Robotic Actuators Research Articles

Piezoelectric approaches to organic polymeric materials

AbstractPiezoelectricity refers to the ability of certain materials to generate electric charges when subjected to mechanical stress or strain, and vice versa. This phenomenon has been widely studied in inorganic materials, such as quartz and ceramics, and has found numerous applications in sensing, actuation, and energy harvesting. There has been a growing interest in piezoelectricity for polymeric materials that are lightweight, flexible, and highly processable for a wide range of applications, including sensors for environmental and biomedical monitoring, energy conversion from mechanical vibrations, and actuation in soft robotics. However, characterizing the piezoelectric tendency of polymeric materials tends to be a complex and challenging process, as the effect is often weak and dependent upon various factors including material structure, processing conditions, and measurement setup. While many past and current review articles have covered theories and principles associated with piezoelectric polymers to some extent, detailed information on the corresponding experimental techniques used to measure their piezoelectric properties remains limited. This mini‐review paper aims to consolidate and summarize various approaches, including quasi‐static methods, optical interferometry, dielectric spectroscopy, and piezoelectric force microscopy, and to provide insights into the principles, advantages, limitations, and challenges associated with these techniques. © 2023 Society of Industrial Chemistry.

Light-Responsive Actuator of Azobenzene-Containing Main-Chain Liquid Crystal Elastomers with Allyl Sulfide Dynamic Exchangeable Linkages.

Herein, a light-responsive and light-induced bond-exchange-reaction (BER)-capable actuator of the monodomain liquid crystal elastomer (xMLCEazo), developed using main-chain mesogenic oligomers containing azobenzene and allyl sulfide linkages, is investigated. Large quantities of the azobenzene and allyl dithiol linkages are incorporated into the main-chain mesogenic oligomer prepared via thiol-acrylate Michael addition polymerization (TAMAP). The xMLCEazo film is generated via visible-light-induced BER of the drawn polydomain xLCEazo (xPLCEazo) film prepared via TAMAP of tetrathiol cross-linkers and diacrylate-terminated mesogenic oligomers. The xMLCEazo film exhibits large length actuation (38%) through the photothermal effect, along with excellent self-healing and reprogramming properties, under ultraviolet (UV) light irradiation. UV light induced BER of the xMLCEazo film is used to develop complex-shaped actuators with a bilayer film, containing the xMLCEazo and xPLCEazo films, which are bonded by the UV light induced BER without glue. The individual arm of the complex eight-arm flower is remotely actuated under UV light irradiation, and a circular band is rolled under blue laser light irradiation, demonstrating the local remote-controlled actuation and fuel-free motion of the motile soft robot using light irradiation, respectively. Thus, the xMLCEazo film can be expanded to other interesting applications requiring reprogrammable, self-healing, reprocessable, patternable, and remote-controlled light-triggered elastic, rubber-like actuators.

Physics-informed and black-box Identification of robotic actuator with a flexible joint

In robotics, precise models are critical for ensuring safety and functionality. However, acquiring a precise model characterizing a system’s dynamics can be challenging. One of the alternatives to address this issue is system Identification, which aims to obtain models through physical and experimental observations. In this manner, the developments in machine learning algorithms, such as neural networks, have significantly improved the modeling of complex and nonlinear phenomena. In this work, a mass-spring-damper (MSD) system and a low-cost original elastomer-based Series Elastic Actuators (eSEA) assembly are used to evaluate the performance of system Identification models. The black-box models selected are variations of the AutoRegressive Moving Average with eXogenous input (ARMAX) algorithm. The gray-box model aims to estimate the parameters of 4 friction models; the optimization is done utilizing physics-informed neural networks (PINNs). For both case studies, the PINNs outperformed the black-box models. In the didactic example, the parameters obtained are close to the ground truth, and the highest determinant coefficient obtained is 0.99. The friction model that best represents the robotic actuator is the LuGre model, with the parameters obtained using the PINNs, outperforming the best black-box model by lowering the mean absolute error (MAE) by 30.83%. With a determinant coefficient of 0.94, the model shows a high capacity for describing the multiple nonlinearities present in the system.

