Chi-Won Roh

A Lidar-Based Decision-Making Method for Road Boundary Detection Using Multiple Kalman Filters

In this paper, a novel decision-making method is proposed for autonomous mobile robot navigation in an urban area where global positioning system (GPS) measurements are unreliable. The proposed method uses lidar measurements of the roads surface to detect road boundaries. An interacting multiple model method is proposed to determine the existence of a curb based on a probability threshold and to accurately estimate the roadside curb position. The decision outcome is used to determine the source of references suitable for reliable and seamless navigation. The performance of the decision-making algorithm is verified through extensive experiments with a mobile robot autonomously navigating through campus roads with several intersections and unreliable GPS measurements. Our experimental results demonstrate the reliability and good tracking performance of the proposed algorithm for autonomous urban navigation.

Outdoor Navigation of a Mobile Robot Using Differential GPS and Curb Detection

This paper demonstrates a reliable navigation of a mobile robot in outdoor environment. We fuse differential GPS and odometry data using the framework of extended Kalman filter to localize a mobile robot. And also, we propose an algorithm to detect curbs through the laser range finder. An important feature of road environment is the existence of curbs. The mobile robot builds the map of the curbs of roads and the map is used for tracking and localization. The navigation system for the mobile robot consists of a mobile robot and a control station. The mobile robot sends the image data from a camera to the control station. The control station receives and displays the image data and the teleoperator commands the mobile robot based on the image data. Since the image data does not contain enough data for reliable navigation, a hybrid strategy for reliable mobile robot in outdoor environment is suggested. When the mobile robot is faced with unexpected obstacles or the situation that, if it follows the command, it can happen to collide, it sends a warning message to the teleoperator and changes the mode from teleoperated to autonomous to avoid the obstacles by itself. After avoiding the obstacles or the collision situation, the mode of the mobile robot is returned to teleoperated mode. We have been able to confirm that the appropriate change of navigation mode can help the teleoperator perform reliable navigation in outdoor environment through experiments in the road.

Autonomous Navigation of KUVE (KIST Unmanned Vehicle Electric)

This article describes the system architecture of KUVE (KIST Unmanned Vehicle Electric) and unmanned autonomous navigation of it in KIST. KUVE, which is an electric light-duty vehicle, is equipped with two laser range finders, a vision camera, a differential GPS system, an inertial measurement unit, odometers, and control computers for autonomous navigation. KUVE estimates and tracks the boundary of road such as curb and line using a laser range finder and a vision camera. It follows predetermined trajectory if there is no detectable boundary of road using the DGPS, IMU, and odometers. KUVE has over 80% of success rate of autonomous navigation in KIST.

Development of Autonomous Navigation Robot in Outdoor Road Environments

This paper discusses an autonomous navigation system for urban environments. For the localization of the robot, EKF (Extended Kalman Filter) algorithm is used with odometry, angle sensor, and DGPS (Differential Global Positioning System) measurement. Especially in an urban environment, DGPS is often blocked by buildings and trees and the resulting inaccurate positioning prevents the robot from safe and reliable navigation. In addition to the global information from DGPS, the local information of the curb on the roadway is used to track a route when the global DGPS information is inaccurate. For this purpose, curb detection algorithm is developed and implemented in the developed navigation algorithm. Four different types of navigation strategies are developed and they are switched to adapt to different localization conditions according to the availability of DGPS and the existence of the curbs on the roadway. The experimental results show that the designed switching strategy improves the navigation performance adapting to the environment conditions.

Development of Patrol Robot using DGPS and Curb Detection

This paper demonstrates the development of a mobile robot for patrol. We fuse differential GPS, angle sensor and odometry data using the framework of extended Kalman filter to localize a mobile robot in outdoor environments. An important feature of road environment is the existence of curbs. So, we also propose an algorithm to find out the position of curbs from laser range finder data using Hough transform. The mobile robot builds the map of the curbs of roads and the map is used fur tracking and localization. The patrol robot system consists of a mobile robot and a control station. The mobile robot sends the image data from a camera to the control station. The remote control station receives and displays the image data. Also, the patrol robot system can be used in two modes, teleoperated or autonomous. In teleoperated mode, the teleoperator commands the mobile robot based on the image data. On the other hand, in autonomous mode, the mobile robot has to autonomously track the predefined waypoints. So, we have designed a path tracking controller to track the path. We have been able to confirm that the proposed algorithms show proper performances in outdoor environment through experiments in the road.

