Hwan-Joo Kwak

Improvement of the inertial sensor-based localization for mobile robots using multiple estimation windows filter

The shortcomings of inertial sensor-based localization for mobile robots are discussed and a method to improve the performance of localization is introduced. Because dead reckoning using wheel speed sensors is insufficient for accurate motion estimation in uneven and slippery environments, many studies have used inertial sensors in order to improve the performance of localization. However, inertial sensor-based localization also has inherent problems caused by an accumulation of sensor errors. To improve the performance of inertial sensor-based localization for mobile robots, a method is suggested that involves the reduction of unexpected noise of inertial sensor using a Multiple Estimation Windows (MEW) filter. The main concept of the MEW filter is to estimate the long-term signal of interest using a single long-term moving window and multiple short-term moving windows. The proposed denoising method can reduce the noise of all frequencies without the loss of information from the signal of interest. The performance improvement of the mobile robots localization using the MEW filter is confirmed experimentally.

Image contrast enhancement for intelligent surveillance systems using multi-local histogram transformation

Among all applications to monitor the safety and security of working environments, surveillance systems that use computer vision are the most efficient and intuitive in the manufacturing industry. This paper introduces a new technique of contrast enhancement for surveillance systems using computer vision. The histogram equalization method is a common and widespread image enhancement method which maximizes the contrast of the image. This contrast enhancement method usually improves the quality of images, but it can suffer from visual deterioration caused by excessive histogram modification. To overcome the limitations of conventional contrast enhancement methods, this paper introduces a new multi-local histogram transformation method for surveillance systems. This technique is based on the local histograms, which are separated from the overall histogram of the image, and the contrast of the image can be enhanced through two major processes: range reassignment of local histograms and local histogram equalization. The multi-local histogram transformation in this paper enhances the contrast of images, preventing excessive compression and extension of image histograms. The performance of the suggested contrast enhancement method is verified by the experiments in four different environments.

Development and Application of Three-axis Motion Rate Table for Efficient Calibration of Accelerometer and Gyroscope

This paper introduces a simple and efficient calibration method for three-axis accelerometers and three-axis gyroscopes using three-axis motion rate table. Usually, the performance of low cost MEMS-based inertial sensors is affected by scale and bias errors significantly. The calibration of these errors is a bothersome problem, but the previous calibration methods cannot propose simple and efficient method to calibrate the errors of three-axis inertial sensors. This paper introduces a new simple and efficient method for the calibration of accelerometer and gyroscope. By using a three-axis motion rate table, this method can calibrate the accelerometer and gyroscope simultaneously and simply. Experimental results confirm the performance of the proposed method.

Efficient dissemination method for traffic jams information sharing based on inter-vehicle communication

In an Intelligent Transport System (ITS), data dissemination based on inter-vehicle communication is effective for acquiring real-time traffic jams information. In this paper, we propose a novel method for traffic jams information dissemination in vehicular ad-hoc networks. In our proposed method, vehicles already trapped in a traffic jams elect leaders according to their locations from upstream and downstream respectively. Then, leaders generate traffic data which contains their position, velocity and counter respectively, and disseminate the information. Then, the data of the traffic jams can be disseminated to vehicles which do not trapped to the traffic jams.

Module-based efficient self-collision detection method for humanoid robots

This paper suggests an efficient and robust self-collision detection for humanoid robots. The self-collision detection is essential and important for the safe and reliable operations of humanoid robots. In addition, low computation time of the self-collision detection must be guaranteed for realtime operation of the humanoid robots. The main concepts of this paper are based on simplifying the structure of the robot and reducing the computational cost using the eliminations of neighbor modules and collision-free modules. The operational reliance and efficiency of the suggested self-collision detection are checked by the practical simulation and experiment of the humanoid robot.

A New Fuzzy Inference Technique for Singleton Type-2 Fuzzy Logic Systems

A new fuzzy inference technique is presented to replace the conventional fuzzy inference process of type-2 fuzzy logic systems. Because conventional type-2 fuzzy logic systems demand a large amount of memory, they cannot be used by most embedded systems, which do not have enough memory space. To overcome this problem, a new fuzzy inference technique for singleton type-2 fuzzy logic systems is presented in this paper which designs mapping functions from input variables to firing sets and brings out the firing sets directly without using as much memory.

Design of adaptive PD controller for weight independent target tracking of home service mobile robot

The previous researches for mobile robots are based on the model of mobile robot with invariant weight parameters. However, the most practical mobile robots that bring heavy burden have variant weight parameters: robots weight and center of gravity. This paper suggests the design of adaptive PD controller, which estimates and adapts the weight parameters of the robots, for a home service mobile robot that brings varying heavy burden. Using the suggested adaptive PD controller, the motion of mobile robots can be independent of the weight parameters, and the operational performance is confirmed by the simulations of target tracking.

Stable walking of humanoid robot by using landing control in various environments

Ensuring stability is one of the most important themes of humanoid robot, because humanoid robot inherently suffers from instability. And the stable landing is the first step in various environments for the stable walking. However, it is difficult to obtain ground information in landing. So, we propose the stable walking of humanoid robot by using landing control in various environments. Landing control needs to satisfy constraints of dynamic stability, ground conditions and various feet motions to fit the each case in various environments. In most of the previous researches on landing control, robot equips foot with special mechanical equipments to obtain environment conditions. However in this paper, the various environments can be recognized with only FSRs (Force Sensing Resistors). The proposed method is evaluated through walking on an ascent with a 19 DOF (Degree of Freedom) biped robot model.

Design of an Adaptive PD Controller for the Weight-Independent Motion Control of a Mobile Robot

Previous research on mobile robots has been based on models of mobile robots with invariant weight parameters. However, the most practical mobile robots that bear heavy burdens have varying weights and centres of gravity. This paper suggests a design for an adaptive PD controller that estimates and adapts to the weight-related parameters of robots that bear varying heavy burdens. Using the suggested adaptive PD controller, the motion of mobile robots can be independent of the weight-related parameters and the operational performance is confirmed by target tracking simulations.