SOTA Snake robots attract wide attention due to their modular bodies and multi-freedom movement abilities. However, the high production costs and intricate manufacturing procedures hinder the rapid mass production and deployment of the snake robot. Additionally, these factors limit its consumer-grade popularization and educational applications. To handle the above issues, this work proposes a low-cost lightweight intelligent modular snake robot, i.e., EdgeSnake, with a simple modular mechanical structure and flexible manufacturing technique. Specifically, each module of the snake robot is designed to minimize unnecessary volume, thereby reducing material usage and achieving a lightweight body structure. Moreover, an innovative rapid 3D printing and assembly approach is developed to efficiently realize mass production and development of specific snake robots, adapting to varying task demands and terrain features. Furthermore, EdgeSnake is also equipped with an RGB camera and onboard processor to achieve environmental perception. A comprehensive simulation control system is developed for EdgeSnake based on the ROSGazebo platform, thereby facilitating its instructional employment. Extensive experiments have been conducted on the selfbuilt simulation control system to verify the superior obstacle detouring and traversing capabilities of EdgeSnake.
Vision-and-language Navigation (VLN) is a challenging problem that requires agents to follow natural language instructions in a photo-realistic environment. The alignment between visual object information and instruction object information is critical for the navigational capabilities of intelligent agents. However, most reinforcement learning policies primarily focus on the agent’s distance change to the target viewpoint as the direct reward after taking an action, with object information playing a minor role in classical reinforcement learning for VLN. To address this limitation, we construct a new reward shaping that incorporates both the changes in the agent’s distance to the target and the progress made in navigating according to the given instruction. To capture the navigation progress, we propose an object alignment method that aligns the visual object information observed by the agent with the object information specified in the instructions. By leveraging the object’s position within the navigation instruction, we estimate the agent’s approximate progress during navigation. Experimental results demonstrate the effectiveness of our approach in reducing the navigation error (NE) and achieving high performance in terms of the success rate weighted by path length (SPL). Our method significantly enhances the agent’s ability to accurately follow natural language instructions to reach the intended destination, while also exhibiting improved generalization in unseen environments.
Intelligent machine fault diagnosis methods that leverage machine learning techniques have received widespread attention owing to their proven efficacy in enhancing production efficiency and quality as well as in reducing production costs. However, given the scarcity of rolling bearing failure data, traditional neural network training demonstrates weak noise immunity and limited network generalization capabilities. To address these issues, this study proposes a rolling bearing fault diagnosis method that combines a dual-channel hybrid domain neural network with transfer learning. The innovatively designed MDRSBU-MA module and BiGRU hybrid channel demonstrate enhanced fault feature extraction and anti-noise capabilities across domains, thus obviating the need for denoising algorithms. Furthermore, a diverse array of transfer tasks was designed for various bearing fault datasets and under differing working conditions. Experimental results suggest that the model retains robust fault diagnosis capability, particularly in terms of resistance to noise interference, small-sample cross-working condition domain, and cross-device domain transfer tasks.
Bioimpedance provides vital information for a variety of clinical application. Most current bioimpedance device are confined in their large volume, high cost and single detectable parameter. This study aimed to design a novel four-channel, multi-parameter bioimpedance acquisition device with lower volume and wearable design. This new device was designed based on an AD5933 impedance analyzer chip, enabling real-time acquisition of bio-impedance values, bio-impedance phase angle, and bio-impedance ratio through a small extension circuit for impedance acquisition and an analog switching circuit based on ADG888. Results showed that the sampling rate of the novel device could reach up to 100 Hz with a resolution of 0.01 Omega and errors within +/- 1%. Application of this novel device in bioelectrical defibrillation showed that the impedance information could successfully guide the onset and energy outputs of the defibrillation. This device may provide a novel avenue for wearable bioimpedance measurement and have a potential application in bioelectrical defibrillation.
This paper extends the Generalized UdwadiaKalaba (GUK) method to the field of control, and a novel robust control based on GUK method is proposed. The robust control can effectively handle uncertain mechanical systems with both equality and inequality constraints, in which these constraints do not affect each other. The control consists of three parts, the first part is the nominal control based on GUK equation, the second part is to compensate for the incompatibility of initial conditions, and the third part is to compensate for the uncertainty in the mechanical model. The stability analysis of the robust control is verified by applying the Lyapunov minimax method. Subsequently, through a comparative analysis of simulation outcomes involving an autonomous vehicle, this paper confirms the superiority and effectiveness of the novel robust control.