Experimental investigation into mechanical, thermal, and shape memory behavior of thermoresponsive PU/MXene shape memory polymer nanocomposite

This research presents an experimental investigation into the mechanical, thermal, and shape memory behavior of a thermos-responsive polyurethane (PU) reinforced with 0–1.0 wt % of MXene (Ti3C2Tx) nanofiller. The PU/MXene nanocomposites were fabricated using sonication and injection molding route. The 0.5 wt % PU/MXene nanocomposite showed the optimum mechanical properties i.e. tensile modulus, tensile strength, and hardness value, which are improved by 22, 281, and 19 %, respectively, compared to pure PU. The improvement is observed in melting temperature (Tm), the heat of melting (hm), crystallization temperature (Tc), and the heat of crystallization (hc) results. The percentage of crystallinity revealed enhancements of 6 %, 18 %, 24 %, and 34 % for 0.1, 0.2, 0.3, and 0.5 wt % PU/MXene samples respectively compared to pure PU. The findings from the shape recovery experiments demonstrated that the inclusion of MXene has no impact on both the shape fixity and shape recovery performance. The PU/MXene nanocomposite with improved mechanical and thermal properties can find potential applications in robotics actuators, medical devices, sensors, etc.

Human induced pluripotent stem cell-derived cardiac muscle rings for biohybrid self-beating actuator.

Cardiac muscle, a subtype of striated muscle composing our heart, has garnered attention as a source of autonomously driven actuators due to its inherent capability for spontaneous contraction. However, conventional cardiac biohybrid robots have utilized planar (2D) cardiac tissue consisting of a thin monolayer of cardiac myotubes with a thickness of 3-5 μm, which can generate a limited contractile force per unit footprint. In this study, 3D cardiac muscle rings were proposed as robotic actuator units. These units not only exhibit higher contractile force per unit footprint compared to their 2D counterparts due to their increased height, but they can also be integrated into desired 3D configurations. We fabricated cardiac muscle rings from human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), evaluated their driving characteristics, and verified the actuation effects by integrating them with artificial components. After the 10th day from culture, the cardiac muscle rings exhibited rhythmic spontaneous contraction and increased contractile force in response to stretching stimuli. Furthermore, after constructing a centimeter-sized biohybrid self-beating actuator with an antagonistic pair structure of cardiac muscle rings, the periodic antagonistic beating motion at its tail portion was confirmed. We believe that 3D cardiac muscle rings, possessing high contractile force and capable of being positioned within limited 3D space, can be used as potent biohybrid robotic actuators.

Serially Coupled Self-Excited Pneumatic Actuator for Environment-Adaptive Steering Robot

Serially Coupled Self-Excited Pneumatic Actuator for Environment-Adaptive Steering Robot

Silicone-layered waterproof electrohydraulic soft actuators for bio-inspired underwater robots.

Electrohydraulic soft actuators are a promising soft actuation technology for constructing bio-inspired underwater robots owing to the features of this technology such as large deformations and forces, fast responses, and high electromechanical efficiencies. However, this actuation technology requires high voltages, thereby limiting the use of these actuators in water and hindering the development of underwater robots. This paper describes a method for creating bio-inspired underwater robots using silicone-layered electrohydraulic soft actuators. The silicone layer functions as an insulator, enabling the application of high voltages underwater. Moreover, bending and linear actuation can be achieved by applying the silicone layers on one or both sides of the actuator. As a proof of concept, bending and linear actuators with planar dimensions of 20 mm × 40 mm (length × width) are fabricated and characterized. Underwater actuation is observed in both types of actuators. The bending actuators exhibit a bending angle and blocked force of 39.0° and 9.6 mN, respectively, at an applied voltage of 10 kV. Further, the linear actuators show a contraction strain and blocked force of 6.6% and 956.1 mN, respectively, at an applied voltage of 10 kV. These actuators are tested at a depth near the surface of water. This ensured that they can operate at least at that depth. The actuators are subsequently used to implement various soft robotic devices such as a ray robot, a fish robot, a water-surface sliding robot, and a gripper. All of the robots exhibit movements as expected; up to 31.2 mm/s (0.91 body length/s) of locomotion speed is achieved by the swimming robots and a retrieve and place task is performed by the gripper. The results obtained in this study indicate the successful implementation of the actuator concept and its high potential for constructing bio-inspired underwater robots and soft robotics applications.

