• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.11
• /
• pp.1008-1016
• /
• 2015

This paper proposes a method for detecting the front side of vehicles. The method can find the car side with a license plate even with complicated and cluttered backgrounds. A convolutional neural network (CNN) is used to solve the detection problem as a unified framework combining feature detection, classification, searching, and localization estimation and improve the reliability of the system with simplicity of usage. The proposed CNN structure avoids sliding window search to find the locations of vehicles and reduces the computing time to achieve real-time processing. Multiple responses of the network for vehicle position are further processed by a weighted clustering and probabilistic threshold decision method. Experiments using real images in parking lots show the reliability of the method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.8
• /
• pp.719-724
• /
• 2012

A traffic detection system is a device that collects traffic information around an intersection. Most existing traffic detection systems provide very limited traffic information for signal control due to the restriction of vehicle detection area. A signal control scheme determines the transition among signal phases and the time that a phase lasts for. However, the existing signal control scheme do not resolve the traffic congestion effectively since they use restricted traffic information. In this paper, a new traffic detection system with a zone division signal control scheme is proposed to provide correct and detail traffic information and decrease the vehicle's waiting time at the intersection. The traffic detection system obtains traffic information in a way of vehicle-to-roadside communication between vehicles and sensor network. A new signal control scheme is built to exploit the sufficient traffic information provided by the proposed traffic detection system efficiently. Simulation results show that the proposed signal control scheme has 121 % and 56 % lower waiting time and delay time of vehicles at an intersection than other fuzzy signal control scheme.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.5
• /
• pp.429-435
• /
• 2011

This paper presents a sensor fusion-based estimation of heading and a Bezier curve-based motion planning for unmanned ground vehicle. For the vehicle to drive itself autonomously and safely, it should estimate its pose with sufficient accuracy in reasonable processing time. The vehicle should also have a path planning algorithm that enables to adapt to various situations on the road, especially at intersections. First, we address a sensor fusion-based estimation of the heading of the vehicle. Based on extended Kalman filter, the algorithm estimates the heading using the GPS, IMU, and wheel encoders considering the reliability of each sensor measurement. Then, we propose a Bezier curve-based path planner that creates several number of path candidates which are described as Bezier curves with adaptive control points, and selects the best path among them that has the maximum probability of passing through waypoints or arriving at target points. Experiments under various outdoor conditions including at intersections, verify the reliability of our algorithm.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.2
• /
• pp.164-171
• /
• 2010

We have studied the port-automation system which is requested by the steep increment of cost and complexity for processing the freight. This paper will introduce a new algorithm for navigating and controlling the autonomous Guided Vehicle (AGV). The camera has the optical distortion in nature and is sensitive to the external ray, the weather, and the shadow, but it is very cheap and flexible to make and construct the automation system for the port. So we tried to apply to the AGV for detecting and tracking the lane using the CCD camera. In order to make the error stable and exact, this paper proposes new concept and algorithm for obtaining the error is generated by the Virtual Photo-Sensor Array (VPSA). VPSAs are implemented by programming and very easy to use for the various autonomous systems. Because the load of the computation is light, the AGV utilizes the maximal performance of the CCD camera and enables the CPU to take multi-tasks. We experimented on the proposed algorithm using the mobile robot and confirmed the stable and exact performance for tracking the lane.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.12
• /
• pp.1046-1052
• /
• 2016

For the safe driving of autonomous vehicles, accurate position estimation is required. Generally, position error must be less than 1m because of lane keeping. However, GPS positioning error is more than 1m. Therefore, we must correct this error and a map matching algorithm is generally used. Especially, road marking intensity map have been used in many studies. In previous work, 3D LIDAR with many vertical layers was used to generate a local intensity map. Because it can be obtained sufficient longitudinal information for map matching. However, it is expensive and sufficient road marking information cannot be obtained in rush hour situations. In this paper, we propose a localization algorithm using an accumulated intensity local map. An accumulated intensity local map can be generated with sufficient longitudinal information using 3D LIDAR with a few vertical layers. Using this algorithm, we can also obtain sufficient intensity information in rush hour situations. Thus, it is possible to increase the reliability of the map matching and get accurate position estimation result. In the experimental result, the lateral RMS position error is about 0.12m and the longitudinal RMS error is about 0.19m.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.12
• /
• pp.995-1001
• /
• 2016

The vehicle suspension system plays a very important part related with vehicle ride and handling. To improve the vehicle ride and handling many researches have been progressed from various damping parameter tuning techniques to the development of the electronic controlled suspension systems. In this paper, as one of the ride performance improvement a disturbance observer(DOB) based control system is applied to the quarter car vehicle model in order to show that the DOB can obtain good vibration isolation characteristics. First, the robust stability criterion for the DOB is introduced in detail, and then how DOB is applied to the 1/4 car vehicle model is represented, and finally to confirm the effectiveness of the DOB in vehicle ride performance improvement a computer simulation is carried out for various driving conditions.

