Robotic Applications Research Articles

Design and Manufacture of Multifunctional 3-D Smart Skins with Embedded Sensor Networks for Robotic Applications.

An investigation was performed to develop a process to design and manufacture a 3-D smart skin with an embedded network of distributed sensors for non-developable (or doubly curved) surfaces. A smart skin is the sensing component of a smart structure, allowing such structures to gather data from their surrounding environments to make control and maintenance decisions. Such smart skins are desired across a wide variety of domains, particularly for those devices where their surfaces require high sensitivity to external loads or environmental changes such as human-assisting robots, medical devices, wearable health components, etc. However, the fabrication and deployment of a network of distributed sensors on non-developable surfaces faces steep challenges. These challenges include the conformal coverage of a target object without causing prohibitive stresses in the sensor interconnects and ensuring positional accuracy in the skin sensor deployment positions, as well as packaging challenges resulting from the thin, flexible form factor of the skin. In this study, novel and streamlined processes for making such 3-D smart skins were developed from the initial sensor network design to the final integrated skin assembly. Specifically, the process involved the design of the network itself (for which a physical simulation-based optimization was developed), the deployment of the network to a targeted 3D surface (for which a specialized tool was designed and implemented), and the assembly of the final skin (for which a novel process based on dip coating was developed and implemented.).

Effect of STEM Training with Educational Robotics Applications Designed for Classroom Teachers on the STEM Awareness and Attitudes of Teachers

Effect of STEM Training with Educational Robotics Applications Designed for Classroom Teachers on the STEM Awareness and Attitudes of Teachers

Development and validation of a survey instrument towards attitude, knowledge, and application of educational robotics (AKAER)

ABSTRACT Educational robotics activities have proven instrumental in creating a dynamic learning environment that provides students with hands-on experiences, aligning with the interdisciplinary principles advocated by STEM education. The integration of educational robotics into the educational process significantly impacts students of various age groups, fostering the acquisition and enhancement of diverse skills. However, the effective implementation of robotics hinges on teachers. This research aims to design a valid instrument (AKAER) to quantitatively capture teachers’ attitudes, knowledge, and application of educational robotics. The six-phase development process, encompassing design, construction, quantitative evaluation, and validation, ensures the precision of the instrument. Exploratory Factor Analysis (EFA) on data from 307 preschool and primary school teachers extracts a 49-item, five-factor model. Confirmatory Factor Analysis (CFA) cross-validates the model, yielding satisfactory fits (CMIN/DF = 2.145, RMR = .60, PNFI = .683, PCFI = .784, CFI = .827, RMSEA = .061, SRMR = .0852). AKAER's reliability, assessed by Cronbach's Alpha coefficient (α = .929), indicates high internal consistency. Overall, these results affirm the reliability and validity of the constructed instrument, confirming its suitability for data collection. This validation positions the instrument as a potential catalyst for advancing research understanding and stimulating educational innovations in the teaching and utilization of educational robotics.

Optical force sensor based on plasmon modulated upconversion luminescence.

We report a novel force sensor exploiting the interaction between plasmonic nanostructures and upconversion nanoparticles (UCNPs). The nanosensor is composed of a gold nanodisk and UCNPs separated by a flexible polymer layer. The gold nanodisk is designed to exhibit a plasmon resonance that selectively enhances one of the emission bands of the UCNPs while leaving the other ones largely unaffected. As the nanosensor is compressed or stretched by an external force, the polymer layer thickness changes, modulating the plasmon-UCNP coupling. The resulting changes in the luminescence intensity provides the basis for sensing. Furthermore, the nanosensor employs ratiometric sensing which makes it highly robust against any environmental variations. Our nanosensors exhibit two orders of magnitude higher responsivity than previously reported UCNP-based force sensors. They can be prepared as an on-chip sensor array or in a colloidal solution, making them suitable for a variety of applications in biology and robotics.

