maintenance strategies. However, several challenges persist, including the difficulty of programming robots to handle complex objects and the complexities of balancing human and robot activities in synchronised tasks. Despite the advantages of these robotic systems, significant challenges remain. A significant hurdle lies in programming robots to accurately identify and manipulate complex objects, often necessitating iterative trial-and-error methods. Besides that, balancing the assembly line and effectively scheduling the robotics and human activities is important. Additionally, industrial robots face technological limitations, including the complexity of multitasking, alongside social and ethical concerns. The review concludes that while industrial robots have revolutionised many aspects of manufacturing, ongoing research is required to overcome technological limitations, such as multitasking capabilities and ethical concerns related to human labour displacement. Anticipated advancements in robotics and AI are expected to enhance manufacturing efficiency further and expand robotic applications beyond traditional industrial settings in the coming years.

Safe Implementation of Robotic Surgery for Gynecologic Diseases at a Tertiary Center: Retrospective Analysis of 149 Cases and Review of the Literature

Abstract Objectives: The initial learning curve is a barrier to introducing robotic surgery. Evidence regarding appropriate simulation programs that allow for a smooth introduction of gynecological robotic surgery remains limited. Materials and Methods: We retrospectively analyzed 149 patients who underwent robotic surgery for gynecologic diseases. Before their first procedure, the surgeons completed a robotic surgery training program. Assistant surgeons also completed simulation programs, including setup procedures and manipulation of the robotic arm. Results: The mean (± standard deviation) operative, setup, and console times were 170 ± 54 min, 22 ± 8 min, and 126 ± 51 min, respectively. No patient required blood transfusion or conversion to laparoscopy or laparotomy. Patients undergoing surgery by the same surgeon were divided into three groups (first-third, middle-third, and last-third of patients undergoing surgery) to assess chronological changes. No statistically significant differences were found between the operative and console times among these groups. The setup times for the middle and last third of patients were 20 ± 7 min and 18 ± 7 min, respectively, which were statistically significantly shorter than those for the first third of patients. No significant differences in the operative and console times done by five physicians who completed programs were observed between the first 75 and the latter 74 procedures; however, the setup times of the latter 74 procedures were significantly shorter than those of the first 74 procedures (25 ± 9 min vs. 19 ± 6 min; P < 0.001). Conclusion: The setup time was influenced by clinical experience. An appropriate simulation program allowed a safe implementation of robotic surgery.

Major Design Capstone Projects: A Multidisciplinary Teaching and Learning Approach

Before 2020, Major Design Capstone Project (MDCP) courses in Mechanical Engineering and in Electrical Engineering and Computer Engineering departments involved teams of students engaged in capstone projects, which often required expertise beyond the scope of their disciplinary training. With the new robotic program, the purpose of the new multidisciplinary MDCP courses is to develop a unified framework that enables mechanical, electrical, computer and robotic engineering students to effectively work together by combining the disciplinary expertise required to achieve their projects. The multidisciplinary MDCP framework involves an open call for project ideas, multidisciplinary assessments by groups of students, three courses over the last three semesters of their undergraduate curricula, multidisciplinary team teaching, and skill-based evaluations. The resulting capstone projects improve in quality, efficiency, and complexity. Having the opportunity to combine their expertise with other engineering fields creates a real teamwork learning experience that enhances students preparation for their future professional practice.

Reliability of the Italian Version of the Fugl-Meyer Upper Extremity Scale Administered Remotely.

Background/Objectives: Despite the increase in home-based rehabilitation, outcome measures for telerehabilitation are still underdeveloped. The Fugl-Meyer Assessment (FMA) is one of the most widely used tools for evaluating post-stroke motor deficits, with the upper extremity component (FMA-UE) recommended for assessing motor deficits of the arm. This study aims to examine the intrarater and interrater reliability of the Italian version of the FMA-UE, administered remotely via video conferencing during a robotic telerehabilitation program. Methods: Twenty stroke patients participated and underwent 20 sessions of remote upper limb rehabilitation with a robotic device. In-person evaluations were conducted before (T0) and after (T1) treatment, with additional remote assessments throughout. The study evaluated both intrarater and interrater reliability using Intraclass Correlation Coefficients (ICC) and Bland-Altman plots, classifying reliability as excellent for scores above 0.90. Results: Bland-Altman analysis showed no systematic variance for both intrarater and interrater reliability of the FMA-UE scale. Excellent reliability was found with intrarater ICC = 0.972 and interrater ICC = 0.981. Sections A and C of the FMA-UE showed excellent intrarater reliability, while sections B and D had satisfactory results. Both intrarater and interrater reliability analysis of the total score of the FMA-UE scale also showed strong agreement with Cohen's Kappa values above 0.70. Conclusions: The findings suggest that the remote administration of the FMA-UE scale is a reliable tool for assessing upper limb motor function in stroke patients, supporting its use in telerehabilitation settings.

