We described a tensegrity robotic fish and detailed its overall structure, stiffness, and mechatronics. The main flexible structure of the robotic fish body was composed with a series of rigid segments linked with tensegrity joints by means of tension elements. Each rigid segment can rotate around tensegrity-compliant joint and have no direct contact with each other. The dominant vibrational mode of the tensegrity robotic fish can be excited by a single harmonic input to mimic the desired kinematics of locomotion. For our tensegrity robotic fish, the experimental results showed that its maximum stride length was about 0.5 body length per cycle; its Strouhal number was roughly between 0.45 and 0.55 near the biological data of carangiform swimmers. Two different vibrational modes that can be achieved would be demonstrated by the harmonic analysis technique. The results indicated that the swimming performance can be improved by using tensegrity joints.

Numerous biological studies of body and/or caudal fin fish have powerfully identified that the fish body's passive properties, such as stiffness and its distribution, contribute to the fish's outstanding swimming performance. In this study, we develop a free-swimming tensegrity fish robot (TenFiBot) to help scholars experimentally improve their understanding of the role of stiffness distribution in fish-like swimming. We detail the overall structure and parameters of TenFiBot, which can achieve body stiffness distribution with high stiffness controllability over a wide range. The effective variable stiffness principle is based on antagonistic stiffness that results from the prestress of tension structures in a kinematically singular configuration. We validate the effectiveness of variable stiffness for one variable-stiffness tensegrity joint (VSTJ) and demonstrate that the virtual rotational centers are always in the acceptable range during stiffness variation. We exemplify some cases of the stiffness distribution to verify the validity of body stiffness variation. The experimental results show that swimming performance, such as swimming velocity, varied dramatically when the stiffness distribution was varied for the robotic fish. The swimming performance of the robotic fish can be enhanced tremendously by changing the stiffness distribution. Moreover, the VSTJs in the fish body have great potential in constructing compliant robotic arms and snake-like robots.

Soft robots, inspired by living organisms in nature, are primarily made of soft materials, and can be used to perform delicate tasks due to their high flexibility, such as grasping and locomotion. However, it is a challenge to efficiently manufacture soft robots with complex functions. In recent years, 3D printing technology has greatly improved the efficiency and flexibility of manufacturing soft robots. Unlike traditional subtractive manufacturing technologies, 3D printing, as an additive manufacturing method, can directly produce parts of high quality and complex geometry for soft robots without manual errors or costly post-processing. In this review, we investigate the basic concepts and working principles of current 3D printing technologies, including stereolithography, selective laser sintering, material extrusion, and material jetting. The advantages and disadvantages of fabricating soft robots are discussed. Various 3D printing materials for soft robots are introduced, including elastomers, shape memory polymers, hydrogels, composites, and other materials. Their functions and limitations in soft robots are illustrated. The existing 3D-printed soft robots, including soft grippers, soft locomotion robots, and wearable soft robots, are demonstrated. Their application in industrial, manufacturing, service, and assistive medical fields is discussed. We summarize the challenges of 3D printing at the technical level, material level, and application level. The prospects of 3D printing technology in the field of soft robots are explored.

At present, the research on gesture recognition based on ultrasonic signals is mostly about static gestures. Compared with static gestures, dynamic gestures reflect human intent more intuitively. However, the speed difference of dynamic gestures will cause a mismatch in time series. To tackle this problem, we proposed a dynamic gesture recognition scheme based on the dynamic time warping (DTW) algorithm. First, the ultrasonic signals of the forearm muscles were acquired by four A-mode ultrasonic transducers. Second, the original signals underwent Gaussian filtering, feature extraction, normalization, and principal component analysis (PCA) dimensionality reduction to obtain the main feature information and convert it into time series. Finally, the time series of dynamic gestures were matched based on the DTW algorithm, and the minimum Euclidean distance between different time series was calculated for prediction. We tested five dynamic gestures on ten subjects. The experiments were conducted for a total of ten rounds, and only one round was used as the training set. Results reveal that using the max feature can get an accuracy of 88.0% ± 5.9%. In addition, the energy feature (87.5% ± 6.4%) and the mean feature (85.2% ± 6.9%) also performed well. This research realizes the dynamic gesture recognition of A-mode ultrasonic for the first time, proving the potential of ultrasonic signals, and providing a wider range of use scenarios.

The gesture recognition technology as one of the human-machine interfaces can convenient and effectively express the intention of human, and has become the hot research hotspot in recent years. Force level is a key factor while gesture recognition for more dexterous and natural prosthetic control. To provide researchers with a systematic and quick overview of the relevant and future developments in gesture recognition and force assessment techniques, this review synthesizes current commonly used sensor interfaces, data processing methods, and methods that have improved recognition performance. The experimental design and related results of gesture recognition and force assessment with various types of sensors are analyzed and compared to understand the scope of application and recognition performance of different sensors. This review summarizes the challenges and future work in the five areas of hardware, use environment, broad applicability, physiological factors, and comfort of use in practical applications. Finally, the conclusion prospects that future research may need to focus on improving model generalization and robustness to environmental, physiological factors, etc. by building large datasets and developing flexible, long-lasting, lightweight and senseless, high-performance interfaces.

Physical intelligence for aerial robots greatly enhances grasping and perching performance, but remains in emerging stages. This letter proposes a novel bistable soft gripper for aerial robots with high response speed (0.11 s), large holding force (23.47 N), and active/passive adaptive grasping and perching. The soft gripper is constructed by four bistable fingers, tension nets, and a bidirectional actuation system. The soft finger evolves from a simple bistable rotational joint. Tension nets inspired by spider webs are proposed to improve the energy barrier and grasping performance. Experiments are conducted to measure the gripper's potential energy variation and grasping performance. One peak and two local minima in the energy curve indicate the gripper's bistability. Experimental results show that tension nets can enhance the gripper's energy barrier, response speed, and maximum holding force by 915.07%, 38.55%, and 62.08%, respectively. The gripper's adjustability of the energy barrier is validated, enabling it to switch active/passive modes as needed. The experiments demonstrated static/dynamic grasping and perching for various daily objects with different shapes, sizes, and stiffness for the gripper and aerial robot. Finally, the robot can transport objects outdoors, and can be aerially manipulated by external force, demonstrating its great potential in aerial application.