Gradients of properties increase the morphing and stiffening performance of bioinspired synthetic fin rays

State-of-the-art morphing materials are either very compliant to achieve large shape changes (flexible metamaterials, compliant mechanisms, hydrogels), or very stiff but with infinitesimal changes in shape that require large actuation forces (metallic or composite panels with piezoelectric actuation). Morphing efficiency and structural stiffness are therefore mutually exclusive properties in current engineering morphing materials, which limits the range of their applicability. Interestingly, natural fish fins do not contain muscles, yet they can morph to large amplitudes with minimal muscular actuation forces from the base while producing large hydrodynamic forces without collapsing. This sophisticated mechanical response has already inspired several synthetic fin rays with various applications. However, most ‘synthetic’ fin rays have only considered uniform properties and structures along the rays while in natural fin rays, gradients of properties are prominent. In this study, we designed, modeled, fabricated and tested synthetic fin rays with bioinspired gradients of properties. The rays were composed of two hemitrichs made of a stiff polymer, joined by a much softer core region made of elastomeric ligaments. Using combinations of experiments and nonlinear mechanical models, we found that gradients in both the core region and hemitrichs can increase the morphing and stiffening response of individual rays. Introducing a positive gradient of ligament density in the core region (the density of ligament increases towards the tip of the ray) decreased the actuation force required for morphing and increased overall flexural stiffness. Introducing a gradient of property in the hemitrichs, by tapering them, produced morphing deformations that were distributed over long distances along the length of the ray. These new insights on the interplay between material architecture and properties in nonlinear regimes of deformation can improve the designs of morphing structures that combine high morphing efficiency and high stiffness from external forces, with potential applications in aerospace or robotics.

A mechanically robust and facile shape morphing using tensile-induced buckling.

Inspired by the adaptive mechanisms observed in biological organisms, shape-morphing soft structures have emerged as promising platforms for many applications. In this study, we present a shape-morphing strategy to overcome existing limitations of the intricate fabrication process and the lack of mechanical robustness against mechanical perturbations. Our method uses tensile-induced buckling, achieved by attaching restraining strips to a stretchable substrate. When the substrate is stretched, the stiffness mismatch between the restraining strips and the substrate, and the Poisson's effect on the substrate cause the restraining strips to buckle, thereby transforming initially flat shapes into intricate three-dimensional (3D) configurations. Guided by an inverse design method, we demonstrate the capability to achieve complicated and diverse 3D shapes. Leveraging shape morphing, we further develop soft grippers exhibiting outstanding universality, high grasping efficiencies, and exceptional durability. Our proposed shape-morphing strategy is scalable and material-independent, holding notable potential for applications in soft robotics, haptics, and biomedical devices.

Biomimetic Mechanically Robust Chiroptical Hydrogel Enabled by Hierarchical Bouligand Structure Engineering.

Natural bouligand structures enable crustacean exoskeletons and fruits to strike a combination of exceptional mechanical robustness and brilliant chiroptical properties owing to multiscale structural hierarchy. However, integrating such a high strength-stiffness-toughness combination and photonic functionalities into synthetic hydrogels still remains a grand challenge. In this work, we report a simple yet general biomimetic strategy to construct an ultrarobust chiroptical hydrogel by closely mimicking the natural bouligand structure at multilength scale. The hierarchical structural engineering of long-range ordered cellulose nanocrystals' bouligand structure, well-defined poly(vinyl alcohol) nanocrystalline domains, and dynamic interfacial interaction synergistically contributes to the integration of high strength (23.3 MPa), superior modulus (264 MPa), and high toughness (54.7 MJ m-3), as well as extraordinary impact resistance, which far exceed their natural counterparts and synthetic photonic hydrogels. More importantly, seamless chiroptical and solvent-responsive patterns with high resolution can also be scalably integrated into the hydrogel by localized manipulation of the photonic band, while maintaining good ionic conductivity. Such exceptional mechanical-photonic combination holds tremendous potential for applications in wearable sensors, encryption, displays, and soft robotics.

