For pneumatic artificial muscles, it is always considered the more maximum contraction ratio the better. While for human joint assisting applications, PAMs with configurable maximum contraction rate are more suitable because of advantageous safety and adaptability. A PAM based on planar-to-specific-wave body shape morph is proposed in this work. Shape-morphing-based braided artificial muscles (SBAMs) have uniqueness of initial elasticity and maximum contraction ration programmability, which meet the favors of human joint assisting applications. The basic structure and working mechanism of contraction in SBAMs will be explained, and their mathematical model will also be established. According to the experimental results, a SBAM prototype generates a force more than 140 times its weight under an easily accessible pressure of 150 kPa. A mannequin wearing the SBAM enables actively flexes its elbow over 120 °.

Soft pneumatic actuators (SPAs) are widely used in robotic applications due to their inherent compliance and outstanding mechanical performance. However, a tradeoff between load capacity and deformation capacity is required when selecting material hardness for SPAs. Too hard material usually results in limited deformation, such as small extension, bending, and twisting, while too soft a material decreases robustness. This study introduces coil-reinforced flat tube actuators (CFTAs) that exhibit excellent flexibility and high load capacity by braiding flat tubes in coil springs. By adjusting the braiding pattern of the flat tube, the CFTAs can realize extending, in-plane bending, and out-of-plane helical bending motions. In addition, analytical models are proposed to predict the deformation behavior of the CFTAs and verified by experiments. The bending type CFTA deforms with an excellent curvature (0.516 mm−1) under 160 kPa input pressure, five times larger than the reported SPAs on the same scale. The CFTAs show high design flexibility by programming the flat tube pattern and using multiple coiled springs for wide application scenarios. Based on the CFTAs, this study shows wearable upper limb robots, a soft entanglement gripper, and a rob-climbing robot. CFTAs provide design insight for applications requiring dexterous and versatile deformations.

Soft actuators have shown advantages of adaptiveness, large deformation, and safe human-robot interaction, making them suitable for various applications. Herein, a novel soft flat tube twisting actuator (SFTTA) is proposed. The SFTTA is composed of a folded flat tube sandwiched between two silicone rubber laminates. When inflated by compressed air, the folded corners of the flat tube tend to unfold, resulting in the twist of the actuator to a helical structure. The SFTTA has great scalability. It can be fabricated through simple processes with low-cost materials. For a sample SFTTA with the size of a human finger, it can twist 5400at an air pressure of 300 kPa. In general, SFTTA based actuators can twist 9.6 degree per millimeter in length, which is significantly larger than previously reported soft twisting actuators. Additionally, the composite-like SFTTA allows mechanical property programming through the alteration of folding patterns of the flat tube and the material structure of the elastomer laminates. Finally, an extensible soft gripper based on flat tube actuators and a robotic wrist module are developed, and their rotation is realized by the proposed SFTTA actuator.

Three dimensional (3D) source positioning techniques based on linear arrays have garnered significant attention from scholars in recent years, which use multiple one-dimensional angles of arrival (1D AOA) to localize sources. Existing 1D AOA-based 3D source positioning techniques are not directly applicable to cooperative positioning systems, particularly in scenarios involving cooperative sources such as underwater environments, the depth of the source is usually observable. This paper presents a novel cooperative positioning model that integrates 1D AOA and depth information, proposing a two-stage weighted least squares (TWLS) closed-form solution method. Furthermore, the Cramér-Rao lower bound (CRLB) for 1D AOAs and depth measurements is derived. The simulation experimental results demonstrate the feasibility of the proposed positioning scheme and the advantages of the TWLS method.

Abstract Due to the limited hardware available in virtual reality, such as VR controllers or simple motion capture devices, users will need more tactile feedback, like the weight of virtual objects. Pseudo‐tactile feedback, such as the control display (C/D) ratio manipulation method, is considered a standard method to simulate weight perception. In past studies, this method was basically used in a static environment, and the C/D ratio was usually determined initially. Can this method still work if the C/D ratio changes in dynamic usage scenarios? In a series of experiments, we tried to answer this question. We proved that the dynamic change of C/D ratio could simulate the weight change and improve the sense of embodiment through another hand redirection method.

