• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2365-2377
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• 2017

This paper addresses two interrelated problems concerning the tracking control of pod propulsion unmanned surface vessel (USV), namely, the modeling of pod propulsion USV, and tracking controller design. First, based on MMG modeling theory, the model of pod propulsion USV is derived. Furthermore, a practical adaptive neural tracking controller is proposed by backstepping technique, neural network approximation and adaptive method. Meanwhile, unlike some existing tracking methods for surface vessel whose control algorithms suffer from "explosion of complexity", a novel neural shunting model is introduced to solve the problem. Using a Lyapunov functional, it is proven that all error signals in the system are uniformly ultimately bounded. The advantages of the paper are that first, the underactuated characteristic of pod propulsion USV is proved; second, the neural shunting model is used to solve the problem of "explosion of complexity", and this is a combination of knowledge in the field of biology and engineering; third, the developed controller is able to capture the uncertainties without the exact information of hydrodynamic damping structure and the sea disturbances. Numerical examples have been given to illustrate the performance and effectiveness of the proposed scheme.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.91-99
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• 2013

An autonomous unmanned underwater vehicle is a type of marine self-propelled robot that executes some specific mission and returns to base on completion of the task. In order to successfully execute the requested operations, the vehicle must be guided by an effective navigation algorithm that enables it to avoid obstacles and follow the best path. Architectures and principles for intelligent dynamic systems are being developed, not only in the underwater arena but also in related areas where the work does not fully justify the name. The problem of increasing the capacity of systems management is highly relevant based on the development of new methods for dynamic analysis, pattern recognition, artificial intelligence, and adaptation. Among the large variety of navigation methods that presently exist, the dynamic window approach is worth noting. It was originally presented by Fox et al. and has been implemented in indoor office robots. In this paper, the dynamic window approach is applied to the marine world by developing and extending it to manipulate vehicles in 3D marine environments. This algorithm is provided to enable efficient avoidance of obstacles and attainment of targets. Experiments conducted using the algorithm in MATLAB indicate that it is an effective obstacle avoidance approach for marine vehicles.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.471-478
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• 2011

This UGV (Unmanned Ground Vehicle) is not only widely used in various practical applications but is also currently being researched in many disciplines. In particular, obstacle avoidance is considered one of the most important technologies in the navigation of an unmanned vehicle. In this paper, we introduce a simple algorithm for path planning in order to reach a destination while avoiding a polygonal-shaped static obstacle. To effectively avoid such an obstacle, a path planned near the obstacle is much shorter than a path planned far from the obstacle, on the condition that both paths guarantee that the robot will not collide with the obstacle. So, to generate a path near the obstacle, we have developed an algorithm that combines an edge detection method and a limit-cycle navigation method. The edge detection method, based on Hough Transform and IR sensors, finds an obstacle's edge, and the limit-cycle navigation method generates a path that is smooth enough to reach a detected obstacle's edge. And we proposed novel algorithm to solve local minima using the virtual wall in the local vision. Finally, we verify performances of the proposed algorithm through simulations and experiments.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.965-973
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• 2011

Small unmanned ground vehicles(SUGVs) are typically operational on unstructured environments such as crashed building, mountain area, caves, and so on. On those terrains, driving control can suffer from the unexpected ground disturbances which occasionally lead turnover situation. In this paper, we have proposed an algorithm which sustains driving stability of a SUGV as preventing from turnover. The algorithm exploits potential field method in order to determine the stability of the robot. Then, the flipper and manipulator posture of the SUGV is optimized from local optimization algorithm known as gradient descent method. The proposed algorithm is verified using 3D dynamic simulation, and results showed that the proposed algorithm contributes to driving stability of SUGV.