Achieving controllable and reversible snap-through in pre-strained strips of liquid crystalline elastomers.

Deformable, elastic materials that buckle in response to external stimuli can display "snap-through", which involves a transition between different, stable buckled states. Snap-through produces a quick release of stored potential energy, and thus can provide fast actuation for soft robots and other flexible devices. Liquid crystalline elastomers (LCEs) exposed to light undergo a phase transition and a concomitant mechanical deformation, allowing control of snap-through for rapid, large amplitude actuation. Using both a semi-analytical model and finite element simulations, we focus on a thin LCE strip that is clamped at both ends and buckles due to an initially imposed strain. We show that when this clamped, strained sample is exposed to light, it produces controllable snap-through behavior, which can be regulated by varying the light intensity and the area of the sample targeted by light. In particular, this snap-through can be triggered in different directions, allowing the system to be reset and triggered multiple times. Removing the light source will cause the system to settle into one of two stable states, enabling the encoding and storage of information in the system. We also highlight a specific case where removing the light source removes the induced buckling and returns the material to an initially flat state. In this case, the system can be reset and form a new shape, allowing it to function as a rewriteable haptic interface.

Task Accuracy Enhancement for a Surgical Macro-Micro Manipulator With Probabilistic Neural Networks and Uncertainty Minimization

Accurate robot kinematic modelling is a major component for autonomous robot control to guarantee safety and precision during task execution. In surgical robotics complex robotic structures and actuation mechanisms are generally employed, therefore machine learning techniques can be adopted to build the model of the robot. Probabilistic neural networks are a class of learning approaches that provide information about the uncertainty of the learnt models. In this work we compare two different probabilistic neural networks (Bayesian and Evidential Neural Networks) to model the kinematics of a surgical robotic instrument and propose a control strategy based on Hierarchical Quadratic Programming (HQP) capable of exploiting the model uncertainty to improve the accuracy and safety of the controller. Simulation and real world experiments on different autonomous path tracking tasks show that the model uncertainty highly affects the control performances and prove the effectiveness of the proposed controller in improving task execution. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The push towards reducing invasiveness and patient’s traumas in surgery has lead to the requirement of miniaturized and highly articulated robots. This however comes at the cost of having systems that are hard to model and control, which is one of the major limitations for autonomy in surgical robotics. Machine learning has become very effective in modelling complex systems and probabilistic approaches additionally allow estimating the confidence of the learnt model. In robotics field where high precision is required to perform an autonomous task, like in minimally invasive surgery, the robot model needs to be very accurate and controllers need to guarantee safety in performing the desired task, while satisfying additional motion constraints imposed by the application scenario. This work proposes the use of probabilistic neural networks to model the complexity of a surgical robotic instrument and a control strategy capable of ensuring safety by maximizing model’s confidence and guaranteeing satisfaction of imposed motion constraints. In this work a macro-micro manipulator setup is employed, consisting of an articulated surgical robotic instrument connected to a serial-link manipulator. The proposed modelling and control approaches can be used in any other field where controllers need to highly rely on the robot model due to limitations in using external sensors and where leveraging information about model’s confidence can be beneficial. Currently, the work focuses only on pure kinematic modelling and control, thus neglecting any possible interaction with the environment. Future work will focus on addressing this limitation in order to ensure proper force control and effective autonomy.