Formation mechanism and elimination method of joint defects in lattice structures printed by metal droplet deposition manufacturing

Benefiting from the dense inner microstructure and smooth bead-like surface morphology, metal droplet deposition manufacturing (MDDM) is advantageous in manufacturing small metallic lattice structures, which have great potential in advanced aerospace, energy, and robotics applications. However, the improper top-layer droplet distance leads to joint defects in the printed lattices and deteriorates the forming accuracy. Moreover, if the substrate temperature and inclined angle increase, joint defects may occur even if the top-layer droplets are at an ideal distance due to droplet oscillation. This work proposes a novel method to eliminate joint defects by re-placing the joint region droplets to an ideal center distance. To this end, the dynamics of top-layer droplets (impact, spread, recoil, and oscillation) were investigated by combining morphology analysis and high-speed photography (Ts = 673 K, θ = 45°). A theoretical model was established to obtain the parameter range of defect-free joints through a series of bifurcate lattice printing experiments. Then, the morphology and generation mechanism of cumulative joint defects in the lattices at higher temperatures and angles (Ts = 773 K, θ = 60°) were analyzed. A parameter window of joint morphology with different values of inclined angle, substrate temperature, and top-layer droplet distance was established for predicting potential defects in the printing trace. Next, a novel method was proposed to remove redundant droplets and re-place top-layer droplet coordinates into the ideal center distance. Furthermore, the printing strategy was expanded to multi-truss lattices using three-dimensional transformation matrixes. Finally, multiple cells bifurcate, triangular, and pyramid lattices were successfully printed with perfect joints. The height deviation of joint regions is <5 % of the droplet diameter, verifying the feasibility of the proposed strategy. This work represents a further step towards MDDM printing of high-quality truss lattice structures and other complex 3D architectures.

Rigidity‐Tunable Materials for Soft Engineering Systems

Engineering systems that leverage the flexibility and softness of soft materials have been fostering revolutionary progress and broad interest across various applications. The inherently flexible mechanical properties of these materials lay the groundwork for engineering systems that can adapt comparably to biological organisms, enabling them to adjust to unpredictable environments effectively. However, alongside the positive benefits of softness, these systems face challenges such as low durability, continuous energy demands, and compromised task performance due to the inherently low stiffness of soft materials. These limitations pose significant obstacles to the practical impact of soft engineering systems in the real world beyond innovative concepts. This review presents a strategy that employs materials with variable stiffness to balance adaptability advantages with the challenge of low rigidity. The developments are summarized in materials capable of stiffness modulation alongside their applications in electronics, robotics, and biomedical fields. This focus on stiffness modulation at the material unit level is a critical step toward enabling the practical application of soft engineering systems in real‐world scenarios.

PrivShieldROS: An Extended Robot Operating System Integrating Ethereum and Interplanetary File System for Enhanced Sensor Data Privacy.

With the application of robotics in security monitoring, medical care, image analysis, and other high-privacy fields, vision sensor data in robotic operating systems (ROS) faces the challenge of enhancing secure storage and transmission. Recently, it has been proposed that the distributed advantages of blockchain be taken advantage of to improve the security of data in ROS. Still, it has limitations such as high latency and large resource consumption. To address these issues, this paper introduces PrivShieldROS, an extended robotic operating system developed by InterPlanetary File System (IPFS), blockchain, and HybridABEnc to enhance the confidentiality and security of vision sensor data in ROS. The system takes advantage of the decentralized nature of IPFS to enhance data availability and robustness while combining HybridABEnc for fine-grained access control. In addition, it ensures the security and confidentiality of the data distribution mechanism by using blockchain technology to store data content identifiers (CID) persistently. Finally, the effectiveness of this system is verified by three experiments. Compared with the state-of-the-art blockchain-extended ROS, PrivShieldROS shows improvements in key metrics. This paper has been partly submitted to IROS 2024.

