Compound eyes found in insects provide intriguing sources of biological inspiration for miniaturised imaging systems. Here, we report an ultrathin arrayed camera inspired by insect eye structures for high-contrast and super-resolution imaging. The ultrathin camera features micro-optical elements (MOEs), i.e., inverted microlenses, multilayered pinhole arrays, and gap spacers on an image sensor. The MOE was fabricated by using repeated photolithography and thermal reflow. The fully packaged camera shows a total track length of 740 μm and a field-of-view (FOV) of 73°. The experimental results demonstrate that the multilayered pinhole of the MOE allows high-contrast imaging by eliminating the optical crosstalk between microlenses. The integral image reconstructed from array images clearly increases the modulation transfer function (MTF) by~1.57 times compared to that of a single channel image in the ultrathin camera. This ultrathin arrayed camera provides a novel and practical direction for diverse mobile, surveillance or medical applications.

Measuring the foot plantar pressure has the potential to be an important tool in many areas such as enhancing sports performance, diagnosing diseases, and rehabilitation. In general, the plantar pressure sensor should have robustness, durability, and high repeatability, as it should measure the pressure due to body weight. Here, we present a novel insole foot plantar pressure sensor using a highly sensitive crack-based strain sensor. The sensor is made of elastomer, stainless steel, a crack-based sensor, and a 3D-printed frame. Insoles are made of elastomer with Shore A 40, which is used as part of the sensor, to distribute the load to the sensor. The 3D-printed frame and stainless steel prevent breakage of the crack-based sensor and enable elastic behavior. The sensor response is highly repeatable and shows excellent durability even after 20,000 cycles. We show that the insole pressure sensor can be used as a real-time monitoring system using the pressure visualization program.

An algorithm has been developed for fusing 3D LIDAR (Light Detection and Ranging) systems that receive objects detected in deep learning-based image sensors and object data in the form of 3D point clouds. 3D LIDAR represents 3D point data in a planar rectangular coordinate system with a 360 • representation of the detected object surface, including the front face. However, only the direction and distance data of the object can be obtained, and point cloud data cannot be used to create a specific definition of the object. Therefore, only the movement of the point cloud data can be tracked using probability and classification algorithms based on image processing. To overcome this limitation, the study matches 3D LIDAR data with 2D image data through the fusion of hybrid level multi-sensors. First, because 3D LIDAR data represents all objects in the sensor's detection range as dots, all unnecessary data, including ground data, is filtered out. The 3D Random Sample Consensus (RANSAC) algorithm enables the extraction of ground data perpendicular to the reference estimation 3D plane and data at both ends through ground estimation. Classified environmental data facilitates the labeling of all objects within the viewing angle of 3D LIDAR based on the presence or absence of movement. The path of motion of the platform can be established by detecting whether objects within the region of interest are movable or static. Because LIDAR is based on 8-and 16-channel rotation mechanisms, real-time data cannot be used to define objects. Instead, point clouds can be used to detect obstacles in the image through deep learning in the preliminary processing phase of the classification algorithm. By matching the labeling information of defined objects with the classified object cloud data obtained using 3D LIDAR, the exact dynamic trajectory and position of the defined objects can be calculated. Consequently, to process the acquired object data efficiently, we devised an active-region-ofinterest technique to ensure a fast processing speed while maintaining a high detection rate.

Recently, unmanned surface vehicles (USV) are being actively developed. For USVs to be put to practical use, it is necessary to secure safety by preventing collisions with general ships. To this end, USVs must avoid opposing vessels based on international regulations for preventing collisions at sea (COLREGs, 1972). This paper proposes an algorithm for USVs to avoid collisions with opposing vessels based on COLREG rules. The proposed algorithm predicts dangerous situations based on distance to closest point of approach (DCPA) and time to closest point of approach (TCPA). It allows USVs to avoid opponent ships based on the dynamic window approach (DWA). The DWA has been improved to comply with COLREGs, and we implemented a simulation and compared the standard DWA with the COLREG-compliant DWA (CCDWA) proposed in this paper. The results confirm that the CCDWA complies with COLREGs.

Individuals with below-knee amputation (BKA) experience increased physical effort when walking, and the use of a robotic ankle-foot prosthesis (AFP) can reduce such effort. The walking effort could be further reduced if the robot is personalized to the wearer using human-in-the-loop (HIL) optimization of wearable robot parameters. The conventional physiological measurement, however, requires a long estimation time, hampering real-time optimization due to the limited experimental time budget. This study hypothesized that a function of foot contact force, the symmetric foot force-time integral (FFTI), could be used as a cost function for HIL optimization to rapidly estimate the physical effort of walking. We found that the new cost function presents a reasonable correlation with measured metabolic cost. When we employed the new cost function in HIL ankle-foot prosthesis stiffness parameter optimization, 8 individuals with simulated amputation reduced their metabolic cost of walking, greater than 15% (p < 0.02), compared to the weight-based and control-off conditions. The symmetry cost using the FFTI percentage was lower for the optimal condition, compared to all other conditions (p < 0.05). This study suggests that foot force-time integral symmetry using foot pressure sensors can be used as a cost function when optimizing a wearable robot parameter.