Developing Talented Children’s Computational Thinking Through Multimodal Literacies in Pop-Up Storybooks: A Case Study in Hong Kong

Computational thinking (CT) currently has been mainly explored using programming robots and conducted in K12 education. In early childhood education, arts have a significant place in children’s learning, expression and cognitive development. Specifically, creating pop-up storybooks is a child-friendly activity. Our study aimed to explore the combination of CT and art, and to develop talented children’s CT abilities through a multimodal literacies educational approach, which referred to using different skills (i.e., writing, drawing, making, and storytelling) in the art activity. A total of 12 talented children were selected to participate in a pop-up storybook production workshop using a convenience sampling method. We adopted an observation method to capture talented children’s CT behaviors, generating a total of 180 min of activity videos and collecting fieldnotes and the children’s worksheets and artworks for the data analysis. Based on a content analysis, we found that talented children enhanced their CT development in multiple modes and practiced seven CT skills. In conclusion, our study emphasizes the importance of art in children’s education and provides new insights for subsequent research on children’s CT education.

A DEMONSTRATIVE-KINESTHETIC TEACHING APPROACH FOR INVERSE KINEMATICS OF A 4-DOF ROBOT MANIPULATOR

Kinesthetic guidance as a paradigm of programming by demonstration of robot manipulators has eased the process of robot programming, especially for non-skilled and semi-skilled shop-floor operators in manufacturing industries. Today, the inverse problem remains an area of interest in robotics, leading up to the deployment of robots for collaborative technology with humans. The paper proposes using a demonstrative-kinesthetic teaching technique to program a robot manipulator, determine the inverse kinematics using the approach, and compare with structured texts to program the manipulator. The approach was carried out on a Dobot magician, a 4-DOF robot manipulator. A control platform was created using MS Visual Studio IDE, using Python to control the arm, and it was programmed demonstratively using the lock arm button and structured texts to carry out a palletizing task. The joint parameters were collected and compared using the demonstrative-kinesthetic technique and structured texts as programming methods. The structured texts were used as a control for the experiment. The results showed that joint values obtained using the demonstrative-kinesthetic technique did not vary significantly from structured texts’ joint values. The approach provided an avenue for quickly programming a robot manipulator, especially concerning the non-skilled workforce, and finding analytical solutions to the inverse problem of the robot manipulator.

Investigating strategies enabling novice users to teach plannable hierarchical tasks to robots

Learning from demonstration (LfD) seeks to democratize robotics by enabling non-experts to intuitively program robots to perform novel skills through human task demonstration. Yet, LfD is challenging under a task and motion planning (TAMP) setting, as solving long-horizon manipulation tasks requires the use of hierarchical abstractions. Prior work has studied mechanisms for eliciting demonstrations that include hierarchical specifications for robotics applications but has not examined whether non-roboticist end-users are capable of providing such hierarchical demonstrations without explicit training from a roboticist for each task. We characterize whether, how, and which users can do so. Finding that the result is negative, we develop a series of training domains that successfully enable users to provide demonstrations that exhibit hierarchical abstractions. Our first experiment shows that fewer than half (35.71%) of our subjects provide demonstrations with hierarchical abstractions when not primed. Our second experiment demonstrates that users fail to teach the robot with adequately detailed TAMP abstractions, when not shown a video demonstration of an expert’s teaching strategy. Our experiments reveal the need for fundamentally different approaches in LfD to enable end-users to teach robots generalizable long-horizon tasks without being coached by experts at every step. Toward this goal, we developed and evaluated a set of TAMP domains for LfD in a third study. Positively, we find that experience obtained in different, training domains enables users to provide demonstrations with useful, plannable abstractions on new, test domains just as well as providing a video prescribing an expert’s teaching strategy in the new domain.

Short-term outcomes after the implementation of a colorectal cancer robotic surgery program

Short-term outcomes after the implementation of a colorectal cancer robotic surgery program

An advanced machining robot flexible programming methodology supported by verification in a virtual environment

ABSTRACT The solutions for robot offline programming use different programming environments to facilitate the deployment of robots in machining tasks with the development of specific postprocessors. There is no easy exchange of data between programming software to realise robot machining tasks. This paper presents a flexible programming methodology using several interchange file formats that can be easily exchanged between different CAD, CAD/CAM, or specialised robot programming software. Integrating the standard programming based on CAD, CAD/CAM systems, and STEP-NC protocol through different output files enables data interoperability during the realisation of the robot machining tasks. The developed methodology is proposed for executing programming, verification supported by a virtual environment, postprocessing, and machining by industrial robots. It uses the software for programming machine tools and adapts them to the specifics of robots and their programming languages using a developed postprocessor. The presented methodology enables the programming of robots for 2.5 to 5-axis machining tasks depending on the neutral file format used. Programming verification was realised, first, by simulation on configured virtual robots and developed robot native language editor and, second, by machining on the available robots. These experiments include different contours and shapes and file formats, which show the programming’s reliability and accuracy.