Single-Walled Carbon Nanotube-Reinforced PEDOT: PSS Hybrid Electrodes for High-Performance Ionic Electroactive Polymer Actuator

Ionic electroactive polymer (iEAP) actuators are recognized as exceptional candidates for artificial muscle development, with significant potential applications in bionic robotics, space exploration, and biomedical fields. Here, we developed a new iEAP actuator utilizing high-purity single-walled carbon nanotubes (SWCNTs)-reinforced poly(3, 4-ethylenedioxythiophene)/poly(4-styrenesulfonate) (PEDOT: PSS, PP) hybrid electrodes and a Nafion/EMIBF4 ion-exchange membrane via a straightforward and efficient spray printing technique. The SWCNT/PP actuator exhibits significantly enhanced electric conductivity (262.9 S/cm) and specific capacitance (22.5 mF/cm2), benefitting from the synergistic effect between SWCNTs and PP. These improvements far surpass those observed in activated carbon aerogel bucky-gel-electrode-based actuators. Furthermore, we evaluated the electroactive behaviors of the SWCNT/PP actuator under alternating square-wave voltages (1–3 V) and frequencies (0.01–100 Hz). The results reveal a substantial bending displacement of 6.44 mm and a high bending strain of 0.61% (at 3 V, 0.1 Hz), along with a long operating stability of up to 10,000 cycles (at 2 V, 1 Hz). This study introduces a straightforward and efficient spray printing technique for the successful preparation of iEAP actuators with superior electrochemical and electromechanical properties as intended, which hold promise as artificial muscles in the field of bionic robotics.

Recent Advances in Robotics and Intelligent Robots Applications

Robotics research has a unique allure for both academia and the industry due to its potential for groundbreaking innovation and real-world applications [...]

Vision-based robotic grasping using faster R-CNN–GRCNN dual-layer detection mechanism

Visual grasping technology plays a crucial role in various robotic applications, such as industrial automation, warehousing, and logistics. However, current visual grasping methods face limitations when applied in industrial scenarios. Focusing solely on the workspace where the grasping target is located restricts the camera’s ability to provide additional environmental information. On the other hand, monitoring the entire working area introduces irrelevant data and hinders accurate grasping pose estimation. In this paper, we propose a novel approach that combines a global camera and a depth camera to enable efficient target grasping. Specifically, we introduce a dual-layer detection mechanism based on Faster R-CNN–GRCNN. By enhancing the Faster R-CNN with attention mechanisms, we focus the global camera on the workpiece placement area and detect the target object within that region. When the robot receives the command to grasp the workpiece, the improved Faster R-CNN recognizes the workpiece and guides the robot towards the target location. Subsequently, the depth camera on the robot determines the grasping pose using Generative Residual Convolutional Neural Network and performs the grasping action. We validate the feasibility and effectiveness of our proposed framework through experiments involving collaborative assembly tasks using two robotic arms.

Solvent‐Independent 3D Printing of Organogels

AbstractOrganogels are polymer networks extended by a liquid organic phase, offering a wide range of properties due to the many combinations of polymer networks, solvents, and shapes achievable through 3D printing. However, current printing methods limit solvent choice and composition, which in turn limits organogels' properties, applications, and potential for innovation. As a solution, a method for solvent‐independent printing of 3D organogel structures is presented. In this method, the printing step is decoupled from the choice of solvent, allowing access to the full spectrum of solvent diversity, thereby significantly expanding the range of achievable properties in organogel structures. With no changes to the polymer network, the 3D geometry, or the printing methodology itself, the choice of solvent alone is shown to have an enormous impact on organogel properties. As demonstrated, it can modulate the thermo‐mechanical properties of the organogels, both shifting and extending their thermal stability range to span from ‐30 to over 100 °C. The choice of solvent can also transition the organogels from highly adhesive to extremely slippery. Finally, the method also improves the surface smoothness of prints. Such advances have potential applications in soft robotics, actuators, and sensors, and represent a versatile approach to expanding the functionality of 3D‐printed organogels.

