Robotic Solutions Research Articles

Enhancing ergonomics in E-waste disassembly: the impact of collaborative robotics on muscle activation and coordination

Disassembly, as a part of the electronic waste (e-waste) management process, is a labour-intensive task. The emergence of collaborative robots (cobots) provides a robotic solution to reduce the human efforts during disassembly. This study evaluated muscle activation patterns during cobot-assisted e-waste disassembly using surface electromyography (EMG). Twenty-two participants were recruited to perform disassembly tasks with and without cobot assistance. EMG signals from biceps brachii (BB), brachioradialis (BR), upper trapezius (UT), and erector spinae (ES) were collected simultaneously. Six features were then calculated to determine muscle activation patterns. Additionally, EMG-EMG coherence analysis was conducted for BR and ES muscles. Results showed a significant reduction in muscle activity with cobot assistance, particularly in the left ES muscle (46.4% decrease). Moreover, coherence between BR and ES muscles significantly increased. These findings indicate the proposed collaboration strategy not only reduces the muscle activity but also sheds light on enhancing muscle coordination during e-waste disassembly.

Future Prospects of Large Language Models: Enabling Natural Language Processing in Educational Robotics

Large language models (LLMs) have recently shown considerable promise in educational robotics by offering generic knowledge necessary in situations when prior programming is not possible. In general, mobile education robots cannot perform tasks like navigation or localization unless they have a working knowledge of maps. In this letter, we tackle the issue of making LLMs more applicable in the field of mobile education robots by helping them to understand Space Graph, a text-based map description. This study, which focuses on LLMs, is divided into several sections. It explores basic natural language processing (NLP) techniques and highlights how they can help create smooth education discussions. Examining the development of LLMs inside NLP systems, the paper explores the benefits and implementation issues of important models utilized in the education sector. Applications useful in educational discussions are described in depth, ranging from patient-focused tools like diagnosis and treatment recommendations to systems that support education providers. We provide thorough instructions and real-world examples for quick engineering, making LLM-based educational robotics solutions more accessible to novices. We demonstrate how LLM-guided upgrades can be easily included in education robotics applications using tutorial-level examples and structured prompt creation. This survey provides a thorough review and helpful advice for leveraging language models in automation development, acting as a road map for researchers navigating the rapidly changing field of LLM-driven educational robotics.

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.

A Small Robot to Repair Asphalt Road Potholes

As part of the Horizon 2020 InfraROB project aimed at enhancing road safety through innovative robotic solutions, a compact autonomous vehicle has been developed to repair asphalt potholes. Central to this system is a 3D printer capable of extruding a novel cold-asphalt mixture, specifically designed for patching road surfaces. The printer is mounted on a small robot that autonomously navigates to potholes, while the human operator controls the operation from a secure location outside the traffic area. The system’s development involved several key steps: designing the repair mixture, constructing the 3D printer for mixture extrusion, implementing a photogrammetric technique to accurately measure pothole geometry for printing, and integrating the extrusion system with the robotic platform. Two preliminary tests were conducted in controlled environments at Sapienza University of Rome to check the reliability of calculation of the amount of material needed to fill in the potholes. Finally, the entire procedure was tested on an Italian motorway, demonstrating the system’s functionality without encountering operational issues.

Automated classification of tree species using graph structure data and neural networks

