• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.595-609
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• 2020

In this study, we propose a decentralized mathematical model for predictive control of a system of multi-autonomous unmanned aerial vehicles (UAVs), also known as drones. Being decentralized and autonomous implies that all members make their own decisions and fly depending on the dynamic information received from other unmanned aircraft in the area. We consider a variety of realistic characteristics, including time delay and communication locality. For this flocking flight, we do not possess control for central data processing or control over each UAV, as each UAV runs its collision avoidance algorithm by itself. The main contribution of this work is a mathematical model for stable group flight even in adverse weather conditions (e.g., heavy wind, rain, etc.) by adding Gaussian noise. Two of our proposed variance control algorithms are presented in this work. One is based on a simple biological imitation from statistical physical modeling, which mimics animal group behavior; the other is an algorithm for cooperatively tracking an object, which aligns the velocities of neighboring agents corresponding to each other. We demonstrate the stability of the control algorithm and its applicability in autonomous multi-drone systems using numerical simulations.

• Journal of Korean Society for Quality Management
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• v.46 no.4
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• pp.973-982
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• 2018

Purpose: The purpose of this study is to develop an effective simulation modeling formalism for autonomous control systems, such as unmanned aerial vehicles and unmanned surface vehicles. The proposed simulation modeling formalism can be used to evaluate the quality and effectiveness of autonomous control systems. Methods: The proposed simulation modeling formalism is developed by extending the classic DEVS (Discrete Event Systems Specifications) formalism. The main advantages of the classic DEVS formalism includes its rigorous formal definition as well as its support for the specification of discrete event models in a hierarchical and modular manner. Results: Although the classic DEVS formalism has been a popular modeling tool, it has limitations in describing an autonomous control system which needs to make decisions by its own. As a result, we proposed an extended DEVS formalism which enables the effective description of internal decisions according to its conditional variables. Conclusion: The extended DEVS formalism overcomes the limitations of the classic DEVS formalism, and it can be used for the effectiveness simulation of autonomous weapon systems.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.590-593
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• 2005

Unmanned Helicopter has several abilities such as vertical Take off, hovering, low speed flight at low altitude. Such vehicles are becoming popular in actual applications such as search and rescue, aerial reconnaissance and surveillance. These vehicles also used under risky environments without threatening the life of a pilot. Since a small unmanned helicopter is very sensitive to environmental conditions, it is generally known that the flight control is very difficult problems. The nonlinear adaptive fuzzy controller design procedure and its applications for altitude control of unmanned helicopter were described in the paper. This research was concentrated on describing the design methodologies of altitude controller design for small unmanned helicopter acquiring autonomous take off and vertical movement. The design methodologies and performance of the altitude controller were simulated and verified with an adaptive fuzzy controller. Throughout simulation results, I showed that the proposed adaptive controllers have enhanced control performance such as robustness, effectiveness and safety, in the altitude control of the unmanned helicopter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1483-1490
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• 2009

This parer is presented multiple path-planning of unmanned autonomous forklift using modified genetic algorithm and fuzzy inference system. There are a task-level feedback method and a method that path is dynamically replaned in realtime while the autonomous vehicles are moving by means of an optimal algorithm for existing multiple path-planning. However, such methods cause malfunctions and inefficiency in the sense of time and energy, and path-planning should be dynamically replanned in realtime. To solve these problems, we propose multiple path-planning using modified genetic algorithm and fuzzy inference system and show the performance with autonomous vehicles. For experiment, we designed and built two autonomous mobile vehicles that equipped with the same driving control part used in actual autonomous forklift, and test the proposed multiple path-planning algorithm. Experimental result that actual autonomous mobile vehicle, we verified that fast optimized path-planning and efficient collision avoidance are possible.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.233-239
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• 2019

An unmanned underwater vehicle is a major weapon system that allows surveillance and reconnaissance missions in border areas or threatening areas where enemy submarines are present. Unmanned underwater vehicles can be used to explore underwater resources, predict disasters, and survey the topography of the ocean floor in the civilian fields, while in the defense fields, it can be used for anti-submarine reconnaissance and mine countermeasures. In this paper, we first investigate the main classification of unmanned underwater vehicles, and foreign R&D trends are analyzed based on the main classification criteria by weight, such as portable, light, heavy and large-scale unmanned underwater vehicles. Then we examine the trends in the development of domestic unmanned underwater vehicles. Finally, through the analysis of both domestic and foreign unmanned underwater vehicles, we present future development trends of unmanned underwater vehicles in order to set defense goals to counter the anticipated threats and diversified potential environment.

