Robotic Applications Research Articles

Development of a novel two-way 3D printed flexible spiral composite actuator based on shape memory alloy wire and its control

Soft robotics find its applications across numerous of scientific and industrial fields, spanning from medicine and surgery to gripper technology, assistive devices, and exploration in underwater and space. The study introduces a soft actuator design for soft robotics, produced using 3D printing technology, offering an efficient alternative to traditional molding and curing methods. A shape memory alloy wire is integrated to the spiral body printed using a flexible filament. The spiral enhances the actuation stroke (AS) to 2 cm for a wire of 189 mm in length, while actuation in the literature is typically accomplished through an axial AS of 3%–5% of the wire’s length. Four types of spirals with increasing gaps are prepared to observe the cooling effect. Their performances are evaluated in terms of AS and time through image processing in order to determine the optimal configuration. An electrical current constraint is established to prevent potential damage, and spiral control is attained using a proportional–integral–derivative controller. Moreover, a pick and place operation showcases the spiral’s ability to autonomously lift a gripped object weighing 6.5 g, achieving a specific displacement of 6.5 mm. Subsequently, the object is lifted down to its initial position using a two-way actuator that utilizes the stored energy within the spiral’s structure and elastic effect. The proposed actuator has the potential to be widely applied across various soft robotic applications, including medical robots, delicate gripping robots, and bioinspired robots.

Learning-based methods for adaptive informative path planning

Adaptive informative path planning (AIPP) is important to many robotics applications, enabling mobile robots to efficiently collect useful data about initially unknown environments. In addition, learning-based methods are increasingly used in robotics to enhance adaptability, versatility, and robustness across diverse and complex tasks. Our survey explores research on applying robotic learning to AIPP, bridging the gap between these two research fields. We begin by providing a unified mathematical problem definition for general AIPP problems. Next, we establish two complementary taxonomies of current work from the perspectives of (i) learning algorithms and (ii) robotic applications. We explore synergies, recent trends, and highlight the benefits of learning-based methods in AIPP frameworks. Finally, we discuss key challenges and promising future directions to enable more generally applicable and robust robotic data-gathering systems through learning. We provide a comprehensive catalog of papers reviewed in our survey, including publicly available repositories, to facilitate future studies in the field.

The role of RObotic surgery in EMergency setting (ROEM): protocol for a multicentre, observational, prospective international study on the use of robotic platform in emergency surgery

BackgroundRobotic surgery has gained widespread acceptance in elective interventions, yet its role in emergency procedures remains underexplored. While the 2021 WSES position paper discussed limited studies on the application of robotics in emergency general surgery, it recommended strict patient selection, adequate training, and improved platform accessibility. This prospective study aims to define the role of robotic surgery in emergency settings, evaluating intraoperative and postoperative outcomes and assessing its feasibility and safety.MethodsThe ROEM study is an observational, prospective, multicentre, international analysis of clinically stable adult patients undergoing robotic surgery for emergency treatment of acute pathologies including diverticulitis, cholecystitis, and obstructed hernias. Data collection includes patient demographics and intervention details. Furthermore, data relating to the operating theatre team and the surgical instruments used will be collected in order to conduct a cost analysis. The study plans to enrol at least 500 patients from 50 participating centres, with each centre having a local lead and collaborators. All data will be collected and stored online through a secure server running the Research Electronic Data Capture (REDCap) web application. Ethical considerations and data governance will be paramount, requiring local ethical committee approvals from participating centres.DiscussionCurrent literature and expert consensus suggest the feasibility of robotic surgery in emergencies with proper support. However, challenges include staff training, scheduling conflicts with elective surgeries, and increased costs. The ROEM study seeks to contribute valuable data on the safety, feasibility, and cost-effectiveness of robotic surgery in emergency settings, focusing on specific pathologies. Previous studies on cholecystitis, abdominal hernias, and diverticulitis provide insights into the benefits and challenges of robotic approaches. It is necessary to identify patient populations that benefit most from robotic emergency surgery to optimize outcomes and justify costs.

Simultaneously learning intentions and preferences during physical human-robot cooperation

The advent of collaborative robots allows humans and robots to cooperate in a direct and physical way. While this leads to amazing new opportunities to create novel robotics applications, it is challenging to make the collaboration intuitive for the human. From a system’s perspective, understanding the human intentions seems to be one promising way to get there. However, human behavior exhibits large variations between individuals, such as for instance preferences or physical abilities. This paper presents a novel concept for simultaneously learning a model of the human intentions and preferences incrementally during collaboration with a robot. Starting out with a nominal model, the system acquires collaborative skills step-by-step within only very few trials. The concept is based on a combination of model-based reinforcement learning and inverse reinforcement learning, adapted to fit collaborations in which human and robot think and act independently. We test the method and compare it to two baselines: one that imitates the human and one that uses plain maximum entropy inverse reinforcement learning, both in simulation and in a user study with a Franka Emika Panda robot arm.