The classification of tree species in urban contexts is pivotal in assessing ecosystem services and fostering sustainable urban development. This paper explores using graph neural networks (GNNs) on graph structure data derived from quantitative structure models (QSMs) and tree structural measurement for appropriate species classification. The study addresses gaps in existing methods by integrating relationships between tree components, such as branches and cylinders, and considering the entire tree structure in a novel graph data format. The results demonstrate the efficacy of GNNs, particularly the Gated Graph Convolutional Network (GatedGCN), in appropriately classifying urban tree species. It gained an overall classification accuracy and weighted F1 score of 0.84. An analysis of confusion matrices revealed similarities in visual characteristics among several species, including A. platanoides and T. cordata, which pose significant challenges in accurately distinguishing between them. However, certain species, such as A. hippocastanum and P. nigra var. italica, have proved easier to classify than others. Furthermore, the results highlight the importance of relationships between different tree components in species recognition, such as the ratio between branch radius and parent branch radius, the factors often overlooked by previous methods. This underscores the novelty and effectiveness of the proposed approach in this study. Future research could explore integrating additional data sources, such as Leaf Area Density (LAD) calculated from LiDAR and hyperspectral data, to enhance classification accuracy. In conclusion, the evaluation results of the GatedGCN model demonstrated its effectiveness in classifying tree species using a novel data structure format derived from QSM tree characteristics. Advancing urban tree species classification through such methods can enhance future urban tree management using automated AI and robotics solutions.

Pilot study protocol evaluating the impact of telerobotics interactions with autistic children during a Denver intervention on communication skills using single-case experimental design

IntroductionFor several years, studies have been conducted on the contribution of social robots as an intervention tool for children with autism spectrum disorder (ASD). One of the early intervention models...

A multimodal educational robots driven via dynamic attention.

With the development of artificial intelligence and robotics technology, the application of educational robots in teaching is becoming increasingly popular. However, effectively evaluating and optimizing multimodal educational robots remains a challenge. This study introduces Res-ALBEF, a multimodal educational robot framework driven by dynamic attention. Res-ALBEF enhances the ALBEF (Align Before Fuse) method by incorporating residual connections to align visual and textual data more effectively before fusion. In addition, the model integrates a VGG19-based convolutional network for image feature extraction and utilizes a dynamic attention mechanism to dynamically focus on relevant parts of multimodal inputs. Our model was trained using a diverse dataset consisting of 50,000 multimodal educational instances, covering a variety of subjects and instructional content. The evaluation on an independent validation set of 10,000 samples demonstrated significant performance improvements: the model achieved an overall accuracy of 97.38% in educational content recognition. These results highlight the model's ability to improve alignment and fusion of multimodal information, making it a robust solution for multimodal educational robots.

Embracing Levin’s Legacy: Advancing Socio-Technical Learning and Development in Human-Robot Team Design Through STS Approaches

AbstractThis paper investigates the synergy between Levin’s theories on technology transfer as a socio-technical learning and developmental process (TLD process), and what we learnt about socio-technical systems (STS) theories in a case study developing human robot solutions for the construction sector. Levin’s extensive work highlights the significance of technology transfer as a means for organizational development. His TLD process emphasizes the intricate interplay between technology, organizational change, and learning and highlights the importance of incorporating cultural knowledge and skills into the technological transfer process. Contemporary STS views developed through our own work are introduced to complement and extend Levin’s theories by providing a systemic lens to understand the broader socio-technical context in which technology transfer occurs. To illustrate the synergies and potential challenges from Levin’s theories of technology transfer with contemporary STS concepts, we use a qualitative study of a unique case about the design and development of human-robot teams (HRTs) for construction tasks. Our findings reveal that while Levin’s theories provide a valuable foundation for understanding technology transfer and organizational change, contemporary socio-technical systems face unique challenges in the context of AI-driven human-robot teams, where intelligent robots also contribute to the socio-technical learning process. Moreover, the rapidly evolving nature of technology and innovations could exponentially impact on multidisciplinary design teams, stakeholder participation and inter-organizational dynamics. The discussions suggest an extension of co-generative learning to incorporate ‘collaborative intelligence’ between human-robot teams enabled by artificial intelligence (AI). Consequently, we suggest that Levin’s theories of technology transfer, developed before the rapid application of AI, may not have fully considered further social challenges caused by the introduction of autonomous systems such as AI-driven HRT systems. We extend Levin’s important work by suggesting that addressing such challenges requires ongoing dialogue and collaboration among researchers, practitioners, and policymakers with different disciplinary backgrounds to develop robust and reliable socio-technical systems frameworks to navigate the complexities of robotics and AI in today’s rapidly evolving technological landscape.

