Abstract:
An auxiliary support method based on variable stiffness supernumerary robotic limbs includes: obtaining data from a surface electromyography sensor and an inertial sensor; processing the data from the inertial sensor to determine whether a wearer has an operation intention; preprocessing the data from the surface electromyography sensor through full-wave rectification, low-pass filtering and normalization; using preprocessed surface electromyography to estimate a reference stiffness of an arm of the wearer; and mapping the reference stiffness of the arm to an impedance control model of the supernumerary robotic limbs. In the method, man-machine cooperation between human and the supernumerary robotic limbs in a task of overhead support is achieved by coordinating a stiffness of the human arm and a stiffness of the supernumerary robotic limbs, thereby reducing input of personnel in the task; and when the stiffness of the arm of the wearer decreases, the stiffness of the supernumerary robotic limbs increases.
Abstract:
A wrist rehabilitation training system based on muscle coordination and variable stiffness impedance control includes the following modules: an electromyographic signal collection and preprocessing module, a muscle co-decomposition and mapping model obtaining module, a man-machine interactive control module, and a virtual reality serious game module; collects a surface electromyographic signal of a forearm of a user, obtains time-domain coordination through non-negative matrix factorization, establishes a position and stiffness estimation model, and controls motion of a target in a serious game through variable stiffness impedance control, so as to complete a training task.
Abstract:
The present disclosure discloses an automated calibration system and calibration method for a flexible robot actuator. The calibration system includes a support frame. A visual positioning system, a pressure measuring system and a pneumatic pressure control system are respectively installed on the support frame. The visual positioning system is configured to measure a relative displacement and an angle between two ends of the flexible actuator. The pneumatic pressure control system is configured to charge air into an actuating end of the flexible actuator and measure an input pneumatic pressure of the flexible actuator. The pressure measuring system includes a pressure gauge installed on the support frame through a vertical axis motor system, and the flexible actuator to be calibrated installed on the support frame through a horizontal axis motor system and a rotating motor system. The rotating motor system is installed on the support frame through the horizontal axis motor system, the actuating end of the flexible actuator is fixed on the rotating motor system, and a free end of the flexible actuator is in contact with a measuring end of the pressure gauge to carry out pressure measurement. The calibration system is high in accuracy and simple to use.
Abstract:
A rehabilitation glove based on a bidirectional driver of a honeycomb imitating structure, including five bidirectional drivers and a cotton glove. The drivers are fixed to a back of the glove through hook and loop fasteners. Each driver includes a hollow buckling air bag in a continuous bent state, a middle guide layer in a continuous bent state and a hollow stretching air bag. The buckling air bag and the middle guide layer are symmetrically arranged, and the stretching air bag in a straightened state is arranged below the middle guide layer. A novel bidirectional driver of a honeycomb imitating structure is provided, which may provide a patient with rehabilitation training in two degrees of freedom: buckling and stretching. A control algorithm of the bidirectional driver is further provided to perform force control output for the driver, which may better help the patient recover hand functions.
Abstract:
The present disclosure discloses an autonomous mobile grabbing method for a mechanical arm based on visual-haptic fusion under a complex illumination condition, which mainly includes approaching control over a target position and feedback control over environment information. According to the method, under the complex illumination condition, weighted fusion is conducted on visible light and depth images of a preselected region, identification and positioning of a target object are completed based on a deep neural network, and a mobile mechanical arm is driven to continuously approach the target object; in addition, the pose of the mechanical arm is adjusted according to contact force information of a sensor module, the external environment and the target object; and meanwhile, visual information and haptic information of the target object are fused, and the optimal grabbing pose and the appropriate grabbing force of the target object are selected. By adopting the method, the object positioning precision and the grabbing accuracy are improved, the collision damage and instability of the mechanical arm are effectively prevented, and the harmful deformation of the grabbed object is reduced.
Abstract:
A exoskeleton finger rehabilitation training device comprises an exoskeleton finger rehabilitation training mechanism comprising a supporting base, a finger sleeve actuating mechanism, and a finger joint sleeve connected to a power output end of the finger sleeve actuating mechanism, wherein the finger joint sleeve can be sheathed at the periphery of a finger joint to be rehabilitated, and the finger joint sleeve can be driven by the power actuation of the finger sleeve actuating mechanism to drive the finger joint to be rehabilitated in order to passively bend or stretch; the supporting base comprises a profiled shell, with an inner surface of the profiled shell being configured based on the profile of the complete back of a palm or part of the back of the palm, and with the back of the profiled shell being provided with a power fixed base.
Abstract:
Disclosed is a portable haptic feedback capacitive stylus for interaction on a mobile terminal, composing a tip, a sleeve ring, a first housing, a connecting cylinder, a second housing, an end-portion housing, a motor, a spring, a slider, a screw rod, a photoelectric code disc, a measurement and control module, a vibration touch module, and a power supply. The first housing comprises a front housing and a rear housing, and a limiting groove is disposed in an inner wall of the first housing. The tip is fixedly connected to one end of the connecting cylinder, and the other end of the connecting cylinder passes through an end-portion through-hole of the front housing.
Abstract:
Disclosed is a wearable haptic feedback device for human-robot formation control, including an interactive interface module and a main control module, wherein the interactive interface module includes an input module and a wearable haptics feedback module; the wearable haptics feedback module includes an extrusion force feedback module, a shear force feedback module, and a vibration feedback module, and the main control module controls each module and receives operation instructions. The device can be wirelessly deployed on an arm of a user, and provide feedback on the formation change, motion guidance, and obstacle detection of the human-robot formation by generating three haptic feedback signals: extrusion, shearing, and vibration. A haptic channel is more advantageous in providing feedback to the user on understanding capability and task situation of external limbs, which can play a unique role in reducing the difficulty of human-robot interaction and creating highly immersive operation experience for the user.
Abstract:
Disclosed in the present disclosure is a method for identifying skills of a human-machine cooperation robot based on a generative adversarial imitation learning, which includes: firstly, defining classifications of human-machine cooperation skills that needed to be conducted; conducing demonstrations on different classifications of the skills by human experts, and collecting image information and data in the demonstrations to make calibrations; identifying the image information by means of image processing, extracting effective feature vectors capable of clearly distinguishing the different classifications of the skills and taking the effective feature vectors as demonstration teaching data; training a plurality of discriminators respectively by utilizing the acquired demonstration teaching data through a method of the generative adversarial imitation learning; extracting user's data after the training and putting the data into different discriminators, and taking a discriminator corresponding to a maximum value eventually output as an output result of identifying the skills. The present disclosure innovatively combines a computer image recognition with the famous generative adversarial imitation learning in a imitation learning, which has short training time and high learning efficiencies.
Abstract:
A force tactile feedback device at master end of robot assisted system for vascular interventional surgery includes a surgeon operating rod, a circumferential directional surgeon operation action capturing and force tactile feedback unit, a compressible and extendable rhombus structure, a transmission directional surgeon operation action capturing and force tactile feedback unit, a housing and a support plate. The force tactile feedback device at master end of robot assisted system for vascular interventional surgery keeps consistent with a traditional manual vascular interventional surgery in operation mode, and not only reduces a learning cycle and use difficulty for a surgeon on a master end apparatus, but also allows the surgeon to make full use of accumulated experience and skills.