Gear failure is the main cause of machine malfunction. Therefore, it has become increasingly important to detect gear failure to ensure the normal operation of a machine. Here, we propose a gear damage detection and localization approach by studying the vibration signal of an individual gear tooth and support vector machines. Generally, it is difficult to detect a small gear failure in the total vibration signal. The waveform of an individual gear tooth was studied to investigate the vibration features of a gear in more detail. The characteristics of damaged and normal teeth were investigated by analyzing their individual waveform. Besides, the feature parameters were also extracted from both the time and frequency domains of the waveform to investigate the characteristics of each gear tooth. The difference between the damaged and normal teeth was detected by the waveform and feature parameters. Additionally, the condition of each gear tooth was diagnosed by support vector machines using the extracted feature parameters. The method was used to analyze the results of cyclic fatigue experiments. The conditions of most of the gear teeth were correctly classified, validating the proposed method.

The accurate perception of external environment information through the robot foot is crucial for the mobile robot to evaluate its ability to traverse terrain. Adequate foot-end contact signals can provide robust support for robot motion control and decision-making processes. The shape and uncertain rotation of the wheel-legged robot foot end represent a significant challenge to sensing the robot foot-end contact state, which current foot-end sensing schemes cannot solve. This paper presents a sensing method for the tire stress field of wheel-legged robots. A finite element analysis was conducted to study the deformation characteristics of the foot-end tire under force. Based on this analysis, a heuristic contact position estimator was designed that utilizes symmetrical deformation characteristics. Strain sensors, arranged in an array, extract the deformation information on the inner surface of the tire at a frequency of 200 Hz. The contact position estimator reduces the dimensionality of the data and fits the eigenvalues to the estimated contact position. Using support vector regression, the force estimator utilizes the estimated contact position and sensor signal to estimate the normal reaction force, designated as F_Z. The sensing system is capable of detecting the contact position on the wheel circumference (with a root mean square error of 1.150°), as well as the normal force of 160 N on the Z axis (with a root mean square error of 6.04%). To validate the efficacy of the sensor detection method, a series of randomized and repeated experiments were conducted on a self-constructed test platform. This novel approach offers a promising avenue for perceiving contact states in wheel-legged robots.

Acquiring terrain information during robot locomotion is essential for autonomous navigation, gait selection, and trajectory planning. Quadruped robots, due to their biomimetic structures, demonstrate enhanced traversability over complex terrains compared to other robotic platforms. Furthermore, the internal sensors of quadruped robots acquire rich terrain-related data during locomotion across diverse terrains. This study investigates the relationship between terrain characteristics and quadruped robots based on proprioception sensor data, and proposes a simple, efficient, and motion-independent terrain classification method by integrating multiple sensor signals. The sensors referred to in the text only include the IMU sensor and joint encoders, which means that the method has a wide range of applicability while requiring sufficiently low hardware cost. The Convolutional Neural Network will serve as the backbone of the algorithm. In addition, the control command about its own control information will serve as supporting information to eliminate the impact of motion patterns on the results. Employing a multi-label classification algorithm, the complex terrains are classified by multiple physical feature labels like roughness, slippage, softness, and slope, which depict terrain attributes. A feature-labeled terrain dataset is established by abstracting diverse terrain features across various terrains. Unlike semantic labels (e.g., grassland, sand, gravel) that are comprehensible only to humans, feature labels provide a more helpful and precise terrain characterization, including broader terrain attributes.

To facilitate the integration of a fragrance encapsulation system into different products to achieve effective releases, a dual-responsive release system with pH and thermal trigger control is designed in this work. A series of ZIF-8 (M) and bilayer ZIF-8-on-ZIF-8 (MM) materials are synthesized by a solvent method at room temperature. The fragrance is encapsulated into the ZIFs by dynamic adsorption or in situ encapsulation combined dynamic adsorption. The fragrance loading contributed by dynamic adsorption was as high as 80%. The fragrance loaded in the double-layer MM host was almost twice that of the monolayer host M due to the stronger electrostatic interaction between MM and vanillin. In the pH and thermal trigger response release experiments, the second MOF layer in the MM host, as a controlled entity, greatly improved the load and kinetic equilibrium time of vanillin, and realized the controlled release of guest molecules. The developed dual-responsive release system in this work exhibits great potential in daily chemical products.