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To achieve accurate pose estimation for robots in industrial applications and services, this brief proposes an effective feature-based downward-facing monocular visual odometry technology that uses an affordable sensor system and a systematic optimization approach. To extract more effective features simply and efficiently from images of the ground, even for small mobile systems, the proposed visual odometry system is designed in a lightweight and cost-effective manner; we used an easily available LED, a single-channel time-of-flight (ToF) sensor, and a monocular camera. From the extracted features, the potentially irrelevant ones are removed in advance, using a masking algorithm and measured velocity. This enhances feature efficiency and reduces the computational burden. Finally, the optimal pose estimate is explicitly obtained by solving a nonconvex optimization problem, to make the best use of the features. The experiments’ results show that our proposed method improves feature tracking ability and pose estimation accuracy.

Lidar and radar sensors are widely used to obtain depth information for various applications in the field of robotics, such as navigation [1], collision avoidance [2], surveillance [3], and map generation [4]. These two sensors are becoming increasingly popular as perception systems for autonomous mobile robots. However, as a result of their versatility and popularity, lidar and radar sensors come in a variety of specifications, sizes, and prices. Consequently, it has become essential to quantitatively evaluate these two sensors from various situational perspectives. This is required for a comprehensive selection of the most appropriate sensors or combinations thereof for the target applications.

Following the recent trend of weight reduction in car industry, producing high quality cold-rolled AHSS (advanced high-strength steel) strip becomes important. Thickness hunting (or fluctuation) problem can be more prominent for this cold-rolled AHSS strip making, which can stem from the non-uniformity of hot-rolled strip and can severely degrade product quality. In this paper, we propose a novel framework to estimate the strip-longitudinal hardness of the TCM (tandem cold mill) process and its feedforward control to substantially reduce the thickness hunting, while fully incorporating the interconnected nature and sensing sparsity of the TCM process. In particular, our estimator consists of the following two complementary loops: 1) fast real-time hardness estimation loop, which optimally fuses the process model and sensing information; and 2) slower constant process-parameter estimation loop via optimization utilizing the nonlinear process model and (stored/measured) sensor data. Efficacy of the proposed estimation and control frameworks are then validated with high-precision TCM process physics simulator.

Abstract: The Korean Railroad Research Institute (KRRI) developed the rubber-tired AGT system (Model: K-AGT) between 1999 and 2005. The K-AGT is a light rail transit system does not require a driver and generally operates on an elevated railroad for transporting passengers. Accidents caused by driverless vehicles can severely affect social confidence, safety and economy. Therefore, it is very important to minimize the occurrences of such faults, and to accurately perform detailed maintenance tasks and thoroughly investigate the cause of any repeated failures. This research develops the web-based reliability centered maintenance (RCM) system for the K-AGT train system. The framework of the RCM system is based on performing a failure mode and effects analyses (FMEA) procedure on all the sub-systems in the K-AGT system. Out of the devices that have a low reliability, the high failure ranked devices are included high on the list for performing the overall maintenance plans. Through registration of historical failure data and the reliability indexes, the results of the FMEA can be updated. Such a process is repeated continuously and can achieve very accurate predictions for device operational lifetimes and failure rates. Also, the RCM system is designed so that workers can refer to the expert system for the latest procedures to perform the required diagnosis and repair of any failure. The overall RCM system consists of a failure/task management system, a preventive maintenance system, an expert system, a material management system, and an approval system. This research describes the development of the preventive maintenance system and the expert system that have been produced because these are the main functions for the RCM system.