A repeated jumping solution based on the bistable film structure for film-structured miniature soft robots

Abstract Miniature film-structured robots achieved remarkable locomotion performance and various applications. The integration of a jumping function could empower existing film-structured terrestrial robots to conquer complex terrains. However, current repeated jumping mechanisms for miniature soft robots are often constrained to specific materials and mechanical configurations, rendering them incompatible with film-structured robots. A solution that effectively addresses these limitations remains absent in the field. This work introduces an electrically actuated solution for repeated jumping in miniature soft robots, utilizing a bistable film structure coupled with a numerical two-rod model. The bistable film structure integrates a flexible film, polydimethylsiloxane (PDMS) linear spring, and a shape memory alloy spring to achieve repeated jumping motion through snap-through buckling. The numerical model is employed to optimize the spring parameters, tailoring the structure to the specific properties of the target robot. To demonstrate the practicality of this solution, the bistable film structure is seamlessly integrated into an existing fast-steering insect robot, which previously lacks jumping capabilities. By customizing PDMS spring design based on the robot body’s dimensions and stiffness, the prototype robot, measuring 23 × 16 × 8 mm3 in size and 120 mg in weight, demonstrates repeated jumping ability with a maximum horizontal jumping distance of 11.8 cm (5.1 body lengths), a jumping height of 3.8 cm (4.7 body heights) and a jumping frequency of 0.1 Hz. The robot traverses a 43 cm-long road with half walls and trench obstacles in 41 s. This work presents the bistable film structure’s potential as a repeated jumping solution for film-structured robots, enhancing their obstacle-crossing abilities and expanding their applicability in complex environments.

Deep learning based approach for actinidia flower detection and gender assessment

Pollination is critical for crop development, especially those essential for subsistence. This study addresses the pollination challenges faced by Actinidia, a dioecious plant characterized by female and male flowers on separate plants. Despite the high protein content of pollen, the absence of nectar in kiwifruit flowers poses difficulties in attracting pollinators. Consequently, there is a growing interest in using artificial intelligence and robotic solutions to enable pollination even in unfavourable conditions. These robotic solutions must be able to accurately detect flowers and discern their genders for precise pollination operations. Specifically, upon identifying female Actinidia flowers, the robotic system should approach the stigma to release pollen, while male Actinidia flowers should target the anthers to collect pollen. We identified two primary research gaps: (1) the lack of gender-based flower detection methods and (2) the underutilisation of contemporary deep learning models in this domain. To address these gaps, we evaluated the performance of four pretrained models (YOLOv8, YOLOv5, RT-DETR and DETR) in detecting and determining the gender of Actinidia flowers. We outlined a comprehensive methodology and developed a dataset of manually annotated flowers categorized into two classes based on gender. Our evaluation utilised k-fold cross-validation to rigorously test model performance across diverse subsets of the dataset, addressing the limitations of conventional data splitting methods. DETR provided the most balanced overall performance, achieving precision, recall, F1 score and mAP of 89%, 97%, 93% and 94%, respectively, highlighting its robustness in managing complex detection tasks under varying conditions. These findings underscore the potential of deep learning models for effective gender-specific detection of Actinidia flowers, paving the way for advanced robotic pollination systems.

Monitoring system for physical activity and fitness based on service robots and biomechanics