A Naturally Inspired Extrusion-Based Microfluidic Approach for Manufacturing Tailorable Magnetic Soft Continuum Microrobotic Devices.

Soft materials play a crucial role in small-scale robotic applications by closely mimicking the complex motion and morphing behavior of organisms. However, conventional fabrication methods face challenges in creating highly integrated small-scale soft devices. In this study, microfluidics is leveraged to precisely control reaction-diffusion (RD) processes to generate multifunctional and compartmentalized calcium-cross-linkable alginate-based microfibers. Under RD conditions, sophisticated alginate-based fibers are produced for magnetic soft continuum robotics applications with customizable features, such as geometry (compact or hollow), degree of cross-linking, and the precise localization of magnetic nanoparticles (inside the core, surrounding the fiber, or on one side). This fine control allows for tuning the stiffness and magnetic responsiveness of the microfibers. Additionally, chemically cleavable regions within the fibers enable disassembly into smaller robotic units or roll-up structures under a rotating magnetic field. These findings demonstrate the versatility of microfluidics in processing highly integrated small-scale devices.

Bead Size and Rheological Properties of SEBS-Based Elastic Beads

Abstract Styrene-ethylene-butylene-styrene, (SEBS), is a thermoplastic elastomer that has applications in robotics and shock absorption. While SEBS as a bulk material as well as an additive to solid composites has been extensively studied, this work focuses on developing SEBS-based beads to enhance material, particularly fluid, elasticity; the first time this has been seen in literature. SEBS bead mixtures were developed by mixing SEBS elastomer, water, and surfactant (Triton X-100) at high temperature. Stability, rheology, and microscopy of SEBS bead mixtures were studied as a function of neat SEBS concentration in SEBS elastomer, SEBS elastomer concentration, and surfactant concentration. Resulting bead mixtures were classified as creamed, homogenous and stable, or aggregated bead mixtures based on the mixture's tendency to separate into layers and ability to disperse in excess water. Microscopic studies suggest that while bead mixtures exhibit size polydispersity, the average bead size is a strong function of neat SEBS, SEBS elastomer, and surfactant concentrations. Rheological studies suggest that all the bead mixtures exhibit shear thinning behavior, and the overall viscosity of a given bead mixture is a function of both SEBS elastomer and surfactant concentration. The developed SEBS elastic beads can be used as additives to enhance the viscoelastic properties of fluid-based systems like magnetorheological and damping fluids.

Numerical study of electroconductive non-Newtonian hybrid nanofluid flow from a stretching rotating disk with a Cattaneo–Christov heat flux model

Motivated by emerging technologies in smart functional nanopolymeric coating systems in pharmaceutical and robotics applications, the convective heat transfer characteristics in swirl coating with a magnetic hybrid power-law rheological nanofluid polymer on a radially stretching rotating disk are examined theoretically. An axial magnetic field is imposed. Copper (Cu) and aluminium alloy (AA7075) nanoparticles with sodium alginate (C6H9NaO7) as a base fluid are considered, and a hybrid volume fraction model is deployed. A non-Fourier (Cattaneo–Christov) heat flux model is deployed to inspire thermal relaxation impacts absent in the classical Fourier heat conduction formulation. Through similarity proxies and the Von Karman transformations, the partial derivative systems of equations with associated boundary constraints are reduced to a system of derivative boundary value problems, which is solved numerically via the weighted residual method with an integral Galerkin scheme, executed in the MAPLE symbolic software. Validation with special cases from articles is captured. The computations revealed that radial, tangential and axial velocity are depleted, whereas temperature is enhanced with increasing magnetic parameter ( M). High thermal transfers are obtained for the hybrid nanofluid relative to the AA7075 alloy nanofluid. A strong increment in temperature is also produced with a greater thermal relaxation effect.

Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms

Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs.