Автоматическая система позиционирования колёсных роботизированных технических средств, обслуживающих кормовой стол

The quality and availability of forage on the feed table increase cow productivity. Feed availability can be ensured by increasing the length of the feed table and using robotic tools. The use of a wheeled robotized device in feed distribution increases labour productivity and reduces the influence of the human factor on the feeding process. To improve the efficiency of the feed distribution process, the author has developed and tested an automatic positioning system and a robotic device for servicing the feed table. The article presents a mathematical description of the kinematic relationships of movement of wheeled robotic devices. The author has developed an algorithm of the automatic positioning system operation, which takes into account the algorithmic connection between the electric drive actuators of the driving wheels and the vision system, where the image is processed according to the intellectual algorithm of the feeding area recognition. The author has developed the principle of constructing the route travelled by the robotic device along the feed table. Tests have confirmed the conformity of output reactions of the robotic device actuators to the input commands of the automatic positioning system.

Neural Optimal Control for Constrained Visual Servoing via Learning From Demonstration

This paper proposes a novel optimal control scheme for constrained image based visual servoing of a robot manipulator. For a robot manipulator with an eye-on-hand configuration, visibility constraint is an essential requirement to avoid servo failure, while robot’s actuator limits must also be satisfied. To ensure this, the constraints are modelled implicitly via learning the task and defining safe regions using expert human demonstrations via mixture of Dynamic Movement Primitives (DMPs). The visual servoing problem is then formulated as a closed-loop optimal control problem using these constraint model where a desired target (possibly time-varying) is obtained by acting upon the feedback from the real-time visual sensors. The visual servo control loop consists of a single network adaptive critic optimal tracking control scheme whose weights are tuned using Lyapunov stability criteria. The stability and the performance of the proposed control scheme is shown theoretically via Lyapunov approach and also verified experimentally using a seven degree of freedom (DOF) Franka Emika and six DOF Universal Robot (UR) 10 manipulator. The approach is also demonstrated on a use case scenarios in mock-up convenience store and warehouse setup. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The applications of robots in busy warehouses, healthcare sectors and convenience store, has a big societal impact. The scenarios in these real-world problems often consist of a dynamic environment. Therefore, sensor-based localization and planning, along with satisfying robot and task constraints are needed. These naturally adds up to the programming costs in addition to the robot platform and actuation. However, modern robots need intuitive and easy programming for more pervasive in a practical societal application. In our proposed method, this sensor-based planning with environmental constraints is modelled implicitly using the Programming by Demonstration framework. The user needs to catch the robot arm by his hand and teach the task at hand, and the framework captures the task and robot constraints while generalizing to new goals. This modelling, along with cost optimal controller, generates real-time constraint aware robot manipulation trajectories for the demonstrated task in dynamic environments.

Integrated Design and Fabrication of Pneumatic Soft Robot Actuators in a Single Casting Step.

Bio-inspired soft robots have already shown the ability to handle uncertainty and adapt to unstructured environments. However, their availability is partially restricted by time-consuming, costly, and highly supervised design-fabrication processes, often based on resource-intensive iterative workflows. Here, we propose an integrated approach targeting the design and fabrication of pneumatic soft actuators in a single casting step. Molds and sacrificial water-soluble hollow cores are printed using fused filament fabrication. A heated water circuit accelerates the dissolution of the core's material and guarantees its complete removal from the actuator walls, while the actuator's mechanical operability is defined through finite element analysis. This enables the fabrication of actuators with non-uniform cross-sections under minimal supervision, thereby reducing the number of iterations necessary during the design and fabrication processes. Three actuators capable of bending and linear motion were designed, fabricated, integrated, and demonstrated as 3 different bio-inspired soft robots, an earthworm-inspired robot, a 4-legged robot, and a robotic gripper. We demonstrate the availability, versatility, and effectiveness of the proposed methods, contributing to accelerating the design and fabrication of soft robots. This study represents a step toward increasing the accessibility of soft robots to people at a lower cost.