Exercise is one of the important ways for people to exercise, with characteristics such as sociality and strong participation. Especially with the improvement of the level of economic development and the improvement of the quality of life of the people, more and more people begin to attach importance to the maintenance of their own health. Physical fitness monitoring, as an effective means, is widely used in daily life, especially among the elderly. However, most of the existing monitoring methods are relatively simple, lacking pertinence, and the data collection process is relatively cumbersome and unstable, which cannot meet current needs. Therefore, it is very necessary to explore a new type of equipment that can more comprehensively and accurately monitor various physiological parameters of the human body to replace existing traditional detection technologies. Service Robots are currently the most promising intelligent hardware products. They mainly provide personalized services centered on users, sensing user behavior and implementing intelligent decision-making based on their characteristics, thereby better meeting the needs of different groups of people. This article focused on the research and development of Service Robots, and designed a comprehensive solution for Service Robots based on theories such as the Internet of Things, cloud computing, big data technology, and artificial intelligence. This article compared existing intelligent monitoring systems with fitness monitoring systems based on Service Robots, and proved that the user experience of fitness monitoring with robot participation has improved by about 4.68%. Its application scenarios were richer and its effects were more significant, enabling it to better complete tasks such as analysis and prediction of physical fitness status, real-time warning, etc., reducing the risk of people suffering from diseases, and enhancing individual protection awareness.

HoLLiECares - Development of a multi-functional robot for professional care.

Germany's healthcare sector suffers from a shortage of nursing staff, and robotic solutions are being explored as a means to provide quality care. While many robotic systems have already been established in various medical fields (e.g., surgical robots, logistics robots), there are only a few very specialized robotic applications in the care sector. In this work, a multi-functional robot is applied in a hospital, capable of performing activities in the areas of transport and logistics, interactive assistance, and documentation. The service robot platform HoLLiE was further developed, with a focus on implementing innovative solutions for handling non-rigid objects, motion planning for non-holonomic motions with a wheelchair, accompanying and providing haptic support to patients, optical recognition and control of movement exercises, and automated speech recognition. Furthermore, the potential of a robot platform in a nursing context was evaluated by field tests in two hospitals. The results show that a robot can take over or support certain tasks. However, it was noted that robotic tasks should be carefully selected, as robots are not able to provide empathy and affection that are often required in nursing. The remaining challenges still exist in the implementation and interaction of multi-functional capabilities, ensuring ease of use for a complex robotic system, grasping highly heterogeneous objects, and fulfilling formal and infrastructural requirements in healthcare (e.g., safety, security, and data protection).

A novel human-centered methodology for assessing manual-to-collaborative safe conversion of workstations

The Industry 5.0 paradigm places workers’ well-being and safety at the core of the production processes. Despite its objectives, the current industry still faces several challenges. Examples of these challenges include global market pressures for customized products, along with the significant occurrence of Work-Related Musculoskeletal Disorders and the ageing workforce. These challenges underscore the persistent need for human-centered solutions, allowing adaptations that align with humans’ physical and cognitive constraints. Collaborative robots offer a promising solution, with the potential to enhance workers’ well-being, while maintaining safety, productivity, and production process flexibility. This study introduces a novel methodology designed to assess the feasibility of converting manual tasks into collaborative ones. During the development of the methodology, a focus group approach was used, comprising 6 experts in the field of Ergonomics & Human Factors and Robotics. The proposed methodology is suitable for the identification of tasks appropriate for joint execution by humans and robots, thereby augmenting the effectiveness of the robotic solution. This methodology incorporates a set of indicators (physical and cognitive ergonomics, health and safety, and operational efficiency) and technical requirements. The results of the focus group allowed the improvement of the suggested methodology. For its testing, we applied across 13 manual workstations in 5 companies from different manufacturing sectors (civil construction, cutlery, furniture, and automotive fabric manufacturing). Results show that it is possible to comprehensively identify manual workstations/tasks with good and high potential to convert into collaborative systems. This methodology seems to constitute a relevant approach to support the conceptualization of collaborative workstations.

In‐Pipe Maintenance Robot Using Spray‐In‐Place Pipe Technique for Long‐Distance and Complex Pipe Environment

