Flexible pressure sensors are essential components of electronic skins for future attractive applications ranging from human healthcare monitoring to biomedical diagnostics, robotic skins, and prosthetic limbs. Here we report a new kind of flexible pressure sensor. The sensors are capacitive, and composed of two Ag wrinkled electrodes separated by a carbon nanotubes (CNTs)/polydimethylsiloxane (PDMS) composite deformable dielectric layer. Ag wrinkled electrodes were formed by vacuum deposition on top of pre-strained and relaxed PDMS substrates which were treated using an O₂ plasma, a surface functionalization process, and a magnetron sputtering process. Ultimately, the developed sensor exhibits a maximum sensitivity of 19.80% kPa⁻¹ to capacitance, great durability over 500 cycles, and rapid mechanical responses (<200 ms). We also demonstrate that our sensor can be used to effectively detect the location and distribution of finger pressure.

Animals have certain cognitive competence about the environment so they can correct their navigation errors. Inspired by the excellent navigational behavior of animals, this paper proposes a brain-like navigation scheme to improve the accuracy and intelligence of Micro-Electro-Mechanical System based Inertial Navigation Systems (MEMS-INS). The proposed scheme employs vision to acquire external perception information as an absolute reference to correct the position errors of INS, which is established by analyzing the navigation and error correction mechanism of rat brains. In addition, to improve the place matching speed and precision of the system for visual scene recognition, this paper presents a novel place recognition algorithm that combines image scanline intensity (SI) and grid-based motion statistics (GMS) together which is named the SI-GMS algorithm. The proposed SI-GMS algorithm can effectively reduce the influence of uncertain environment factors on the recognition results, such as pedestrians and vehicles. It solves the problem that the matching result will occasionally go wrong when simply using the scanline intensity (SI) algorithm, or the slow matching speed when simply using grid-based motion statistics (GMS) algorithm. Finally, an outdoor Unmanned Aerial Vehicle (UAV) flight test is carried out. Based on the reference information from the high-precision GPS device, the results illustrate the effectiveness of the scheme in error correction of INS and the algorithm in place recognition.

Accurately obtaining the navigation information of the device is crucial for realizing various emerging Internet of Things (IoT) applications, and a multi-source fusion navigation system is the key to achieving this goal. A distributed integrated inertial navigation system (INS), polarization compass (PC), and geomagnetic compass (MAG) enhanced direction approach is presented to improve the accuracy and robustness of the multisource fusion navigation system in complex environments. To estimate the time-varying measurement noise covariance in a nonlinear multi-source fusion navigation system, the traditional federated Kalman filter (FKF) is improved. In the FKF framework, the third-order spherical radial cubature rule and variational Bayesian theory are introduced, and a variational Bayesian federated cubature Kalman filter (VBFCKF) is proposed. Furthermore, a distributed information monitoring and compensation algorithm based on residuals is developed to address issues like anomalous measured values and asynchronous multi-rate problems. Finally, an experimental platform for unmanned vehicle navigation is designed, and the tests are conducted to confirm the efficacy of the suggested approach. The experimental results show that the system can precisely estimate values based on the measurement quality of sub-filters during navigation. It effectively adjusts measurement noise covariance during updates, thereby mitigating the negative impact of interferences like occlusions and electromagnetic noise on the multi-source fusion navigation system in complex environments. This can strengthen the accuracy and robustness of the navigation system.

Autonomous velocity measurement technology based on optical flow plays an important role in applications of the Internet of Things. However, robust velocity measurement results need to provide a robust optical flow field and distance to the ground, especially for complex ground scenes. To solve this problem, the present paper proposes a visual velocity measurement method based on a 3 × 3 camera-array multi-aperture biomimetic compound-eye imaging system. The multi-aperture optical flow field is first obtained from compound-eye through multi-scale analysis and Bayes threshold processing. The real distance to the ground is then obtained by extracting the disparity information of nine apertures for adaptive depth estimation. The velocity measurement error is less than 0.6m/s in low-altitude (8 m and 13 m) flight scenarios with multiple obstacles.

Acoustic sensors with ultrahigh sensitivity, broadband response, and high resolution are essential for high-precision nondestructive weak signal detection technology. In this paper, based on the size effect of an ultrahigh-quality (Q) calcium fluoride (CaF₂) resonator, a weak acoustic signal is detected by the dispersive response regime in which an acoustic, elastic wave modulates the geometry and is converted to a resonance frequency shift. Through the structural design of the resonator, the sensitivity reaches 11.54 V/Pa at 10 kHz in the experiment. To our knowledge, the result is higher than that of other optical resonator acoustic sensors. We further detected a weak signal as low as 9.4 µPa/Hz^1/2, which greatly improved the detection resolution. With a good directionality of 36.4 dB and a broadband frequency response range of 20 Hz–20 kHz, the CaF₂ resonator acoustic sensing system can not only acquire and reconstruct speech signals over a long distance but also accurately identify and separate multiple voices in noisy environments. This system shows high performance in weak sound detection, sound source localization, sleep monitoring, and many other voice interaction applications.

Accurate and reliable navigation data is a key component in internet of things (IoT). High-precision and stable autonomous orientation has attracted considerable attention regarding environments in which the global navigation satellite system signal is unreliable. This study proposed a robust orientation method based on the atmospheric polarization mode called weather weighting sparse coding that includes weather classification, sparse coding and fitting. In comparison with previous studies, the proposed method is effective in restoring the angle of polarization image and achieving accurate orientation under complex weather. Specifically, to achieve high-precision orientation in complex weather, a polarization compass was designed, which used the characteristics of the different levels of destruction of atmospheric polarization images under different weather conditions for classification and denoising. The proposed strategy was used to process atmospheric polarization images obtained in complex weather. Experimental results showed that the orientation accuracy was better than 0.31∘ (Root Mean Square Error) under conditions of overcast, sandstorms, clear with overexposed areas, and smog with tree-obscured and overexposed areas.