This paper addresses an observer-based output-feedback sampled-data controller design problem for nonlinear systems in Takagi-Sugeno (T-S) form including singular perturbations. The design condition is represented in terms of linear matrix inequalities. The separation principle is also investigated.

A Crimp Force Monitor (CFM) is equipment for detecting crimp errors by analyzing crimp signals obtained from force and strain sensors. The analysis is commonly performed by aligning a measured crimp signal with a reference signal and comparing their difference. Current analysis methods often suffer from wrong alignments that result in false negative detections. This paper presents a new crimp signal analysis method in CFM. First, a falling edge alignment is proposed that matches falling edges of the measured and the reference signals by minimizing the absolute difference summation. Second, a signal parameter error is introduced to evaluate the crimp quality difference between the measured signal and the reference. For calculating the signal parameter error, part of a signal is identified and divided into several regions to maximize the signal parameter errors. Experiments showed that the proposed method can improve the signal alignment and accurately detect bad crimps especially with the strain sensor.

A decentralized control method is proposed to enable a group of robots to achieve maximum performance in multisensory target tracking while avoiding collision with the target. The decentralized control was designed based on navigation function formalism. The study showed that the multiple agent system converged to the positions providing the maximum performance by the decentralized controller, based on Lyapunov and Hessian theory. An exemplary simulation was given for a multiple agent system tracking a stationary target.

In this paper, we propose a development method for a drowsiness detection system using retinex theory and edge information for vehicle safety. Detection of a drowsy state of a driver is very important because the drowsiness of driver is often the main cause of many car accidents. After acquiring an image of the entire face, we executed the pre-process step using the retinex theory. We then applied a technique for the detection of the white pixels using edge information. Experimental results showed that the proposed method improved the accuracy of detecting drowsiness to nearly 98%, and can be used to prevent a car accident caused by the driver's drowsiness.

Micro-electro-mechanical systems (MEMS) gyroscopes are widely used in various robot applications. However, these conventional gyroscopes need to vibrate the proof mass using a built-in actuator at a fixed resonance frequency to sense the Coriolis force. When a robot is not moving, the meaningless vibration of the gyroscope wastes power. In addition, this continuous vibration makes the sensor vulnerable to external sound waves with a frequency close to the proof-mass resonance frequency. In this paper, a feasibility study of a new type of gyroscope inspired by insect halteres is presented. In dipterous insects, halteres are a biological gyroscope that measures the Coriolis force. Wing muscles and halteres are mechanically linked, and the halteres oscillate simultaneously with wing beats. The vibrating haltere experiences the Coriolis force if the insect is going through a rotational motion. Inspired by this haltere structure, a gyroscope using a thin mast integrated with a robot actuation mechanism is proposed. The mast vibrates only when the robot is moving without requiring a separate actuator. The Coriolis force of the mast can be measured with an accelerometer installed at the tip of the mast. However, the signal from the accelerometer has multiple frequency components and also can be highly corrupted with noise, such that raw data are not meaningful. This paper also presents a suitable signal processing technique using the amplitude modulation method. The feasibility of the proposed haltere-inspired gyroscope is also experimentally evaluated.

Due to its simplicity and high reliability, the box-counting(BC) method is one of the most frequently used techniques to estimate the fractal dimensions of a binary image with a self-similarity property. The fractal calculation requires data sampling that determines the size of boxes to be sampled from the given image and directly affects the accuracy of the fractal dimension estimation. There are three non-overlapping regular grid methods: geometric-step method, arithmetic-step method and divisor-step method. These methods have some drawbacks when the image size M becomes large. This paper presents a BC algorithm for enhancing the accuracy of the fractal dimension estimation based on a new sampling method. Instead of using the geometric-step method, the new sampling method, called the coverage ratio-step method, selects the number of steps according to the coverage ratio. A set of experiments using well-known fractal images showed that the proposed method outperforms the existing BC method and the triangular BC method.

Coordinate estimation is an essential function for autonomous navigation of a mobile robot. The optical mouse sensor is convenient and cost-effective for the coordinate estimation problem. It is possible to overcome the position estimation error caused by the slip and the model mismatch of robot's motion equation using the optical mouse sensor. One of the simple methods for the position estimation using the optical mouse sensor is integration of the velocity data from the sensor with time. However, the unavoidable noise in the sensor data may deteriorate the position estimation in case of the simple integration method. In general, a mobile robot has ready-to-use motion information from the encoder sensors of driving motors. By combining the velocity data from the optical mouse sensor and the motion information of a mobile robot, it is possible to improve the coordinate estimation performance. In this paper, a coordinate estimation algorithm for an autonomous mobile robot is presented based on the well-known Kalman filter that is useful to combine the different types of sensors. Computer simulation results show the performance of the proposed localization algorithm for several types of trajectories in comparison with the simple integration method.

