A new design strategy for a research platform of a telepresence telerobot system based on virtual reality technology is put forward. The design frame of the system is simply described, and its important core techniques are described. An octrees data structure is utilized to build kinematic and dynamic modeling of the virtual simulation environment, Delphi+OpenGL+3DS MAX are adopted to carry through the virtual modeling and visible simulation exploitation of the slave-robot and its environment. Photo-correction is adopted to correct positioning deviation of the virtual geometric model and modeling errors. The cost of software and hardware equipment for the research platform realized is low. The master/slave robot (manipulator) system and all software in the system were designed and manufactured by our research group. The performance of the system has reached the level required for research. An indispensable experiment base is provided for the research of a telepresence telerobot system based on virtual reality technology.
Object handover is a fundamental task for collaborative robots, particularly service robots. In in-home assistance scenarios, individuals often face constraints due to their posture and declining physical functions, necessitating high demands on robots for flexible real-time control and intuitive interactions. During robot-to-human handovers, individuals are limited to making perceptual judgments based on the appearance of the object and the consistent behavior of the robot. This hinders their comprehensive perception and may lead to unexpected dangerous behavior. Various handover trajectories pose challenges to predictive robot control and motion coordination. Many studies have shown that force guidance can provide adequate information to the receivers. However, force modulation with intention judgments based on velocity, acceleration, or jerk may impede the intended motion and require additional effort. In this paper, starting from a human-to-human handover study, an anisotropic variable force-guided robot-to-human handover control method is proposed to overcome the cognition-reality gap. The retraction motion was decoupled based on a fitted motion plane and a task-related linear trajectory, which served as a reference for overshoot suppression and impedance force modulation. The experimental results and user surveys show that the anisotropic variable impedance force suppresses overshooting without impeding the intended motions, giving the receiver sufficient time for behavioral adjustments and assisting them in completing a safe and efficient handover in a preferred manner.
In the surgery of nasopharyngeal cancer, the surgeon operates through natural channels within small room. In its robot-assisted surgery, collisions between two manipulators and parameter variation of local tissue may lead to contact force change, It may cause target position offset. As the vessels and nerves were distributed densely in head and neck. The control system needs to ensure the operating contact force is within the safety threshold. It can avoid soft tissue tearing caused by changing of relative position over the limits. The research studies indirect adaptive admittance control based on position synchronization, which adjust the damping compensation rate. It improves the force control precision and system robustness of robot-assisted surgery through alleviating the effect of stiffness variation and collisions. And it can improve surgery security.
Facial paralysis is one of the symptoms of neurological diseases such as stroke and Bell's paralysis. It causes a partial loss of facial muscle control, resulting in some facial asymmetry. The traditional diagnosis needs experienced professional doctors. We propose a vision-based facial image acquisition and auxiliary diagnosis system for patients with facial paralysis. The acquisition system based on an industrial camera and array microphone collects high-quality patient images, which are preprocessed and input into the auxiliary diagnostic system for image analysis. For auxiliary diagnosis, an image classification algorithm based on fusion feature was adopted to extract Histogram of Oriented Gradients (HoG) features, facial landmark features, and learning features. The output of auxiliary diagnosis includes normal, left weakness, and right weakness. The diagnostic effect was verified on a self-made dataset. The proposed auxiliary diagnosis algorithm achieves a classification accuracy of 96.1%, recall of 91.6%, precision of 96.2%, and F2 of 92.5%. The system is suitable for auxiliary diagnosis in hospitals and early self-examination at home for the rehabilitation of stroke people. It has higher efficiency than traditional methods.
The simulation of deformable soft objects is a key challenge in virtual reality. This paper presents a novel parallel rhombus-chain-connected haptic deformation model based on physics. Because the rhombus in every chain structure unit are proportional in length, calculation cost is less and deformation modeling is easier for different soft objects in this model by only changing the length and angle of the rhombus. Based on the model, contact deformation and virtual feedback force for virtual human liver are simulated in DELTA manipulator. The experiment results demonstrate that the parallel rhombus-chain-connected model is suit for realistic haptic rendering of soft objects.
As an important component of force feedback devices, the multi-dimensional force sensor (MDFS) has been widely used in haptic devices, prosthetic hands, and other devices. The structural distribution of the stiffness and mass of an MDFS is key to deeply understand its dynamic performance. To obtain this information, a model-based method for load characteristics analysis of the MDFS is proposed in this paper. The dynamic behavior of the force sensor for a given load is described by a lumped mass model consisting of spring-mass-damper elements and characterized by the model parameters that describe the dynamic correlation distribution of mass, stiffness, and damping. Compared with the results of finite element analysis (FEA) and the spectrum analysis of the step response, the natural frequencies with different load masses are in accordance with the model based on the proposed method. The main purpose of the proposed method is the description of the load characteristics of the MDFS, independent of the given mechanical environment, which provides a certain theoretical reference for the calculation of the load capacity of the force sensor. Meanwhile, in order to improve the dynamic performance, a dynamic compensated filter is added to the force sensor coupling system, thereby broadening the operation frequency and greatly reducing the response time.
With the advantages of comfortable wearing and outdoor usage, the myoelectric gesture recognition techniques have gained much attention in the field of human-machine interaction (HMI). The purpose of this study is to optimize model structure and transfer generalized features to improve the robustness of myoelectric hand motion decoding. We derived the hand motion recognition framework from the muscle synergy theory, which is formulated as a temporal convolutional (TC) model of array sEMG signals, then a hierarchical myoelectric decoding model was proposed to predict simultaneous and continuous hand motion. The model was trained by the methods of unsupervised low-level feature learning and automated data labeling to minimize training supervision. Extensive experiments on the public sEMG database (17 subjects in Biopatrec) show that the TC model can extract muscle synergy features with higher fidelity ( R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.85±0.23) than the traditional instantaneous mixture model, the results of online test demonstrate robust myoelectric decoding on multiple simultaneous and continuous hand motions. More importantly, the analysis of weights visualization shows that the low-level feature representation layer of TC model can be migrated across the individuals, which provides a transferrable feature extraction layer for generalized hand motion decoding.
A novel four degree-of-freedom (4 DOF) wrist force/torque sensor is developed, which is mechanically decoupled. This type of wrist force/torque sensor is different from ordinary mechanically decoupled ones. It has a simple structure and small coupled interference or noise. It is easy to process and calibrate, and low in cost. This paper introduces the elastic body structure of the wrist force/torque sensor, and analyses the mechanically decoupled principle in detail
Many applications in virtual reality and telerobot call for the implementation of displaying to the human softness haptic on the object being touched. Although there are lots of literatures on discrimination thresholds for displacement, force magnitude, shape, and viscosity, there is still a lack of research on discrimination and remembrance of softness perception of human fingertip. In this paper, a novel stiffness display device based on deformable length of elastic element control is presented firstly. Then the experiments of human fingertip perception of softness haptic by using the device that we developed are carried out. The haptic perception of human finger which includes resolution, times, frequency and remembrance are discussed. At last, some important conclusions are drawn. The results can aid in the process of designing haptic devices.