Sensor fusion-based line detection for unmanned navigation

We propose an algorithm of reliable detection of line for unmanned navigation of mobile robots using sensor fusion. To detect the distance and the angle between the robot and the line, we use a vision sensor system and a laser range finder (LRF). Each sensor system runs its own extended Kalman filter (EKF) to estimate the distance and orientation of the line. The vision system processes images being captured using well-known edge detection algorithms, and the LRF detects the line using the measurement of the intensity of the laser beam reflected. However, depending on the condition of the road and ambient light, each sensor gives us wrong measurement of the line or sometimes completely fails to detect it. To resolve such uncertainty, we develop a simple and easy-to-implement sensor fusion algorithm that uses weighted sum of the output of each EKF, and it gives us more reliable estimate of the distance and orientation of the line than each measurement/estimator system.

A Hybrid Autonomous / Teleoperated Strategy for Reliable Mobile Robot Outdoor Navigation

This paper demonstrates a reliable navigation strategy of a mobile robot teleoperated in outdoor environment. The navigation system for the teleoperated mobile robot consists of a mobile robot and a control station. The mobile robot sends the image data from a camera to the control station. The control station receives and displays the image data and the teleoperator commands the mobile robot based on the image data. Since the image data does not contain enough data for reliable teleoperation, a hybrid autonomous/teleoperated strategy for reliable mobile robot in outdoor environment is suggested. When the mobile robot is faced with unexpected obstacles or the situation that, if it follows the command, it can happen to collide, it sends a warning message to the teleoperator and changes the mode from teleoperated to autonomous to avoid the obstacles by itself. After avoiding the obstacles or the collision situation, the mode of the mobile robot is returned to teleoperated mode. And also, we fuse differential GPS and odometry data using the framework of extended Kalman filter to localize the mobile robot. We have been able to confirm that the appropriate change of navigation mode can help the teleoperator perform reliable navigation in outdoor environment through experiments in the roadway

A fuzzy decision making algorithm for safe driving in urban environment

Safe and autonomous driving is one of the most important challenges in mobile robotics and has been received considerable attention over the past years in indoor and outdoor navigations. Most methods developed so far immediately activate an obstacle avoidance algorithm when a robot meets obstacles without predicting the motion of the obstacle. These methods would be inefficient for the navigation in urban environments with traffic lane because the traffic lane becomes a constraint in the robot motion. For the safe driving in urban environments, it is efficient to consider this constraint before performing an obstacle avoidance algorithm in the planning phase when the robot meets an obstacle. Therefore, a decision making algorithm for safe driving in case of navigating on the road is needed. In terms of its simplicity and its short response time, a fuzzy algorithm is especially suitable for real-time applications. In this paper, we propose a fuzzy-based decision making algorithm for the outdoor navigation of mobile robots. The algorithm is tested in crossroad environment. To satisfy the robots safety requirements and to spend less time on the intersection, we designed our navigation algorithm consists of two primary parts: perception (understanding environment) and decision making part. This paper focuses on the decision making part. Simulation results show the algorithms effectiveness.

Experiments of Urban Autonomous Navigation using Lane Tracking Control with Monocular Vision

Autonomous Lane detection with vision is a difficult problem because of various road conditions, such as shadowy road surface, various light conditions, and the signs on the road. In this paper we propose a robust lane detection algorithm to overcome shadowy road problem using a statistical method. The algorithm is applied to the vision-based mobile robot system and the robot followed the lane with the lane following controller. In parallel with the lane following controller, the global position of the robot is estimated by the developed localization method to specify the locations where the lane is discontinued. The results of experiments, done in the region where the GPS measurement is unreliable, show good performance to detect and to follow the lane in complex conditions with shades, water marks, and so on.

Design of an autonomous antarctic exploration platforms through manipulations of All-Terrain Vehicles

In Antarctica, there exists many crevasses which can be greatest risks to Antarctica exploration crew. In order to protect the scientists from these hazards, a robotic platform is being developed which can detect dangerous crevasses hidden below the surface. This paper presents such an effort to develop an unmanned vehicle by modifying a commercially available All Terrain Vehicle (ATV). The developed platform be driven autonomously given the user provided GPS trajectories in a harsh Antarctic environments. In order to provide such capabilities, several manipulators are designed on the throttle, break, steering wheel and gears of the ATV, each controlled by embedded motor drivers. The command to motor drivers are generated from the embedded controllers which can communicate wirelessly to either user command station or handheld remote controllers. This redundancy in the user input can enhance safety in an emergency situation. The developed platform is tested in many different experimental scenarios similar to the Antarctic exploration.