• Journal of Ocean Engineering and Technology
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• v.31 no.2
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• pp.170-176
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• 2017

In this paper, we propose an underwater localization scheme through the fusion of an inertial navigation system (INS) and the received signal strength (RSS) of electromagnetic (EM) wave sensors to guarantee precise localization performance with high sampling rates. In this localization scheme, the INS predicts the pose of the unmanned underwater vehicle (UUV) by dead reckoning at every step, and the RF sensors corrects the UUV position functions using the Earth-fixed reference when the UUV is located in underwater wireless sensor networks (UWSN). The localization scheme and state modeling were conducted in the extended Kalman filter framework, and UUV localization experiments were conducted in a basin environment. The scheme achieved reliable localization accuracy during long-term navigation, demonstrating the feasibility of exploiting EM wave attenuation as Earth-fixed reference sensors.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.147-155
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• 2015

In this paper, we consider a mobile sensor network for monitoring a polluted area where human beings cannot access. Due to the limited sensing range of individual unmanned vehicles, they need to cooperate to achieve an effective sensing coverage and move to a more polluted region. In order to address the limitations of sensing and communication ranges, we propose a hazardous substance monitoring network based on virtual force algorithms, and develop a testbed. In the considered monitoring network, each unmanned vehicle achieves an optimal coverage and move to the highest interest area based on neighboring nodes sensing values and locations. By using experiments based on the developed testbed, we show that the proposed monitoring network can autonomously move toward a more polluted area and obtain a high weighted coverage.

• The Journal of Korea Robotics Society
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• v.8 no.1
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• pp.51-57
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• 2013

Recently, the number of jellyfish has been rapidly grown because of the global warming, the increase of marine structures, pollution, and etc. The increased jellyfish is a threat to the marine ecosystem and induces a huge damage to fishery industries, seaside power plants, and beach industries. To overcome this problem, a manual jellyfish dissecting device and pump system for jellyfish removal have been developed by researchers. However, the systems need too many human operators and their benefit to cost is not so good. Thus, in this paper, the design, implementation, and experiments of autonomous jellyfish removal robot system, named JEROS, have been presented. The JEROS consists of an unmanned surface vehicle (USV), a device for jellyfish removal, an electrical control system, an autonomous navigation system, and a vision-based jellyfish detection system. The USV was designed as a twin hull-type ship, and a jellyfish removal device consists of a net for gathering jellyfish and a blades-equipped propeller for dissecting jellyfish. The autonomous navigation system starts by generating an efficient path for jellyfish removal when the location of jellyfish is received from a remote server or recognized by a vision system. The location of JEROS is estimated by IMU (Inertial Measurement Unit) and GPS, and jellyfish is eliminated while tracking the path. The performance of the vision-based jellyfish recognition, navigation, and jellyfish removal was demonstrated through field tests in the Masan and Jindong harbors in the southern coast of Korea.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.2
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• pp.154-160
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• 2017

In this paper, we studied the Bartlett method to correctly estimate the targets of a unmanned vehicles. The Bartlett method estimates the desired signals by making the gain constant for the received signal incident on the array antenna. In this paper, the weights of the Bartlett method are updated by applying the winner method and steepest descent method in order to estimation the accurate unmanned. The updated weights improve the resolution of the existing Bartlett method by applying optimal weights to all received signals received at the array antenna. Through simulation, we are comparative analysis about the performance of proposed method. From result of simulation, We showed the superior performance of the proposed method relative to the classical method, and Bartlett using steep descent method showed more superior than one using wiener method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2400-2405
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• 2013

We detect the obstacle for the UGV(unmanned ground vehicle) from the compound image which is generated by stereo vision sensor masking the depth image and color image. Stereo vision sensor can gathers the distance information by stereo camera. The obstacle information from the depth compound image can be send to mobile robot and the robot can localize the indoor area. And, we test the performance of the mobile robot in terms of distance between the obstacle and the robot's position and also test the color, depth and compound image respectively. Moreover, we test the performance in terms of number of frame per second which is processed by operating machine. From the result, compound image shows the improved performance in distance and number of frames.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.532-537
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• 2009

The necessity of intelligent surveillance system is gradually considered seriously from the space where the security is important. In this paper, we embodied unmanned intelligent system by developing embedded mobile robot based on images and sounds tracking. For objects tracking, we used block-matching algorithm and for sound source tracking, we calculated time differences and magnitude dissimilarities of sound. And we demonstrated the superiority of intruder tracking algorithm through the embodiment of Pan-Tilt camera and sound source tracking module using system, Network camera and mobile robot using system and mobile robot using system. By linking security system, the suggested system can provide some interfacing functions for the security service of the public facilities as well as that of home.