• The Journal of Korea Robotics Society
• /
• v.19 no.1
• /
• pp.79-91
• /
• 2024

Large warehouses are building automation systems to increase efficiency. However, small warehouses, military bases, and local stores are unable to introduce automated logistics systems due to lack of space and budget, and are handling tasks manually, failing to improve efficiency. To solve this problem, this study designed small loading and unloading equipment that can be mounted on transportation vehicles. The equipment can be controlled remotely and is automatically controlled from the point where pallets loaded with cargo are visible using real-time video from an attached camera. Cargo recognition and control command generation for automatic control are achieved through a newly designed deep learning model. This model is designed to be optimized for loading and unloading equipment and mission environments based on the YOLOv3 structure. The trained model recognized 10 types of palettes with different shapes and colors with an average accuracy of 100% and estimated the state with an accuracy of 99.47%. In addition, control commands were created to insert forks into pallets without failure in 14 scenarios assuming actual loading and unloading situations.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.44 no.3
• /
• pp.64-69
• /
• 2007

Motion control of the system is a position control of motor. Motion control of an uncertain robot system is considered as one of the most important and fundamental research directions in the robotics. Some distinguished works using linear control, adaptive control, robust control strategies based on computed torque methodology have been reported. However, it is generally recognized within the control community that these strategies suffer from the following problems : the exact robot dynamics are needed and hard to implement, the adaptive control cannot guarantee the performance during the transient period for adaptation under the variation, the robust control algorithms such as the sliding mode control need information on the bounds of the possible uncertainty and disturbance. And it produces a large control input as well. In this dissertation, a motion control for the unmanned intelligent robot system using disturbance observer is studied. This system is affected with an impact vibration disturbance. This paper describes a stable motion control of the system with the consideration of external disturbance. To obtain the stable motion independently against the external disturbance, the disturbance rejection is strongly required. To address the above issue, this paper presents a Disturbance OBserver(DOB) control algorithm. The validity of the suggested DOB robust control scheme is confirmed by several computer simulation results. And the experiments with a motor system is performed to give the validity of applicability in the industrial field. This results make the easier implementation of the controller possible in the field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.229-231
• /
• 2018

As autonomous vehicle technology has been gradually developed, robots that have introduced autonomous navigation systems have been actively involved in areas where there is a lot of livelihoods such as industrial sites and accident sites. For this reason, the unmanned transportation system equipped with the autonomous traveling system is widely used in harmful environments where human access is difficult. In addition, the introduction of the autonomous driving system reduces the collision and casualties that occur in a mobility environment like the industrial field, and it helps the efficient work process. In addition, autonomous driving vehicles can be handled more safely and quickly in a wider area by transmitting the surrounding environment of each vehicle to a server connected to each autonomous driving vehicle and passing it through the main server. In this paper, by utilizing V2X communication for autonomous unmanned forklift system, it can increase industrial workload, reduce loss of life and damage to property through wide area forklifts.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.1
• /
• pp.33-39
• /
• 2017

In mission-critical applications such as vehicular networks, distributed robotics, and other cyber-physical systems, the requirements for latency are more stringent than traditional applications. Among them, autonomous V2V communication is a rapidly emerging domain of applications with a few milliseconds' latency requirements. Today's systems utilizing 802.11p or LTE-direct standards are not primarily designed for ultra-low latency. Because the medium access function contributes to a significant portion of the total latency, it is necessary to modify Layer2 in order to solve the problem. Focusing on MAC layer, we developed a scalable and latency-guaranteed MAC by devising Autonomous TDMA (ATDMA) in which autonomous joining/leaving is allowed without scheduling by coordinator. We also evaluated the performance of the algorithm by comparing with the WAVE protocol.