Nano-Sized rGO-Encapsulated TiO2 Nanowire-Filled PDMS cone type dielectric elastomer actuator operating at low applied electric field

Dielectric elastomer actuators (DEAs) have versatile applications in soft robotics, medical devices, and environmental monitoring, making them a highly anticipated area for future applications. On the other hand, developing DEAs exhibiting high strain at low voltages remains challenging. This paper reports a strategy for enhancing the actuating performance of polydimethylsiloxane (PDMS) at low voltages by preparing a hybrid filler comprised of TiO2 nanowires (TiO2 NWs), polydopamine (PDA), and nano-sized reduced graphene oxide (nrGO). This hybrid filler, merging the virtues of these three materials, was added at 15 parts per hundred of rubber (phr), resulting in a 2.3-fold increase in the dielectric permittivity of PDMS while mitigating the increase in loss tangent and enhancing efficiency. Actuators fabricated using this composite exhibited the highest deformation at 10 phr, reaching approximately 27.31 % (at 28 V/µm), representing a remarkable 15.2-fold improvement compared to pure PDMS. Moreover, even at a low voltage of 1.6 V/µm, they displayed a substantial actuated strain of 2 %. This novel strategy for manufacturing hybrid fillers is a promising example of enhancing the performance of DEAs, offering innovative solutions for future technological advancements.

Optimizing Object Classification in Robotic Perception Environments Exploring Late Fusion Strategies

Robotic perception systems often include approaches that can extract valuable features or information from the studied dataset. These methods often involve the application of deep learning approaches, such as convolutional neural networks (CNNs), for processing of images, as well as the incorporation of 3D data. The notion of image categorization is well delineated via the use of networks that include convolutional networks. However, some network topologies exhibit a substantial scope and need significant amounts of time and memory resources. On the other hand, the neural networks FlowNet3D and PointFlowNet have the capability to accurately predict scene flow. Specifically, these networks are capable of estimating the three-dimensional movements of point clouds (PCs) within a dynamic environment. When using PCs in robotic applications, it is crucial to examine the robustness of accurately recognizing the points that belong to the object. This article examines the use of robotic perception systems inside autonomous vehicles and the inherent difficulties linked to the analysis and processing of information obtained from diverse sensors. The researchers put out a late fusion methodology that integrates the results of many classifiers in order to enhance the accuracy of categorization. Additionally, the authors propose a weighted fusion technique that incorporates the proximity to objects as a significant factor. The findings indicate that the fusion methods described in this study exhibit superior performance compared to both single modality classification and classic fusion strategies.

Design and Scalable Fast Fabrication of Biaxial Fabric Pouch Motors for Soft Robotic Artificial Muscle Applications

Soft pouch motors, engineered to mimic the natural movements of skeletal muscles, play a crucial role in advancing robotics and exoskeleton development. However, the fabrication techniques often involve multistage processes; they lack soft sensing capabilities and are sensitive to cutting and damage. This work introduces a new textile‐based pouch motors with the capacity for biaxial actuation and capacitive sensory functions, achieved through the application of computerized knitting technology using ultrahigh molecular weight polyethylene yarn (Spectra) and conductive silver yarns. This method enables the rapid and scalable mass fabrication of robust pouch motors. The resulting pouch motors exhibit maximum lifting capacity of 10 kg, maximum contraction of 53.3% along the y‐axis, and transverse extension of 41.18% along the x‐axis at 50 kPa pressure. Finite element analysis closely matches the experimental data. The capacitance signals in relation to contraction motion are well suited for detecting air pressure levels and hold promise for applications requiring robotic control. Notably, it effectively elevates an ankle joint simulator at a 20° angle, highlighting its potential for applications such assisting individuals with foot drop. This study presents a practical demonstration of the soft ankle exosuit designed to provide lifting support for individuals facing this mobility challenge.

Light‐activated 3D printed fish‐like actuator

AbstractHydrogel‐based actuators are innovative materials that exhibit responsive and dynamic behavior in response to external stimuli, making them promising candidates for a wide range of applications in fields such as soft robotics. We employ 3D printing to create layered rectangles, using a passive ink composed of gelatin methacryloyl (GelMA) and an active ink consisting of GelMA and poly(N‐isopropylacrylamide) (PNIPAM). Our aim is to assess and compare the bending capabilities of these structures based on their layer arrangements in a buffer above the lower critical solution temperature. Therefore, an asymmetric rectangular structure is selected as the shape‐changing component in the tail of a 3D printed fish‐shaped hydrogel actuator. We show that gold nanostars integrated into the actuators serve as photothermal elements, enabling the fish tail to repeatedly bend when near infrared light is turned on and off. Our effort illustrates the potential of combining 3D printing, responsive hydrogels and photothermal elements with near infrared light towards soft robotic applications. This combination yields actuators capable of shape‐shifting without requiring localized light or transitioning between environments with varying temperatures.

