In this study, we propose a reconfigurable laminate mechanism based bistable tail for Throwbot transforming into a ball type and a wheel type. Various robots such as snake robots, drones, and throwing robots for life-saving missions on behalf of humans at disaster sites have been studied. In particular the hybrid type throwing robot can have both the throwing ease of the ball type and the driving stability of the wheel type. However, it requires the tail to be stored inside when being thrown and to be rigidly deployed when driving. To satisfy these requirements, we developed a foldable tail based on scissor lift structure in our previous study. But, such a structure was composed of only rigid parts, which caused interference with other parts when stored, and difficulty about changing the maximum deployed tail length further. To overcome these limitations, we wanted to develop a bistable tail suitable for the hybrid type that can maintain a bendable state and a rigid state. Before actual development, we calculate the minimum tail length for overcoming obstacle through statics analysis. Then, we design a bistable structure utilizing a reconfigurable laminate mechanism. Next, we calculate the design constraints to mount it on the actual robot. Finally, the developed tail is mounted on the actual Throwbot to perform obstacle overcoming experiments. We confirm that it can secure both ease throwing and stable obstacle overcoming ability. Through this, we propose a bistable tail suitable for the hybrid type throwing robots.

In this paper, we have developed a protection module for Light Detection and Ranging (LiDAR) sensors used in outdoor unmanned vehicles. Bio-inspired wiping motion was figured to have more efficient and excellent wiping performance than conventional cleaning methods for LiDAR sensors. An water wiping experiment confirmed that the finger wiping motion removed 35% more water than the translational wiping motion. Also, the theoretical analysis for the existence of an optimal rotational speed at maximum wiping performance was verified to be consistent with the experiment. The LiDAR distortion experiment results demonstrated no data distortion, showing an average error of up to 0.40% for detecting obstacles even when the acrylic cover rotates. Finally, a contamination protection experiment was conducted for water, powder, soil, and mud. As a result, although there was a change in the number of pointcloud and a decrease in the intensity of the sensor data after contamination, it was validated that the number of pointclouds and average intensity of data could be restored to at least 97% and 67% after being cleaned.

In this study, a throwing robot equipped with an shock absorbing structure, utilizing paired-Cross Flexural Hinge (p-CFH) and an airbag, was fabricated and validated to assess the effectiveness of its impact absorption mechanism. This robot was developed in anticipation of situations where direct human intervention for life rescue would be challenging. Throwing robots can be broadly categorized into ball type, wheel type, and hybrid type. The hybrid type combines the advantages of both: the ease of throwing from ball type, due to its low air resistance coefficient, and the versatile mobility of the wheel type in diverse environments. However, hybrid type throwing robots are more vulnerable to external impacts due to the complexity of their internal structure, resulting in a lower maximum drop height compared to wheel type robots.To address these challenges, this research proposes a the Throwbot that combines the easy throwing capability of ball type with the obstacle overcoming ability of the wheel type, while also addressing the low free fall height drawback inherent in hybrid types. To achieve this, we developed a Throwbot with a ball to wheel transform structure, p-CFH mechanism, and airbag based impact absorption system. Additionally, materials were selected based on simulation results to refine the Throw-bot. The performance of the proposed robot was evaluated through various assessments, including free fall experiments and obstacle overcoming tests. Through this research, the proposed Throwbot effectively addresses the shortcomings of existing throwing robots, establishing a novel approach to throwing robot design.

Multi-pixel image sensors such as Tetra-Cell image sensor have been developed in recent years, and its pixel size is becoming smaller with high resolution. As the amount of pixel data being transmitted has increased, it consumes significantly higher power in the imaging system. Although many image compression algorithms have been studied to reduce the amount of pixel data, it comes with high-complexity, focuses on Bayer pattern, and even has challenges of geometrical distances on Tetra-Cell pattern. In this paper, we propose Multi-Pixel Compression (MPC) which can provide high image quality as well as high compression ratio on Tetra-Cell pattern. We also propose low-power imaging system with MPC. In the proposed imaging system, the power consumption of MIPI transmission and DRAM access can be reduced by 75%. The proposed MPC architecture is implemented as ASIC, its encoder design achieves energy efficiency 1.94 Gb/s/mW and 1.29 Gb/s/mW on Bayer and Tetra-Cell pattern image, respectively, and it achieves 57.11dB PSNR, thus outperforming related previous work.

This paper presents a low-power stacked CMOS image sensor (CIS) in 65/14nm process. With 14nm process, we could achieve 29% less power consumption than the conventional CIS in 65/28nm process. The measured random telegraph noise (RTN) result shows the 65/14nm stacked CIS to guarantee the commercial sensor image quality even though a fin field-effect transistor (FinFET) process is three-dimension channel structure. The pixel array of the implemented chip consists of 12-mega pixels (Mp) with a dual-photodiode (2PD) in 1.4μm pixel pitch, and the sensor output provides 120 frames per second while it consumes 612mW for 12Mp image and 3Mp auto-focus data via a mobile industry processor interface (MIPI) physical layer (DPHY) up to 6.5Gbps/lane. The measured random noise is 2.2e-at 16× analog gain, and figure-of-merit (FoM) of analog-to-digital converters (ADCs) achieves 0.46e-nJ.

A human eye has the logarithmic response over wide range of light intensity. Although the gain can be set high to identify details in darker area on the image, this results in saturation in brighter area. The gamma correction is essential to fit the human eye. However, the digital gamma correction degrades image quality especially for darker area on the image due to the limited ADC resolution and the dynamic range. This paper proposes a CMOS image sensor (CIS) with nonlinear analog-to-digital converter (ADC) which performs analog gamma correction. The CIS with the proposed nonlinear ADC conversion scheme was fabricated with a 0.35-/spl mu/m CMOS process. The test results show the improved image quality than digital gamma correction.