This paper proposes an approach to fused navigation of an unmanned surface vehicle(USV) and to detection of the outlier or interference of global positioning system(GPS). The method fuses available sensor measurements through extended Kalman filter(EKF) to find the location and attitude of the USV. The method uses error covariance of EKF for detection of GPS outlier or interference. When outlier or interference of the GPS is detected, the method excludes GPS data from navigation process. The measurements to be fused for the navigation are GPS, acceleration, angular rate, magnetic field, linear velocity, range and bearing to acoustic beacons. The method is tested through simulated data and measurement data produced through ground navigation. The results show that the method detects GPS outlier or interference as well as the GPS recovery, which frees navigation from the problem of GPS abnormality.

In this paper, we propose a mechanism that can compensate for gravity in a robot manipulator. Industry robots generate torque due to carrying heavy weight. For this reason, the robots need high specification motors, which increases the prices of the robots and their production costs. In order to resolve these problems, a mechanism for gravity compensation has been developed using a spring and wire system. But this system has problems related to wire stretching. A winding mechanism is therefore used to supplement this drawback of the wire. The robot used was developed by the 1-DOF system. Analysis was performed for the performance of the mechanism. Experiments were conducted to compare simulation results and experimental results.

In this paper, a robust visual odometry system has been proposed and implemented in an environment with dynamic illumination. Visual odometry is based on stereo images to estimate the distance to an object. It is very difficult to realize a highly accurate and stable estimation because image quality is highly dependent on the illumination, which is a major disadvantage of visual odometry. Therefore, in order to solve the problem of low performance during the feature detection phase that is caused by illumination variations, it is suggested to determine an optimal threshold value in the image binarization and to use an adaptive threshold value for feature detection. A feature point direction and a magnitude of the motion vector that is not uniform are utilized as the features. The performance of feature detection has been improved by the RANSAC algorithm. As a result, the position of a mobile robot has been estimated using the feature points. The experimental results demonstrated that the proposed approach has superior performance against illumination variations.

We suggest an efficient Simultaneous Localization and 3D Polygon Map Building (SLAM) method with Kinect depth sensor for mobile robots in indoor environments. In this method, Kinect depth data is separated into row planes so that scan line segments are on each row plane. After grouping all scan line segments from all row planes into line groups, a set of 3D Scan polygons are fitted from each line group. A map matching algorithm then figures out pairs of scan polygons and existing map polygons in 3D, and localization is performed to record correct pose of the mobile robot. For 3D map-building, each 3D map polygon is created or updated by merging each matched 3D scan polygon, which considers scan and map edges efficiently. The validity of the proposed 3D SLAM algorithm is revealed via experiments.

In this paper, we propose a novel method for estimating a vehicle's current position, even on roads that have similar patterns. In the proposed method, we classified the semantic information of the nodes in detail and added the semantic information of the link to solve the problem due to similar and repeated patterns. We also improved the mapping method by comparing the result of the duplicated matching with that of the only matching obtained just before corresponding duplicated matching. From the simulation results, we verify that the performance of the proposed method is better than that of the existing method.

This paper presents the principle, dynamics modeling and control, hardware implementation, and flight test result of a hybrid-type unmanned aerial vehicle (UAV). The proposed UAV was designed to provide both hovering and fixed-wing type aerodynamic flight modes. The UAV's flight mode transition was achieved through the attitude transformation in pitch axis, which avoids a complex rotor tilt mechanism from a structural and control viewpoint. To achieve this, a different navigation coordinate was introduced that avoids the gimbal lock in pitch singularity point. Attitude and guidance control algorithms were developed for the flight control system. For flight test purposes, a quadrotor attached with a tailless fixed-wing structure was manufactured. An onboard flight control computer was designed to realize the navigation and control algorithms and the UAV's performance was verified through the outdoor flight tests.

Recent advances in networking and computers, especially internet of things (IoT) technologies, have improved the quality of home life and industrial sites. However, the security vulnerability of IoT technologies causes life-threatening issues and information leakage concerns. Studies regarding security algorithms are being conducted. In this paper, we proposed SEED algorithms based on one time passwords (OTPs). The specified server sent time data to the client every 10 seconds. The client changed the security key using time data and generated a ciphertext by combining the changed security key and the matrix. We applied the SEED algorithms with enhanced security to Linux-based embedded boards and android smart phones, then conducted a door lock control experiment (door lock & unlock). In this process, the power consumed for decryption was measured. The power consumption of the OTP-based algorithm was measured as 0.405-0.465W. The OTP-based algorithm didn't show any difference from the existing SEED algorithms, but showed a better performance than the existing algorithms.

In this paper, a novel multi-object tracking method to track an unknown number of objects is proposed. To handle multiple object states and uncertain observations efficiently, a probability hypothesis density (PHD) filter is adopted and modified. The PHD filter is capable of reducing false positives, managing object appearances and disappearances, and estimating the multiple object trajectories in a unified framework. Although the PHD filter is robust in cluttered environments, it is vulnerable to false negatives. For this reason, we propose to exploit local observations in an RFS of the observation model. Each local observation is generated by using an online trained object detector. The main purpose of the local observation is to deal with false negatives in the PHD filtering procedure. The experimental results demonstrated that the proposed method robustly tracked multiple objects under practical situations.