• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.275-284
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• 2006

This paper deals with problems of safe and efficient navigation algorithm for autonomous mobile robots in unknown environments. Since the obstacle avoidance algorithms are very important in mobile robot navigation, two obstacle avoidance algorithms: VFH(vector field histogram) algorithm and a fuzzy algorithm are combined to have optimal performance in various environments. And a upper-level supervisor is to select the proper one from VFH algorithm and the fuzzy algorithm according to the situations the robot faces. Computer simulation results show the effectiveness of the proposed navigation algorithm for autonomous mobile robots.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.3
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• pp.132-138
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• 2011

This paper presents an intelligent control approach for lateral position control of an autonomous four wheel steered snowplowing robotic vehicle. The vehicle is built for removing snow on the highway. Dynamics of the vehicle is derived and linearized for LQR control. Lateral position is controlled by the LQR method first, then the neural network control technique is introduced to improve tracking performances under the presence of load. The feasibility of using four wheel steering control is investigated by simulation studies of lateral position tracking of the Ford F-250 truck model. Performances of a LQR control method and a neural network control method under virtual snowplowing situation are compared.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.5
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• pp.327-333
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• 2011

Intelligent robots are widely needed in various areas of industry from extremely dangerous environments to service tasks. For autonomous mobile robots, it is significant to move itself safely to a destination point, recognizing its surroundings. Advances in sensor technology and its applications are achieved in order to develop an intelligent robot. In this paper, a mobile robot with a path-finding algorithm is presented. The path-finding algorithm is the one that does not only find a path to designated destination and also recognizes obstacles on the way, calculating its distance. 10 ultrasonic sensor are mounted on the front and rear of the mobile robot to figure out its position. Specular reflection and wide viewing angle, which are inherent characteristics of ultrasonic waves, cause errors in measuring distance.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.13-22
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• 2019

Various hyper-intelligent and ultra-realistic data-driven services are being increasingly developed with the goal of achieving a hyper-connected intelligent society. To sustain this trend, our research focuses on the integration and optimization of data-driven applications from several aspects such as delivery, storage, execution, and sharing of data and software, beyond the limitations of the existing network infrastructure. In this paper, we present important research issues of data-centric hyper-distributed autonomous infrastructure technologies.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.2
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• pp.55-63
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• 2019

Vehicle communication networks have some deficient network resources to support a vast multimedia service including safety driving information, video, news and some broadcast relayed from the playgrounds such as professional baseball games for autonomous vehicles. This paper deals with the framework for providing seamless multimedia service including safety driving information using multicast in cooperated-connected vehicle communication network (CVCN). It adopts smart-switch (SS) and smart intelligent multicast agent(SIMA) to support the seamless multimedia service. The SS manages and switches multimedia streams through SIMA in CVCN network. The SIMA to operate as an access point, is composed of multicast supporting part and control part of mobile devices/autonomous vehicles in CVCN network. Therefore this proposed technique using SS and SIMA within CVCN network is a new framework for multimedia service that can disperse the load of server.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.7-12
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• 2022

Safety and security are the topmost priority in every environment. With the aid of Artificial Intelligence (AI), many objects are becoming more intelligent, conscious, and curious of their surroundings. The recent scientific breakthroughs in autonomous vehicular designs and development; powered by AI, network of sensors and the rapid increase of Internet of Things (IoTs) could be utilized in maintaining safety and security in our environments. AI based on deep learning architectures and models, such as Deep Neural Networks (DNNs), is being applied worldwide in the automotive design fields like computer vision, natural language processing, sensor fusion, object recognition and autonomous driving projects. These features are well known for their identification, detective and tracking abilities. With the embedment of sensors, cameras, GPS, RADAR, LIDAR, and on-board computers in many of these autonomous vehicles being developed, these vehicles can properly map their positions and proximity to everything around them. In this paper, we explored in detail several ways in which these enormous features embedded in these autonomous vehicles, such as the network of sensors fusion, computer vision and natural image processing, natural language processing, and activity aware capabilities of these automobiles, could be tapped and utilized in safeguarding our lives and environment.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.93-98
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• 2023

In this paper, the development of an autonomous electric vehicle for logistics with a robotic arm is introduced. The manual driving electric vehicle was converted into an electric vehicle platform capable of autonomous driving. For autonomous driving, an encoder is installed on the driving wheels, and an electronic power steering system is applied for automatic steering. The electric vehicle is equipped with a lidar sensor, a depth camera, and an ultrasonic sensor to recognize the surrounding environment, create a map, and recognize the vehicle location. The odometry was calculated using the bicycle motion model, and the map was created using the SLAM algorithm. To estimate the location of the platform based on the generated map, AMCL algorithm using Lidar was applied. A user interface was developed to create and modify a waypoint in order to move a predetermined place according to the logistics process. An A-star-based global path was generated to move to the destination, and a DWA-based local path was generated to trace the global path. The autonomous electric vehicle developed in this paper was tested and its utility was verified in a warehouse.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.10
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• pp.58-66
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• 2008

In the development of intelligent vehicles, path tracking of unmanned vehicle is a basis of autonomous driving and automatic navigation. It is very important to find the exact position of a vehicle for the path tracking, and it is possible to get the position information from GPS. However the information of GPS is not the current position but the past position because a vehicle is moving and GPS has a time delay. In this paper, therefore, the moving distance of a vehicle is estimated using a direction sensor and a velocity sensor to compensate the position error of GPS. In the steering control, optimal fuzzy rules for the path tracking can be found through the simulation of Simulink. Real driving experiments show the fuzzy rules are good for the steering control and the position error of GPS is well compensated by the proposed estimation method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5299-5320
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• 2019

Interest in self-driving vehicle research has been rapidly increasing, and related research has been continuously conducted. In such a fast-paced self-driving vehicle research area, the development of advanced technology for better convenience safety, and efficiency in road and transportation systems is expected. Here, we investigate research in self-driving vehicles and analyze the main technologies of driverless car software, including: technical aspects of autonomous vehicles, traffic infrastructure and its communications, research techniques with vision recognition, deep leaning algorithms, localization methods, existing problems, and future development directions. First, we introduce intelligent self-driving car and road infrastructure algorithms such as machine learning, image processing methods, and localizations. Second, we examine the intelligent technologies used in self-driving car projects, autonomous vehicles equipped with multiple sensors, and interactions with transport infrastructure. Finally, we highlight the future direction and challenges of self-driving vehicle transportation systems.

• International Journal of Automotive Technology
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• v.7 no.5
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• pp.609-618
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• 2006

This paper presents the lateral control of an autonomous vehicle by using a look-ahead sensing system. In look-ahead sensing by an absolute positioning system, a reference lane, constructed by straight lanes or circular lanes, was switched by a segment switching algorithm. To cope with sensor noise and modeling uncertainty, a robust LMI-based $H_{\infty}$ lateral controller was designed by the feedback of lateral offset and yaw angle error at the vehicle look-ahead. In order to verify the safety and the performance of lateral control, a scaled-down vehicle was developed and the location of the vehicle was detected by using an ultrasonic local positioning system. In the mechatronic scaled-down vehicle, the lateral model and parameters are verified and estimated by a J-turn test. For the lane change and reference lane tracking, the lateral controllers are used experimentally. The experimental results show that the $H_{\infty}$ controller is robust and has better performance compared with look-down sensing.