Soft robotics最新文献

The insect-computer hybrid soft robots are receiving increasing attention due to their excellent motor capabilities, small size, and low power consumption. However, the effective control of insects is limited to minutes since the response from insects is reduced as the number of stimulus signal increase. This phenomenon is known as habituation, which causes the loss of control of robots and hinders their application in practical tasks such as search and rescue missions that require several hours. It has been shown that constantly switching the pattern of stimulus signals can slow down the onset of habituation. Moreover, when habituation occurs, applying a different stimulus signal can break the habituation. Based on this, we have designed a navigation algorithm that can extend the control time of insects to several hours. The algorithm is composed of a stimulation decision-making core responsible for deciding on the type of stimulus signal (left, right, acceleration), a stimulation parameters adjustment (SPA) core responsible for adjusting the stimulus signal voltage constantly to delay the occurrence of habituation, and a reactivation function (RF), as a different stimulus signal from the normal stimulus signal, is used to break insects' habituation to the normal stimulus signal. Experiments have shown that our SPA regulator and RF can significantly extend the control time of insects. Navigation experiments demonstrating effective control of the insects for up to 3 h verified the effectiveness of the navigation algorithm, which strikes a balance between control accuracy and control time.

Organisms can adapt to various complex environments by obtaining optimal morphologies. Plant tendrils evolve an extraordinary and stable spiral morphology in the free-growing stage. By combining apical and asymmetrical growth strategies, the tendrils can adjust their morphology to wrap around and grab different supports. This phenomenon of changing tendril morphology through the movement of growth inspires a thoughtful consideration of the laws of growth that underlie it. In this study, tendril growth is modeled based on the Kirchhoff rod theory to obtain the exact morphological equations. Based on this, the movement patterns of the tendrils are investigated under different growth strategies. It is shown that the self-interference phenomenon appears as the tendril grows, allowing it to hold onto its support more firmly. In addition, a finite element model is constructed using continuum media mechanics and following the finite growth theory to simulate tendril growth. The growth morphology and self-interference phenomenon of tendrils are observed visually. Furthermore, an innovative class of fluid elastic actuators is designed to verify the growth phenomena of tendrils, which can realize the wrapping and locking functions. Several experiments are conducted to measure the end output force and the smallest size that can be clamped, and the output efficiency of the elastic actuator and the optimal working pressure are verified. The results presented in this study could reveal the formation law of free tendril spiral morphology and provide an inspiring idea for the programmability and motion control of bionic soft robots, with promising applications in the fields of underwater rescue and underwater picking.

Bio-syncretic robots consisting of artificial structures and living muscle cells have attracted much attention owing to their potential advantages, such as high drive efficiency, miniaturization, and compatibility. Motion controllability, as an important factor related to the main performance of bio-syncretic robots, has been explored in numerous studies. However, most of the existing bio-syncretic robots still face challenges related to the further development of steerable kinematic dexterity. In this study, a bionic optimized biped fully soft bio-syncretic robot actuated by two muscle tissues and steered with a direction-controllable electric field generated by external circularly distributed multiple electrodes has been developed. The developed bio-syncretic robot could realize wirelessly steerable motion and effective transportation of microparticle cargo on artificial polystyrene and biological pork tripe surfaces. This study may provide an effective strategy for the development of bio-syncretic robots and other related studies, such as nonliving soft robot design and muscle tissue engineering.

Numerous recent research efforts have leveraged networks of rigid struts and flexible cables, called tensegrity structures, to create highly resilient and packable mobile robots. However, the locomotion of existing tensegrity robots is limited in terms of both speed and number of distinct locomotion modes, restricting the environments that a robot is capable of exploring. In this study, we present a tensegrity robot inspired by the volumetric expansion of Tetraodontidae. The robot, referred to herein as Spikebot, employs pneumatically actuated rigid struts to expand its global structure and produce diverse gaits. Spikebot is composed of linear actuators that dually serve as rigid struts linked by elastic cables for stability. The linearly actuating struts can selectively protrude to initiate thrust- and instability-driven locomotion primitives. Such motion primitives allow Spikebot to reliably locomote, achieving rolling, lifting, and jumping. To highlight Spikebot's potential for robotic exploration, we demonstrate how it achieves multi-dimensional locomotion over varied terrestrial conditions.

In recent years, ferrofluids have found increased popularity as a material for medical applications, such as ocular surgery, gastrointestinal surgery, and cancer treatment, among others. Ferrofluidic robots are multifunctional and scalable, exhibit fluid properties, and can be controlled remotely; thus, they are particularly advantageous for such medical tasks. Previously, ferrofluidic robot control has been achieved via the manipulation of handheld permanent magnets or in current-controlled electromagnetic fields resulting in two-dimensional position and shape control and three-dimensional (3D) coupled position-shape or position-only control. Control of ferrofluidic liquid droplet robots poses a unique challenge where model-based control has been shown to be computationally limiting. Thus, in this study, a model-free control method is chosen, and it is shown that the task of learning optimal control parameters for ferrofluidic robot control can be performed using machine learning. Particularly, we explore the use of Bayesian optimization to find optimal controller parameters for 3D pose control of a ferrofluid droplet: its centroid position, stretch direction, and stretch radius. We demonstrate that the position, stretch direction, and stretch radius of a ferrofluid droplet can be independently controlled in 3D with high accuracy and precision, using a simple control approach. Finally, we use ferrofluidic robots to perform pick-and-place, a lab-on-a-chip pH test, and electrical switching, in 3D settings. The purpose of this research is to expand the potential of ferrofluidic robots by introducing full pose control in 3D and to showcase the potential of this technology in the areas of microassembly, lab-on-a-chip, and electronics. The approach presented in this research can be used as a stepping-off point to incorporate ferrofluidic robots toward future research in these areas.