Owing to high compliance, adaptiveness, and easy controllability, soft actuators are widely adopted in soft grippers to grasp irregularly shaped or fragile objects. The specific motions can be preprogrammed into the flexible and constrained structures of the actuator, which provides an inexpensive and convenient method for desired motions. However, most preprogrammed structures cannot change the constraints on the actuator to achieve different kinds of deformations, which limits the motion diversities of actuators. This article proposes a scaffold reinforcement mechanism, where rotatable scaffolds distribute on the surface of the soft structure. The orientation adjustments of the scaffolds can change the deformation constraint of the actuator, which results in different kinds of motions. Based on the scaffold reinforcement mechanism, a scaffold-reinforced actuator is proposed, which can achieve bending motion and complex helical motion in the 3-D space by properly adjusting the orientation of the scaffolds. In addition, both the kinematic and mechanical models are proposed to forecast the behavior of the actuator when driven by cable displacement or tension force. Experimental results verify the validity of the theoretical model, and the actuator can achieve an independent control of bending and helical motion, which can be adopted in applications where both high dexterity and flexibility are required.

Current 3D target detection methods used in the field of autonomous driving generally have low real-time performance and insufficient target context feature to detect dynamic multi-target accurately. In order to solve these problems, a dynamic multi-target detection algorithm of voxel point cloud fusion based on PointRCNN is proposed, which adopts a two-stage detection structure. The first stage directly processes the point cloud to extract key point features and divides voxel space. A novel submanifold sparse convolution is used to extract voxel features. Then key point features and voxel features of the point cloud are merged to generate pre-selection boxes. In the second stage, reference points are set based on the voxel features. The features of key points around reference points are merged for the second time to achieve optimized detection boxes. Finally, for the problem of inconsistent confidence, a mandatory consistency loss function is proposed to improve the accuracy of the detection box. The proposed algorithm was compared with other algorithms in three different datasets, and further tested on a self-made dataset from an actual vehicle platform. Results showed that the proposed algorithm had higher accuracy, better robustness, stronger generalization ability for dynamic multi-target detection.

Soft continuum robots have many applications such as medical surgeries, service industries, rescue tasks, and underwater exploration. Flexibility and good accessibility of such robots are the key reasons for their popularity. However, the complexity of their structural design and control systems limit their broader applications. In this paper, a novel variable stiffness soft continuum robot based on pre-charged air, particle jamming, and origami is proposed. The robot is a bellow-like origami structure with internal chambers. A spine-like chamber is filled with particles, and three identical chambers surrounding the spine chamber are filled with pressurized air. When the origami structure is compressed, the particles are jammed by the compression force and the increased pressure of the three air chambers, thus increasing the overall stiffness of the robot. The robot expansion-contraction and bending are controlled by three tendons. An analytical model of the proposed stiffness variation mechanism has been developed. The effects of various parameters on the lateral and axial stiffness of the soft continuum robot have been investigated by experimental studies. A prototype robot has been fabricated to demonstrate grasping operations.

Robotic hands with anthropomorphism considerations are of prominent popularity in human-centered environment. Existing anthropomorphic robotic hands achieving part or most of human hand comparable dexterity have been applied as various robotic end-effectors and prosthetics. However, two deficiencies are evident that the design for a dexterous anthropomorphic hand is largely based on the intuition of designers and the dexterity of robotic hand is hard to evaluate. To tackle these two challenges, this paper summarizes 50 hand dexterity benchmarks (HD-marks) to evaluate hand dexterity comprehensively from three perspectives. Secondly, a novel 22-DOFs soft robotic hand (S-22) replicates human hand kinematics is used to demonstrate all the 50 HD-marks. Thirdly, 7 critical joint-based kinematic motions (K-motions) and their correlation with the 50 HD-marks are established. Therefore, a clear robotic hand design guideline is built by mapping the hand functional dexterity to the required joint kinematics.

Soft robotic actuators are highly elastic bodies that oscillate drastically once excited. This oscillation is undesirable in many applications. So far, very little studies on soft actuator damping have been reported. In this paper, we report a simple and effective vibration damping method based on passive and active particle damping. Experimental studies on the effectiveness of particle damping have been conducted. It is found that active particle damping is more effective than passive damping, nevertheless, active particle damping demands a more complicated design with extra energy source and control. Since particles are discrete matters, they can be seamless integrated into soft actuator design with only minor influence of soft actuator's compliance and softness.