Bending, electroadhesion and sensing performances of single compliant electrode dielectric elastomer actuator based on SEBS gel

Dielectric elastomer actuators (DEAs) have great potential for application in soft robotics due to their ability to undergo substantial deformations, rapid response times, and high energy density when subjected to external electrical stimuli. However, the application of DEAs in the field of soft grippers is limited by their restricted direct electro-bending capability, output force, self-sensing capacity, and pre-stretching requirement. In this study, we fabricated a single compliant electrode DEA (SCE-DEA) which was made by sandwiching a compliant electrode between two layers of poly(styrene-b-ethylene-co-butylene-b-styrene) (SEBS)/white mineral oil (WO) dielectric elastomer films. The SCE-SEBS/WO was demonstrated to have the capacities of bending, sensing, and electroadhesion (EA). The SCE-SEBS/WO can be used in various soft actuator modes, such as the unipolar EA actuator, soft gripper, and soft vibrator. The grabbing mechanism of SCE-SEBS/WO-based soft grippers with opposite bipolar configuration is caused by electric field induced bending of SEC-SEBS/WO and subsequent electrostatic attraction between both SEC-SEBS/WO. The SEC-SEBS/WO based soft grippers have rapid response detachment, and ability to grasp various types of objects. Three-layer stacked SCE-SEBS/WO-60 (with 60 wt% of WO) exhibited 531 mN cm−2 of EA stress on the paper at 3.0 kV applied voltage, and the soft gripper made by four SCE-SEBS/WO-60 can successfully grab wood blocks weighing 162.4 g at 5 kV applied voltage. The sensing capacity of SCE-SEBS/WO based soft gripper was based on the bending strain dependent resistance changes of the compliant electrode. Our results provide new insights into the fabrication of DEA based soft grippers.

Design and Implementation Accurate Three Distance Sensors Device Using Neural Network

This paper suggested and created a proposed device for sensing an accurate distance from 5 to 400 cm with three sensors using the proposed on-line Feedforward Backpropagation Neural Network (FFBPN). Firstly, preparing a dataset (distances) with two distances utilizing Arduino and measured distance calculated from three sensors (2 ultrasonic and 1 IR). Secondly, target distances are calculated from the ground manually using a distance tape tool. Thirdly, interfacing Arduino and MATLAB using USB easily saved datasets from Arduino directly to MATLAB, then training, and testing data. The proposed device divides reading distances into three distances, getting from three sensors to get accurate distances. The first reading was 5-10 cm from down ultrasonic sensor 3. The second reading was 10-80 cm from middle IR sensor 2. The third reading was 80-400 cm from up ultrasonic sensor 1. The dataset was created and improved using the suggested FFBPN to create a very accurate distance gadget with three sensors. A suggested FFBPN consisted of 3 layers: The first input measured distance layer (from 3 sensors), the second hidden layer with ten neurons, and the third output layer distance. The numerical results showed that the proposed distance device was significantly accurate due to regression result R=1 using the proposed Neural Network (NN), which meanings that the device had 100% fitting. It had the best validation performance in 464 epochs, i.e., is 0.0036122. Furthermore, the proposed on-line FFBPN was fast because the training time equals three seconds. This distance device was implemented for use in robotic and radar applications to detect objects accurately because this device successfully detects near and far objects.

Sequential Optimal Trajectory Planning Scheme for Robotic Manipulators along Specified Path Based on Direct Collocation Method

Robotic manipulators play a pivotal role in modern intelligent manufacturing and unmanned production systems, often tasked with executing specific paths accurately. However, the input of the robotic manipulators is trajectory which is a path with time information. The resulting core technology is trajectory planning methods which are broadly classified into two categories: maximum velocity curve (MVC) methods and multiphase direct collocation (MPDC) methods. This paper concentrates on addressing challenges associated with the latter methods. In MPDC methods, the solving efficiency and accuracy are greatly influenced by the number of discretization nodes. When dealing with systems with complex dynamics, such as robotic manipulators, striking a balance between solving time and path discretization errors becomes crucial. We use a mesh refinement (MR) algorithm to find a suitable number of nodes under the premise of ensuring the path discretization error. So, the actual device can effectively implement the planned solutions. Nonetheless, the conventional application of the MR algorithm requires solving the original problem in each iteration; these processes are extremely time-consuming and may fail to solve when dealing with a complex dynamic system. As a result, we propose a sequential optimal trajectory planning scheme to solve the problem efficiently by dividing the original optimal control (OC) problem into two stages: path planning (PP) and trajectory planning (TP). In the PP stage, we employ a DC method based on arc length and an MR algorithm to identify key nodes along the specified path. This aims to minimize the approximation error introduced during discretization. In the TP stage, the identified key nodes serve as boundary conditions for an MPDC method based on time. This facilitates the generation of an optimal trajectory that maximizes motion performance, considering constant velocity in Cartesian space and dynamic constraints while keeping the path discretization error. Simulation and experiment are conducted with a six-axis robotic manipulator, ROCR6, and show significant potential for a wide range of applications in robotics.