In this article, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots based on proprioceptive sensing feedback. Developments of 3-D shape perception and control technologies are crucial for continuum and soft robots to perform tasks autonomously in surgical interventions. However, owing to the nonlinear properties of continuum robots, one main difficulty lies in the modeling of them, especially for soft robots with variable stiffness. To address this problem, we propose a versatile learning-based adaptive shape controller by leveraging proprioception of 3-D configuration from fiber Bragg grating (FBG) sensors, which can online estimate the unknown model of continuum robot against unexpected disturbances and exhibit an adaptive behavior to the unmodeled system without priori data exploration. Based on a new composite adaptation algorithm, the asymptotic convergences of the closed-loop system with learning parameters have been proven by Lyapunov theory. To validate the proposed method, we present a comprehensive experimental study using two continuum and soft robots both integrated with multicore FBGs, including a robotic-assisted colonoscope and multisection extensible soft manipulators. The results demonstrate the feasibility, adaptability, and superiority of our controller in various unstructured environments, as well as phantom experiments.

Impedance pumps are simple designs that allow the generation or amplification of flow. They are fluid-filled systems based on flexible tubing connected to tubing with different impedances. A periodic off-center compression of the flexible tubing causes the fluid to move and generate flow. Wave reflection at the impedance mismatch is the primary driving mechanism of the flow. In addition to their straightforward design, impedance pumps are bladeless, valveless, and pulsatile. These properties are highly sought after by demanding and challenging applications, such as the biomedical field, as they present less risk of damage, disruption, and obstruction when handling viscous and delicate fluids/matter. In this study, we propose a high-performance impedance-driven pumping concept with embedded actuation based on a multilayered tubular dielectric elastomer. This pumping system is made of three parts, a dielectric elastomer actuator tube, a passive tube, and a rigid ring that binds and decouples the two subsystems. The system is able to generate net fluid flow rates up to 1.35 L/min with an internal pressure of 125 mmHg. The soft simplistic design, self-contained concept, and high performances of these pumping systems could make them disruptive in many challenging meso- and macroscale applications in general and in the biomedical field in particular.

Robust hand motion tracking holds promise for improved human-machine interaction in diverse fields, including virtual reality, and automated sign language translation. However, current wearable hand motion tracking approaches are typically limited in detection performance, wearability, and durability. This article presents a hand motion tracking system using multiple soft polymer acoustic waveguides (SPAWs). The innovative use of SPAWs as strain sensors offers several advantages that address the limitations. SPAWs are easily manufactured by casting a soft polymer shaped as a soft acoustic waveguide and containing a commercially available small ceramic piezoelectric transducer. When used as strain sensors, SPAWs demonstrate high stretchability (up to 100%), high linearity (R² > 0.996 in all quasi-static, dynamic, and durability tensile tests), negligible hysteresis (<0.7410% under strain of up to 100%), excellent repeatability, and outstanding durability (up to 100,000 cycles). SPAWs also show high accuracy for continuous finger angle estimation (average root-mean-square errors [RMSE] <2.00°) at various flexion-extension speeds. Finally, a hand-tracking system is designed based on a SPAW array. An example application is developed to demonstrate the performance of SPAWs in real-time hand motion tracking in a three-dimensional (3D) virtual environment. To our knowledge, the system detailed in this article is the first to use soft acoustic waveguides to capture human motion. This work is part of an ongoing effort to develop soft sensors using both time and frequency domains, with the goal of extracting decoupled signals from simple sensing structures. As such, it represents a novel and promising path toward soft, simple, and wearable multimodal sensors.

Soft grippers with good passive compliance can effectively adapt to the shape of a target object and have better safe grasping performance than rigid grippers. However, for soft or fragile objects, passive compliance is insufficient to prevent grippers from crushing the target. Thus, to complete nondestructive grasping tasks, precision force sensing and control are immensely important for soft grippers. In this article, we proposed an online learning self-tuning nonlinearity impedance controller for a tactile self-sensing two-finger soft gripper so that its grasping force can be controlled accurately. For the soft gripper, its grasping force is sensed by a liquid lens-based optical tactile sensing unit that contains a self-sensing fingertip and a liquid lens module and has many advantages of a rapid response time (about 0.04 s), stable output, good sensitivity (>0.4985 V/N), resolution (0.03 N), linearity (R² > 0.96), and low cost (power consumption: 5 mW, preparation cost

Multiple sclerosis (MS) is a chronic autoimmune disorder that affects the central nervous system and can result in various symptoms, including muscle weakness, spasticity, and fatigue, ultimately leading to the deterioration of the musculoskeletal system. However, in recent years, exosuits have emerged as a game-changing solution to assist individuals with MS during their daily activities. These lightweight and affordable wearable robotic devices have gained immense popularity. In our study, we assessed the performance of an elbow exosuit on eight individuals with MS using high-density electromyography to measure biceps muscle activity. The results demonstrated that our prototype significantly reduced muscle effort during both dynamic and isometric tasks while increasing the elbow range of motion. In addition, the exosuit effectively delayed the onset of muscle fatigue, enhancing endurance for people with MS and enabling them to perform heavy duty tasks for a longer period.