A Miniature Traveling Wave Ultrasonic Linear Motor Utilizing Bimorph Transducers.

Scaling down linear actuators is crucial for various industrial applications, yet the efficiency of electromagnetic linear actuators decreases significantly as they are miniaturized. Millimeter-scale miniature ultrasonic motors, on the other hand, maintain high efficiency. This paper describes a new approach to facilitate the miniaturization of traveling wave linear ultrasonic motors by attaching bimorph transducers to the ends of the stator beams. To control the resonance frequency and facilitate the generation of a traveling flexural wave in the beam, grooves are incorporated into the bimorph structure. Mechanical output is improved by amplifying the transverse displacement through the addition of teeth to the beam. Utilizing the Finite Element Method (FEM), a prototype measuring 10 mm × 10 mm × 160 mm was designed, fabricated, and tested. It achieved an output speed of 53.7 mm/s and a thrust of 0.83 N at a peak-to-peak voltage of 300 V and a frequency of 32.7 kHz. The results show that the proposed ultrasonic linear motors with small size, simple structure and overall compactness have promising applications in robotics, precision machining, medical equipment and other fields requiring miniature compact linear motions.

Robotics applications, inclusive employment and income disparity

Robots are outstanding representatives of contemporary high-end intelligent equipment and high-tech. Widespread industrial robot adoption has profound implications for employment quality now and in the future. This study utilizes international and Chinese data to estimate provincial robot density. Fixed-effects models reveal industrial robot adoption improves labor conditions, especially for low-skilled and agricultural workers, indicating inclusive employment gains. However, quantile regression demonstrates an inverted U-shaped relationship between robot adoption and employment quality, suggesting diminishing marginal returns. Further analysis uncovers rising income inequality from robot adoption, shown by increased labor income variation. The study aims to inform policies promoting quality employment in the digital era while optimizing income distribution as automation reshapes work. Understanding robotics' nuanced impact on employment and inequality is critical for evidence-based policymaking. Key findings show robots presently boost job quality for vulnerable workers but may also widen income gaps. More research is needed on sustainable policies maximizing the benefits of automation while ensuring broadly shared prosperity. As automation diffuses through the economy, policymakers must carefully balance productivity with equity. This study contributes robust data analysis illuminating the complex interactions between robot adoption, employment gains, and income distribution. The results highlight the need for calibrated policies targeting inclusive growth as automation transforms the labor market. Further research should explore these dynamics across countries and industries. With thoughtful governance, robotics can raise productivity and living standards for all.

Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot.

Path planning creates the shortest path from the source to the destination based on sensory information obtained from the environment. Within path planning, obstacle avoidance is a crucial task in robotics, as the autonomous operation of robots needs to reach their destination without collisions. Obstacle avoidance algorithms play a key role in robotics and autonomous vehicles. These algorithms enable robots to navigate their environment efficiently, minimizing the risk of collisions and safely avoiding obstacles. This article provides an overview of key obstacle avoidance algorithms, including classic techniques such as the Bug algorithm and Dijkstra's algorithm, and newer developments like genetic algorithms and approaches based on neural networks. It analyzes in detail the advantages, limitations, and application areas of these algorithms and highlights current research directions in obstacle avoidance robotics. This article aims to provide comprehensive insight into the current state and prospects of obstacle avoidance algorithms in robotics applications. It also mentions the use of predictive methods and deep learning strategies.

Stiff morphing composite beams inspired from fish fins.

Morphing materials are typically either very compliant to achieve large shape changes or very stiff but with small shape changes that require large actuation forces. Interestingly, fish fins overcome these limitations: fish fins do not contain muscles, yet they can change the shape of their fins with high precision and speed while producing large hydrodynamic forces without collapsing. Here, we present a 'stiff' morphing beam inspired from the individual rays in natural fish fins. These synthetic rays are made of acrylic (PMMA) outer beams ('hemitrichs') connected with rubber ligaments which are 3-4 orders of magnitude more compliant. Combinations of experiments and models of these synthetic rays show strong nonlinear geometrical effects: the ligaments are 'mechanically invisible' at small deformations, but they delay buckling and improve the stability of the ray at large deformations. We use the models and experiments to explore designs with variable ligament densities, and we generate design guidelines for optimum morphing shape (captured using the first moment of curvature), that capture the trade-offs between morphing compliance (ease of morphing the structure) and flexural stiffness. The design guidelines proposed here can help the development of stiff morphing bioinspired structures for a variety of applications in aerospace, biomedicine or robotics.