Hourglass cascaded recurrent stereo matching network

Stereo matching acts a crucial role in computer vision and robotics applications. An accurate cost volume and robust disparity regression method are essential for stereo matching of high accuracy. Following GCNet and PSMNet, constructing 4D cost volume and then using the soft argmin method to regress has been dominated. However, it will encounter many difficulties due to the multi-modal distribution of cost volume. One of the reasons for this multi-modal distribution is the occlusion area which not be possible to find a matching region on the reference image and rarely discussed. In this paper, we propose to use global context information could improve the performance of model in occluded regions. Recently, novel recurrent neural network regression methods are proposed, but most of them regress disparity maps from 3D cost volume. In this paper, we propose the new combinatorial paradigm that combine stacked hourglass modules and recurrent neural networks to further aggregate 4D cost volume and regress disparity respectively. The proposed method can be seamlessly integrated into most stereo matching networks, we improved the accuracy by 45% for PSMNet and 38% for GwcNet in our experiment. Experimental results on Scene Flow, KITTI2012, KITTI2015, and ETH3D datasets show our method is competitive. The code is available at: https://github.com/truman1211/HCRnet.

Water‐Assisted Reprocessing and Shape Programming of Epoxy Vitrimer

AbstractVitrimers are reprocessing and recycling thermosetting plastics. They possess reconfigurable polymer networks that allow for unlimited transformation in shape in principle. However, current strategies to reshape the vitrimers typically involve heat or light, which often induces undesirable oxidation and decomposition. To address this issue, here a water‐assisted approach is proposed for programming epoxy vitrimers’ shapes. In this design, water molecules are utilized to reversibly dissociate the hydrogen bonds in epoxy vitrimers to enable the polymer segments to move flexibly. The hydrated epoxy vitrimers can then be reprogrammed and retained into different temporary shapes by removing the water. Such samples would be recovered to their original shapes by rehydration, exhibiting water‐induced shape memory property. More than temporary deformation, the permanent figures of the hydrated vitrimers can also be permanently changed at room temperature (rt) or elevated temperatures in the presence of transesterification catalysts. Combing the shape memory and high temperature plasticity or utilizing rt plasticity, sophisticated shapes such as spiral shapes are demonstrated. It is envisioned that this water‐assisted methodology can be useful in programming cross‐linked polymers into diverse 3D structures, which has wide practical applications in soft robots, deployable devices, aerospace materials, etc.

Crystallization-Inspired Design and Modeling of Self-Assembly Lattice-Formation Swarm Robotics.

Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this paper introduces a distributed self-assembly formation strategy by defining free, moving, growing, and solid states for robots. Robots in these states can spontaneously organize into user-specified two-dimensional shape formations with lattice structures through local interactions and communications. To address the challenges posed by complex spatial structures in modeling a macroscopic model, this work introduces the structural features estimation method. Subsequently, a corresponding non-spatial macroscopic model is developed to predict and analyze the self-assembly behavior, employing the proposed estimation method and a stock and flow diagram. Real-robot experiments and simulations validate the flexibility, scalability, and high efficiency of the proposed self-assembly formation strategy. Moreover, extensive experimental and simulation results demonstrate the model's accuracy in predicting the self-assembly process under different conditions. Model-based analysis indicates that the proposed self-assembly formation strategy can fully utilize the performance of individual robots and exhibits strong self-stability.