• Journal of Ocean Engineering and Technology
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• v.26 no.3
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• pp.6-12
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• 2012

In this paper, addressed is the control problem of generating a formation for a group of unmanned surface and underwater vehicles. The formation control scheme proposed in this work is based on a fusion of theleader-follower and virtual reference approaches. This scheme gives a formation constraint representation that is independent of the number of vehicles in the formation and the resulting control algorithm is scalable. One of the most important features in controller design is the ability of the controller to globally and exponentially stabilize the formation errors defined by the formation constraints. The proposed controller is based on feedback linearization, and the formation errors are shown to be globally and exponentially stable in the sense of Lyapunov.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.95-100
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• 2013

Localization is very important for the autonomous navigation of Unmanned Ground Vehicles; however, it is difficult that they have a precise Inertial Navigation System(INS) sensor, especially Small Unmanned Ground Vehicle(SUGV). Moreover, there are some condition such as denial of global position system(GPS), GPS/INS integrated system is not robust. This paper proposes the estimation algorithm with optical flow sensor and INS. Being compared with previous researches, the proposed algorithm is suitable for skid steering vehicles. We revised the measurement model of previous research for the accuracy of side direction position. Experimental results were performed to verify the algorithm, and the result showed an excellent performance.

• Journal of Ocean Engineering and Technology
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• v.36 no.1
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• pp.41-49
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• 2022

With the advancement of intelligent vehicles and unmanned systems, there is a growing interest in underwater surveys using autonomous marine vehicles (AMVs). This study presents an automated planning strategy for a long-term survey mission using a fleet of AMVs consisting of autonomous surface vehicles and autonomous underwater vehicles. Due to the complex nature of the mission, the actions of the vehicle must be of high-level abstraction, which means that the actions indicate not only motion of the vehicle but also symbols and semantics, such as those corresponding to deploy, charge, and survey. For automated planning, the planning domain definition language (PDDL) was employed to construct a mission planner for realizing a powerful and flexible planning system. Despite being able to handle abstract actions, such high-level planners have difficulty in efficiently optimizing numerical objectives such as obtaining the shortest route given multiple destinations. To alleviate this issue, a widely known technique in operations research was additionally employed, which limited the solution space so that the high-level planner could devise efficient plans. For a comprehensive evaluation of the proposed method, various PDDL-based planners with different parameter settings were implemented, and their performances were compared through simulation. The simulation result shows that the proposed method outperformed the baseline solutions by yielding plans that completed the missions more quickly, thereby demonstrating the efficacy of the proposed methodology.

• Journal of Power System Engineering
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• v.16 no.1
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• pp.84-90
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• 2012

In unmanned vehicles' navigation, the shapes of obstacles are generally irregular and complex. The motion of vehicles based on the range sensor system such as ultrasonic sensors or laser sensors can be unstable due to the irregular shape of the obstacles. In this case, to generate stable trajectory of unmanned vehicles equipped with range sensors, we need an approach that can simplify an obstacle's irregular shape information. In this paper, we propose the trajectory generation algorithm that an vehicle can stably navigate in the environments where irregular shaped obstacles are scattered. The proposed method is verified through the analysis of vehicle's trail and direction data acquired by simulations and implementations.

• The Journal of Korea Robotics Society
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• v.6 no.2
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• pp.97-107
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• 2011

This paper describes efficient flight control algorithms for building a reconfigurable ad-hoc wireless sensor networks between nodes on the ground and airborne nodes mounted on autonomous vehicles to increase the operational range of an aerial robot or the communication connectivity. Two autonomous flight control algorithms based on adaptive gradient climbing approach are developed to steer the aerial vehicles to reach optimal locations for the maximum communication throughputs in the airborne sensor networks. The first autonomous vehicle control algorithm is presented for seeking the source of a scalar signal by directly using the extremum-seeking based forward surge control approach with no position information of the aerial vehicle. The second flight control algorithm is developed with the angular rate command by integrating an adaptive gradient climbing technique which uses an on-line gradient estimator to identify the derivative of a performance cost function. They incorporate the network performance into the feedback path to mitigate interference and noise. A communication propagation model is used to predict the link quality of the communication connectivity between distributed nodes. Simulation study is conducted to evaluate the effectiveness of the proposed reconfigurable airborne wireless networking control algorithms.