An Ultrawide Range, Highly Stretchable Liquid Metal Force and Strain Sensor Based on Spiral Multilayer Microfluidic Fibers

Presently, a variety of soft sensing technologies are available, but there is still an opportunity to research on flexible sensors that can withstand high loads. Here, we report a highly stretchable, wide measuring range, and easy-to-fabricate liquid metal sensor capable of detecting force and strain modes. The key enabling design feature of the proposed sensor is the spiral winding combination of two different types of stretchable liquid metal microfluidic fibers. Pressing or elongating the fibers changes the geometry and, thus, the electrical resistance in a predictable way. The proposed sensor can offer a simple mechanism to measure force up to about 400N and strain up to about 200% under limited laboratory conditions. The sensor signal is repeatable in both cases. The ability of our sensor in wide measuring range in actual application is verified via detecting the loads of one human body and driving car wheel. We further show a proof of concept of the soft sensor by a soft robotic actuator, enabling the identification of bending angles and object surface features.

Identification of the friction model of a single elastic robot actuator from video

Obtaining a highly accurate dynamic model from video data is a challenging task within system Identification. More specifically, in robotics, there is a constant need for precise models to optimize the performance and safety of these systems. With the developments in machine learning, algorithms such as convolutional neural networks (CNNs) have shown good performances in extracting features from images. Techniques like transfer learning (TL) can lower the computational cost and optimize the training task. This work aims to make the gray-box Identification of friction parameters of an actuator from video. The proposed approach is composed of three steps. Firstly, it compares the transfer learning performance of three pre-trained CNNs to estimate the pose of the motor and the link from video data. Secondly, a long-term short-term (LSTM) network is added to estimate the velocity and acceleration. Thirdly, the parameters of the two friction models are estimated using the estimated states and the dynamic equations of the system. Through these steps, a vision-based gray-box Identification is obtained. The approach is tested using an original elastomer-based series of elastic actuators (eSEA) benchmark. The results show that the VGG19 model combined with the LSTM layer is the optimal pre-trained CNN for the vision module, obtaining a coefficient of determination higher than 0.96 for all three states. From the state estimates, the parameters of the friction models are optimized. The best-performing friction model is the Coulomb-Stribeck, which decreases the mean absolute error (MAE) by 15.50% compared to the viscous linear model.

Consequent pole flux modulated linear actuator under winding chang and field oriented control driving conditions for long track and multi-track agricultural robot

The electromagnetic performance improvement of a double saliency (DS) flux modulated (FM)- permanent magnet linear synchronous motors (PMLSM) for agricultural robots is proposed in this work by minimizing the effect of cross-coupling. To understand the effect of the cross-coupling on the electromagnetic performance, a dq axes model of the DS FM-PMLSM is developed and analyzed under different driving conditions. Based on the result obtained, winding modification schemes are proposed to minimize the thrust ripple and improve the zero torque control. The modified models are studied using finite element analysis (FEA) and the model with the best performance is selected for prototyping. The prototype is then tested and the results are validated against the electromagnetic performance obtained using the FEA. This research targeted the development of a low-cost linear actuator for a sapling grower gantry robot that can be used by farmers around the world.