Development of an unmanned ground vehicle for seed planting

As global population growth intensifies the demand for sustainable food production, the application of robotics to agriculture emerges as a promising solution. This research focuses on the design, development, and deployment of an unmanned ground vehicle for seed planting, also known as a robotic seed planter. The robotic seed planter automates seed planting processes, offering advantages such as increased accuracy, reduced labour requirements, and optimal resource usage. Parametric Technology Corporation (PTC) Creo was used for the structural design, Proteus 8.14 for the circuitry design, and Arduino IDE 2.0 with Visual Studio Code for the programming. The design incorporates seed metering and drilling mechanisms guided by intelligent systems. Results show exceptional accuracy in seed placement (94%), operational efficiency, and adaptability to diverse conditions, with energy consumption relatively low. The planter is equipped with a web application for remote monitoring and control. The application is hosted on one of the microcontrollers and WebSockets protocol is utilized for inter-microcontroller communication. It offers an auto mode for automated planting and Manual mode for easier manoeuvrability. The findings of this study demonstrate the robotic seed planter’s transformative impact on precision agriculture, providing a glimpse into the future of efficient and sustainable farming operations.

TEACHING MATHEMATICS WITH VIRTUAL ROBOTS TO STUDENTS

The study introduces the case of development of mathematics oriented 20-h robotics course for students (n=50) with educational program of “6B01501 - Mathematics”. The research aimed at exploring the impact of robotics on students’ motivation and engagement by learning math using robotics activities. These activities include controlling virtual robots while learning Algebra concepts. Overall, the aim of practicing math using robots is to investigate the effectiveness of this approach to teaching math, and to identify ways to improve math education using technology. During the study, the researchers examined the global application of robotics and conducted a review of academic databases to collect relevant literature. The findings revealed a dearth of knowledge in the academic literature concerning this particular subject. The study was carried out for three months on the basis of NJSC "Zhetysu University named after Ilyas Zhansugurov" (Taldykorgan), during which the training program "Learning Mathematics with the help of robotics" was developed and tested. Example of lessons at roboblocky.com implemented by students presented in this article. Roboblocky is an educational platform that integrates robotics and programming to facilitate the instruction of mathematical and scientific concepts to students. The platform employs a block-based programming approach, which enhances the accessibility and comprehension of these concepts among students.Based on the available research, it can be concluded that teaching math with Roboblocky is a promising approach. Studies on using robotics to teach mathematics have shown that it can improve student engagement, achievement, problem-solving skills, and spatial reasoning. Additionally, the use of virtual robots in the classroom can promote positive attitudes towards learning among students

Evaluation of Storage Placement in Computing Continuum for a Robotic Application

This paper analyzes the timing performance of a persistent storage designed for distributed container-based architectures in industrial control applications. The timing performance analysis is conducted using an in-house simulator, which mirrors our testbed specifications. The storage ensures data availability and consistency even in presence of faults. The analysis considers four aspects: 1. placement strategy, 2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Comparison results also reveal that, while the method may underperform current centralized solutions in fault-free conditions, it outperforms the centralized solutions in failure scenario. Moreover, the used evaluation method is applicable for assessing other container-based critical applications with timing constraints that require persistent storage.

Experimental analysis for comparison of wireless transmission technologies: Wi-Fi, Bluetooth, ZigBee and LoRa for mobile multi-robot in hostile sites

This research paper conducts a thorough comparison of four prominent transmission technologies suitable for mobile robots operating in challenging environments. Emphasizing key factors such as signal strength, noise resistance, and data transfer efficiency, the study aims to identify the optimal communication solution in hostile conditions. The exploration delves into the intricacies of received signal strength indication (RSSI) and signal-to-noise ratio (SNR), revealing distinctive traits and trade-offs among the technologies. Navigating through the complexities of frequency bands, modulation types, and communication topologies, the paper examines the impact of obstacles, energy consumption dynamics, and potential real-world applications. Beyond contributing to the fields of robotics and communication, the study offers practical insights for stakeholders seeking resilient and efficient transmission methods for mobile robotic applications. Advocating for long range (LoRa) as the preferred transmission technology in hostile environments, the paper highlights its unmatched immunity to noise, stability, and minimal energy consumption. These findings provide valuable guidance for technology choices in collaborative mobile robot operations under challenging conditions. This research sets the stage for future developments in robotic communication, underscoring the crucial role of selecting the right transmission means for mission-critical applications in hostile environments.