ABSTRACTConventional pipe rehabilitation technologies are not the most sustainable solution for the utility industry because of various drawbacks, such as time consumption, disturbance to operations, society, and traffic, and the production of waste material. However, aged underground metal pipes are affected by natural forces. Trenchless rehabilitation technologies have been introduced for the treatment and renovation of aged pipes to ensure that utilities are safe and durable from an economic perspective. This paper proposes a portable and automatic robotic solution for a trenchless spray‐in‐place pipe approach for small underground pipelines. This approach shortens the duration of the rehabilitation operation from approximately a week to < 5 h. The robot delivered and monitored a protective well‐mixed urethane coating with plural components accurately using rotary mixing and spraying with a low‐pressure and safer configuration (< 0.8 MPa). The robot exhibited superior capacities in complicated pipe environments for turning into 90° elbows and 6‐in. (150 mm), 8‐in. (200 mm), and 10‐in. (250 mm) pipelines. An automated robot was deployed and rehabilitated at various underground and on‐ground pipe sites. The spray performance was validated using tensile tests. This robot can be applied to any pipeline system and can be developed for other in‐pipe robotic construction and renovation approaches.

Self‐Propelled Morphing Matter for Small‐Scale Swimming Soft Robots

AbstractAquatic insects have developed versatile locomotion mechanisms that have served as a source of inspiration for decades in the development of small‐scale swimming robots. However, despite recent advances in the field, efficient, untethered, and integrated powering, actuation, and control of small‐scale robots remains a challenge due to the out‐of‐equilibrium and dissipative nature of the driving physical and chemical phenomena. Here, we have designed small‐scale, bioinspired aquatic locomotors with programmable deterministic trajectories that integrate self‐propelled chemical motors and photoresponsive shape‐morphing structures. A Marangoni motor system is developed integrating structural protein networks that self‐regulate the release of chemical fuel with photochemical liquid crystal network (LCN) actuators that change their shape and deform in and out of the surface of water. While the diffusion of fuel from the motor system regulates the propulsion, the dissipative photochemical deformation of LCNs provides locomotors with control over the directionality of motion at the air‐water interface. This approach gives access to five different but interchangeable modes of locomotion within a single swimming robot via morphing of the soft structure. The proposed design, which mimics the mechanisms of surface gliding and posture change of semiaquatic insects such as water treaders, offers solutions for autonomous swimming soft robots via untethered and orthogonal power and control.

Path planning of manure-robot cleaners using grid-based reinforcement learning

The use of a robot cleaner for manure removal improves housing conditions for dairy cows in the face of labor shortages. However, current robot cleaners follow programmed fixed routes without considering the dynamic behaviors of cows. This cleaning approach is less efficient and leads to more cow-robot encounters or collisions, thus affecting animal welfare. To address these issues, this paper (1) developed heatmap models for cow locations and defecation behaviors; (2) proposed a dynamic path planning approach for the manure robot cleaner using Grid-based Reinforcement Learning; (3) incorporated cow location information and defecation behavior into the path planning process; (4) compared the performance of the proposed approach with two different cleaning methods: the current fixed programmed cleaning in practice and the ideal path produced by simulated annealing for traveling salesman problem. The simulations mimic the situation in a barn at Dairy Campus of Wageningen Livestock Research located in Leeuwarden (the Netherlands). Obviously, the best performance was achieved when the route was executed without cows present, resulting in no cow-robot collision. However, with cows present, the proposed dynamic path planning strategy achieved a 67.6% reduction in cow-robot encounters while maintaining 85.4% of the cleaning performance compared to the current programmed fixed routes. Compared to the ideal path produced by simulated annealing for traveling salesman problem, the proposed dynamic path planning approach achieved 5% better cleaning performance, at the cost of 25% more cow-robot encounters due to its longer working path. We conclude the proposed grid-based Reinforcement Learning solution for manure robots in barns cleaned most efficient with the least interference with cow traffic.

Robots for the Energy Transition: A Review

The energy transition relies on an increasingly massive and pervasive use of renewable energy sources, mainly photovoltaic and wind, characterized by an intrinsic degree of production uncertainty, mostly due to meteorological conditions variability that, even if accurately estimated, can hardly be kept under control. Because of this limit, continuously monitoring the operative status of each renewable energy-based power plant becomes relevant in order to timely face any other uncertainty source such as those related to the plant operation and maintenance (O&M), whose effect may become relevant in terms of the levelized cost of energy. In this frame, the use of robots, which incorporate fully automatic platforms capable of monitoring each plant and also allow effective and efficient process operation, can be considered a feasible solution. This paper carries out a review on the use of robots for the O&M of photovoltaic, wind, hydroelectric, and concentrated solar power, including robot applications for controlling power lines, whose role can in fact be considered a key complementary issue within the energy transition. It is shown that various robotic solutions have so far been proposed both by the academy and by industries and that implementing their use should be considered mandatory for the energy transition scenario.

