This paper introduces a novel hybrid unmanned aerial vehicle (UAV) configuration named as the hybrid QuadPlane with all-moving wings (HQWAW), which features two wings capable of dynamic adjustment during flight. Compared with conventional QuadPlane, the HQWAW can optimize lift generation and reduce rotor thrust by altering its angle of attack. But the additional degrees of freedom and nonlinear dynamics pose challenges for control strategy design. We proposed an end-to-end control strategy using deep reinforcement learning (DRL). This approach enables the HQWAW to discover an optimal control policy that simultaneously improve tracking accuracy and energy efficiency, with the simulation results illustrating the effectiveness of the proposed method.

This article introduces a novel composite aerial vehicle configuration called hybrid quadrotor with all-moving wings (HQWAW), consisting of a conventional quadrotor combined with two independently all-moving wings. A nonlinear geometric controller in the special Euclidean group SE(3) is proposed as the basic controller for the HQWAW, achieving high maneuverability and energy-efficient flight. Lyapunov stability criterion is used to prove that the proposed control scheme can track the reference trajectory almost globally ultimately uniformly bounded. A deep reinforcement learning compensator, based on the twin delayed deep deterministic policy gradient algorithm, is designed to fine-tune all-moving wing angles, ensuring that wing surfaces remain at optimal angles, thereby maximizing aerodynamic efficiency and reducing rotor consumption. Tracking results for a trajectory involving high-speed dive followed by spiral ascent demonstrate that the proposed algorithm achieves both high maneuverability and improved energy efficiency of the HQWAW.

Iontronic tactile sensors are promising to measure spatial-temporal contact information with high performance. However, no suitable measuring method has been presented, due to issues with crosstalk and non-negligible equivalent resistance. Hence, this study presents an impedance-separating method, which does not require complex analog components. A general Quadri-Terminal Impedance Network (QTIN) model is introduced to reduce crosstalk, which has specific compatibility with the impedance-separating method. The precise ranges are measured, showing non-rectangle shapes suitable for the response of iontronic tactile sensors. A simple denoising method is provided to reduce initial array noise obviously. This work could benefit various scenarios, such as human-robot interaction and physiological information monitoring.

With the aging of society, there has been an increase in the number of elderly individuals with limb movement disorders. Active rehabilitation training using limb rehabilitation electromechanical devices that incorporate multimodal sensing and monitoring functions can significantly contribute to the recovery of limb motor functions. This report introduces a wearable mechanical pressure-electrophysiological monitoring bimodal sensing system specifically designed for human limb rehabilitation devices. By utilizing just four electrodes (SE, CE/DE, GND, REF), this system enables simultaneous and co-located measurement of surface electromyographic (sEMG), pressure, and mechanomyography (MMG) signals. These signals can be utilized to analyze muscle tension, stiffness, and tremor information. At last, this sensing system was used to assess muscle contraction force and localized muscle fatigue. The time and frequency domain characteristics of physiological signals during exercise were thoroughly investigated. The wearable mechanical pressure-electrophysiological bimodal sensing system can provide valuable data references for rehabilitation robots or human limb rehabilitation device, which is of great significance in the diagnosis of muscular diseases and rehabilitation treatment.

Humanoid lower limbs with tactile cognition are crucial for future bipedal robots developing advanced bionic intelligence, such as owning autonomous reflexes and performing human-like actions. Most existing robotic lower limbs focus on providing physical support and mobility, with little work on more bionic DoFs or tactile sensing abilities that are more than significant for a fully humanoid system. This paper develops a robotic lower limb with whole-foot tactile sensing capability. An eight-DoF mechanism with humanoid joints is designed comprehensively, and a tactile sensor with electric double-layer capacitors principle wraps the special-shaped foot surface. An STM32-based circuit integrating perception and control with a real-time inverse kinematics algorithm is experimentally demonstrated. This work provides novel insight and methodology for humanoid robots and tactile-based bionic intelligence.

Human touching the robot to convey intentions or emotions is an essential communication pathway during physical Human-Robot Interaction (pHRI). Therefore, advanced service robots require superior tactile intelligence to guarantee naturalness and safety when making physical contact with human subjects. Tactile intelligence is the capability to percept and recognize tactile information from touch behaviors, in which understanding the physical meaning of touching actions is crucial. For this purpose, this report introduces a recently collected and organized dataset "TacAct" that encloses real-time tactile information when human subjects touched the test device mimicking a robot forearm. The dataset contains 12 types of 24,000 touch actions from 50 subjects. The dataset details are described, the data are preliminarily analyzed, and the validity of the dataset is tested through a convolutional neural network LeNet-5 which classifying different types of touch actions. We believe that the TacAct dataset would be beneficial for the community to understand the touch intention under various circumstances and to develop learning-based intelligent algorithms for different applications.