• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.239-246
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• 2004

In this paper, we develop and implement a high integrity GNC(Guidance, Navigation, and Control) system, based on the combined use of the Global Positioning System (GPS) and an Inertial Measurement Unit (IMU), for autonomous land vehicle applications. This paper highlights guidance for the predetermined trajectory and navigation with detection of possible faults during the fusion process in order to enhance the integrity of the navigation loop. The implementation of the GNC system to the autonomous land vehicle presented with fault detection methodology considers high frequency faults from the GPS receiver caused by shadowing and multipath error The implementation, based on a low-cost, strapdown INS aided by standard GPS technology, is described. The results of the field test in the urban environment are presented and showed effectiveness of the GNC system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.20-25
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• 2002

This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. Genetic programming can be useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent tree structured chromosome on hardware, and it is difficult to use crossover operator on hardware. Therefore, genetic programming is not so popular as genetic algorithms in evolvable hardware community in spite of its possible strength. We propose a chromosome representation methods and a hardware implementation method that can be helpful to this situation. Our method uses context switchable identical block structure to implement genetic tree on evolvable hardware. We composed an evolutionary strategy for evolvable hardware by combining proposed method with other's striking research results. Proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.229-235
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• 2003

We designed a family of completely autonomous mobile robots with local intelligence. Each robot has a number of on-board sensors, including vision, and does not rely on global positioning systems The on-board embedded controller is sufficient to analyze several low-resolution color images per second. This enables our robots to perform several complex tasks such as navigation, map generation, or providing intelligent group behavior. Not being limited to playing the game of soccer and being completely autonomous, we are also looking at a number of other interesting scenarios. The robots can communicate with each other, e.g. for exchanging positions, information about objects or just the local states they are currently in (e.g. sharing their current objectives with other robots in the group). We are particularly interested in the differences between a behavior-based approach versus a traditional control algorithm at this still very low level of action.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.151-156
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• 2005

This paper proposes a Distributed Autonomous Robotic System(DARS) based on an Artificial Immune System(AIS) and a Classifier System(CS). The behaviors of robots in the system are divided into global behaviors and local behaviors. The global behaviors are actions to search tasks in given environment. These actions are composed of two types: aggregation and dispersion. AIS decides one among these two actions, which robot should select and act on in the global. The local behaviors are actions to execute searched tasks. The robots learn the cooperative actions in these behaviors by the CS in the local one. The proposed system will be more adaptive than the existing system at the viewpoint that the robots learn and adapt the changing of tasks.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.185-190
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• 2002

In distributed autonomous robotic systems (DARS), each robot must behave by itself according to its states ad environments, and if necessary, must cooperate with other robots in order to carry out their given tasks. Its most significant merit is that they determine their behavior independently, and cooperate with other robots in order to perform the given tasks. Especially, in DARS, it is essential for each robot to have evolution ability in order to increase the performance of system. In this paper, a schema co-evolutionary algorithm is proposed for the evolution of collective autonomous mobile robots. Each robot exchanges the information, chromosome used in this algorithm, through communication with other robots. Each robot diffuses its chromosome to two or more robots, receives other robot's chromosome and creates new species. Therefore if one robot receives another robot's chromosome, the robot creates new chromosome. We verify the effectiveness of the proposed algorithm by applying it to cooperative search problem.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.154-163
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• 2013

In this paper, we propose a formation control algorithm for underactuated autonomous underwater vehicles (AUVs) with parametric uncertainties using the approach angle. The approach angle is used to solve the underactuated problem for AUVs, and the leader-follower strategy is used for the formation control. The proposed controller considers the nonzero off-diagonal terms of the mass matrix of the AUV model and the associated parametric uncertainties. Using the state transformation, the mass matrix, which has nonzero off-diagonal terms, is transformed into a diagonal matrix to simplify designing the control. To deal with the parametric uncertainties of the AUV model, a self-recurrent wavelet neural network is used. The proposed formation controller is designed based on the dynamic surface control technique. Some simulation results are presented to demonstrate the performance of the proposed control method.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.69-74
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• 2009

In this paper, we propose a method of ontology-based control of autonomous robots. Advancing one step further from using ontology as a hierarchical storage of information, the proposed method shows how to control robots through ontology inference. That is, the information on obstacles detected by robots is represented as an ontology, and robots' action planning and control are performed according to robots' surroundings through ontology inference. We make a differentially driven robot and illustrate the effectiveness of the proposed method via the experiment of the robot's navigation in real environment.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.4
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• pp.19-25
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• 2005

This paper presents a lateral control system for the autonomous navigation vehicle that was developed and tested by Robotics Centre of Ecole des Mines do Paris in France. A robust lane detection algorithm was developed for detecting different types of lane marker in the images taken by a CCD camera mounted on the vehicle. $^{RT}Maps$ that is a software framework far developing vision and data fusion applications, especially in a car was used for implementing lane detection and lateral control. The lateral control has been tested on the urban road in Paris and the demonstration has been shown to the public during IEEE Intelligent Vehicle Symposium 2002. Over 100 people experienced the automatic lateral control. The demo vehicle could run at a speed of 130km1h in the straight road and 50km/h in high curvature road stably.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.11-16
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• 2023

Vehicular networks are part of the next generation wireless and smart Intelligent Transportation Systems (ITS). In the future, autonomous vehicles will be an integral part of ITS and will provide safe and reliable traveling features to the users. The reliability and security of data transmission in vehicular networks has been a challenging task. To manage data transmission in vehicular networks, road networks are divided into clusters and a cluster head is selected to handle the data. The selection of cluster heads is a challenge as vehicles are mobile and their connectivity is dynamically changing. In this paper, a novel secure cluster head selection algorithm is proposed for secure and reliable data sharing. The idea is to use the secrecy rate of each vehicle in the cluster and adaptively select the most secure vehicle as the cluster head. Simulation results show that the proposed scheme improves the reliability and security of the transmission significantly.