Robotic solutions for precision agriculture

The robotic system is a key component in modern agriculture. The article aims to expand robotic solutions for precision agriculture and improve productivity, resource utilization, decision-making, sustainability, and farmworker safety. It aims to automate repetitive tasks, gather real-time data, promote sustainable practices, and reduce risks for farmworkers. They offer numerous potential solutions to issues related to the growing global population, changing demographics, and economic status. This article investigates how robotic systems can be significant to precision agriculture. Traditional farming is facing issues such as climate change, resource depletion, labour shortage, etc. The use of robotic systems makes precision agriculture achievable and it provides a sustainable solution. This study examined the importance of robotics in agricultural processes such as planting, seeding, weeding, and harvesting. The potential benefits of robotic solutions, such as increased efficiency, reduced labour costs, and improved crop yield, are explored. The article identifies key challenges and opportunities associated with robotic implementation in agriculture. The research aids in creative effective agriculture techniques by imaging the future use of robotics in agriculture.

Robustness multi-objective optimization for parallel robot based on subregional meta-heuristic iteration

PurposeThis work aims to provide a rapid robust optimization design solution for parallel robots or mechanisms, thereby circumventing inefficiencies and wastage caused by empirical design, as well as numerous physical verifications, which can be employed for creating high-quality prototypes of parallel robots in a variety of applications.Design/methodology/approachA novel subregional meta-heuristic iteration (SMI) method is proposed for the optimization of parallel robots. Multiple subregional optimization objectives are established and optimization is achieved through the utilisation of an enhanced meta-heuristic optimization algorithm, which roughly employs chaotic mapping in the initialization strategy to augment the diversity of the initial solution. The non-dominated sorting method is utilised for updating strategies, thereby achieving multi-objective optimization.FindingsThe actuator error under the same trajectory is visibly reduced after SMI, with a maximum reduction of 6.81% and an average reduction of 1.46%. Meanwhile, the response speed, maximum bearing capacity and stiffness of the mechanism are enhanced by 63.83, 43.98 and 97.51%, respectively. The optimized mechanism is more robust and the optimization process is efficient.Originality/valueThe proposed robustness multi-objective optimization via SMI is more effective in improving the performance and precision of the parallel mechanisms in various applications. Furthermore, it provides a solution for the rapid and high-quality optimization design of parallel robots.

A Highly Sensitive 3D-Printed Flexible Sensor for Sensing Small Pressures in Deep-Sea High-Pressure Environment.

The origin of life on Earth is believed to be from the ocean, which offers abundant resources in its depths. However, deep-sea operations are limited due to the lack of underwater robots and rigid grippers with sensitive force sensors. Therefore, it is crucial for deep-sea pressure sensors to be integrated with mechanical hands for manipulation. Here, a flexible stress sensor is presented that can function effectively under high water pressure in the deep ocean. Inspired by biological structures found in the abyssal zone, our sensor is designed with an internal and external pressure balance structure (hollow interlocking spherical structure). The digital light processing (DLP) three-dimensional (3D) printing technology is utilized to construct this complex structure after obtaining optimized structural parameters using finite element simulation. The sensor exhibits linear sensitivity of 0.114 kPa-1 within the range of 0-15 kPa and has an extremely short response time of 32 ms, good dynamic-static load response capability, and excellent resistance cycling stability. It shows high sensitivity of 1.74 kPa-1 even under 30 MPa static water pressures and the theoretical working pressure can exceed 110 MPa, which provides a new solution for deep-sea robots.