Soft robots are inherently compliant and adaptive, therefore they are promising candidates for interacting with humans. However robotic manipulators utilizing soft actuators are often constrained by a series of actuator performance limitations. In this work we design a novel linear soft robotic actuator with significantly improved performances over the existing products, achieving 300% deformation ratio, quasi-constant output force over a wide motion range, while maintaining passive compliance and adaptability. Moreover, the novel actuator is less prone to friction, and could be fabricated using inject molding and 3D printing, hence having high repeatability at very low cost. An analytical model was developed to characterize the actuator behavior and provide a guideline for actuator design according to performance specifications. A 6 DOF soft manipulator was designed and fabricated utilizing the novel soft actuator. The manipulator arm had a serial kinematic structure with a biomimetic wrist and was driven by 12 soft actuators mounted onto the arm links. With 1.2m workspace radius and 1kg payload, the working air pressure could be as low as 1bar. Preliminary results have shown the validity of the novel soft actuator and manipulator designs, as well as the strong potential of soft robots in human-oriented applications.

Current research interests for humanoid robots include cost, static stability, and human-likeness. With the help of well-developed locomotion controllers, human-like gait has been realized on humanoid robots with higher energy efficiency and increased motion speed. However, such results are based on the walking function of bipedal robots, running function has been less touched or investigated. Different from walking, running could cause more injuries when human cope with the impact from the ground. In this paper, we define a new foot strike pattern that describes the instinctive human running manner and the performance of a group of adults is collected and analysed for comparison of different foot strike patterns. This helps with lower limbs' injury prevention by proving that most of the impact transient is reduced in the new foot strike pattern for both the shod and the barefoot conditions. Therefore, the newly defined foot strike pattern can be considered as an alternative option to reduce the injury of running activities. Furthermore, it would be possible to apply the foot strike pattern mechanism in humanoid robots, not only to extend the service life from the collision and vibration damage of the components, but also to make robots more human-like.

The compliance of soft grippers contributes to their great superiority over rigid grippers in grasping irregularly shaped objects and forming soft contact with environments. Due to a relatively small pressure, soft grippers lack the stiffness required for wider applications. Particle jamming has been frequently reported as a means of stiffness control. Unlike previous research using vacuum for particle jamming, this paper proposes a novel passive particle jamming principle that does not need any vacuum power or other control means. The proposed method is by simply patching a silicone rubber soft actuator and a pack (made of strain-limiting membrane) of particles to form an integral gripping finger. The inflation of the soft actuator applies a pressure to the particle pack causing particles inside it to jam. A larger squeezing pressure will result in tighter particle jamming, thus increasing the stiffness of the finger. The stiffness of the finger is controllable as it is proportional to the actuator's air pressure, which has been verified by experiments in this research. The stiffness can increase more than six fold when air pressure changes from 20 to 80 kPa in the experimental studies. The reported discovery may enhance the capabilities of soft robotic grippers so that more robotic picking operations could be performed by soft grippers.

Shape memory polymer (SMP) is a type of functional materials that changes Young's modulus when heated above glass transition temperature (Tg). In this work, this property of SMP has been explored for the design and fabrication of a modular omni-directional joint with variable stiffness. When cascading a number of such joints, a variable stiffness hyper-redundant robotic arm can be built. The basic design of the variable stiffness joint is based on a ball joint where the ball is made of two materials: acrylonitrile butadiene styrene (ABS) and SMP, and the socket is made of only ABS material. When heated, the ball joint shows different resistive torques below and above the SMP's glass transition temperature Tg. Moreover, shape recovery property of SMP material above Tg guarantees the design with high repeatability. Both the ball and the socket are made by a 3D printing process fused deposition modeling (FDM). The FDM fabrication of SMP is made possible by a novel process control method in the FDM process. The ball joint's variable stiffness is tested by a number of experiments. Experimental results indicate distinct changes in resistive torques at different test temperatures. Using the proposed modular omni-directional ball joints, a variable stiffness hyper-redundant robotic arm is built.