Design of a Hybrid Adaptive Controller for Series Elastic Actuators of Robots

Design of a Hybrid Adaptive Controller for Series Elastic Actuators of Robots

Soft actuators in surgical robotics: a state-of-the-art review

Soft actuators in surgical robotics: a state-of-the-art review

ROSES, the only RObotic System for any Endovascular Surgery, Including the Control of an Animated Catheter Characterized by the Presence of two Controlled Curvatures

The paper presents ROSES, its robotic components, the different devices, not necessarily sterile, and its disposables, suitable for any endovascular procedure, both actually performed and presently not assisted by any robotic system, and open in the future for new application yet to come, such as what will be allowed by the new animated catheter. In fact, this is due to the mechanical configuration of the robotic actuators based on a peculiar gear train which presents a big passage hole which allows both the passage of big catheters and even hemostasis valves, as well as full control of very small catheters and guide wires. The system measures forces opposed by the body showing their value both numerically and analogically without the need of any. special tool, measures length of penetration of each catheter and guide wires recording their value. Thus, it may become in future, connected to a work station that will register in real time also the fluoroscopic images, a kind of black box of endovascular surgeries, separating completely doctor and nurses from the patient, using also cameras and microphones to replace the physical contact with the patient.

ToRoS: A Topology Optimization Approach for Designing Robotic Skins

Soft robotics offers unique advantages in manipulating fragile or deformable objects, human-robot interaction, and exploring inaccessible terrain. However, designing soft robots that produce large, targeted deformations is challenging. In this paper, we propose a new methodology for designing soft robots that combines optimization-based design with a simple and cost-efficient manufacturing process. Our approach is centered around the concept of robotic skins---thin fabrics with 3D-printed reinforcement patterns that augment and control plain silicone actuators. By decoupling shape control and actuation, our approach enables a simpler and cost-efficient manufacturing process. Unlike previous methods that rely on empirical design heuristics for generating desired deformations, our approach automatically discovers complex reinforcement patterns without any need for domain knowledge or human intervention. This is achieved by casting reinforcement design as a nonlinear constrained optimization problem and using a novel, three-field topology optimization approach tailored to fabrics with 3D-printed reinforcements. We demonstrate the potential of our approach by designing soft robotic actuators capable of various motions such as bending, contraction, twist, and combinations thereof. We also demonstrate applications of our robotic skins to robotic grasping with a soft three-finger gripper and locomotion tasks for a soft quadrupedal robot.

Cognitive Robotics: Integrating Artificial Intelligence and Embodied Intelligence for Advanced Problem Solving

Artificial intelligence (AI) and embodied intelligence are combined in cognitive robotics, which marks a paradigm change in highly developed problem-solving skills. This study offers a comprehensive review of the theoretical underpinnings, practical difficulties, practical applications, and moral implications of cognitive robots. The study explores machine learning methods, natural language processing tools, and computer vision technologies after introducing the fundamental concepts. It clarifies embodied intelligence further by emphasizing the important roles that sensory data fusion, robotic actuation, and adaptive behaviours play in the cognitive capabilities of robots. The study then moves on to discuss cognitive robotics' integration difficulties, including issues in sensory data fusion, cognitive dissonance in AI-driven robotics, and human-robot interaction. Applications in autonomous cars, healthcare, manufacturing, and assistive technologies demonstrate how cognitive robotics is transforming many different industries. The ethical obligations involved in the creation and use of cognitive robotic systems are highlighted, along with ethical issues relating to employment, privacy, and ethical AI practises. In-depth analysis is done of the present and foreseeable difficulties in cognitive robotics. Along with cognitive difficulties like contextual comprehension and moral conundrums, technical obstacles linked to sensor limits and processing restrictions are also studied. The study highlights cutting-edge research projects and multidisciplinary partnerships that provide answers to these problems, paving the path for the development of cognitive robots in the future. The article envisions a future where cognitive robots would play a part in space exploration, disaster response, and personalised robotics. It also surveys integration possibilities with cutting-edge technologies like augmented reality and the Internet of Things. The study summarises the key findings and presents the implications for future research, and emphasises the important part cognitive robotics plays in resolving difficult issues. To ensure that cognitive robots continue to have an impact on a future that is smarter, more connected, and understanding, ethical issues, multidisciplinary collaboration, and responsible development practises are necessary.