• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.130-134
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• 2012

In this paper, we propose a space partitioning technique for swarm robots by using the Centroidal Voronoi Tessellation. The proposed method consists of two parts such as space partition and collision avoidance. The space partition for searching a given space is carried out by a density function which is generated by some accidents. The collision avoidance is implemented by the potential field method. Finally, the numerical experiments show the effectiveness and feasibility of the proposed method.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.345-350
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• 2008

This study aims to propose the concept design of oil spill protection robot which can rapidly intervene to control the oil spillage situation at the sea. Taking into account the fact that a huge amount of oil is transported trans-continentally by oil tanker, none of industrialized countries are completely safe from the marine oil spill which results in social, economical and ecological damages to their communities. The employment of double hull-oil tanker, pipe line transporting can be most safe way. Yet complete prevention of oil spill is probably not realistic. Accordingly the alternative solution to control marine oil spill and minimize the damages caused by the incident using intelligent robot technology based on swarm control method is proposed. The main features of oil spill protection(OSP) robot is explained via following three perspectives. Firstly, from functional point of view, OSP robot system safely and efficiently replaces oil boom installation manually conducted by human workers with intelligent robot technology based on swarm control theory. For second, its modular architecture brings efficient storage of main components including oil boom and facilitates maintenance. For the last, its geometric form and shape enables whole system to be installed to helicopter, boat or oil tanker itself with ease and to rapidly deploy the units to the oil spill area.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.1
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• pp.9-16
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• 2023

Robots are expected to expand their scope of application to the military field and take on important missions such as surveillance and enemy detection in the coming future warfare. Swarm robots can perform tasks that are difficult or time-consuming for a single robot to be performed more efficiently due to the advantage of having multiple robots. Swarm robots require mutual recognition and collaboration. So they send and receive vast amounts of data, making it increasingly difficult to verify SW. Hardware-in-the-loop simulation used to increase the reliability of mission verification enables SW verification of complex swarm robots, but the amount of verification data exchanged between the HILS device and the simulator increases exponentially according to the number of systems to be verified. So communication overload may occur. In this paper, we propose a digital twin-based communication optimization technique to solve the communication overload problem that occurs in mission verification of swarm robots. Under the proposed Digital Twin based Multi HILS Framework, Network DT can efficiently allocate network resources to each robot according to the mission scenario through the Network Controller algorithm, and can satisfy all sensor generation rates required by individual robots participating in the group. In addition, as a result of an experiment on packet loss rate, it was possible to reduce the packet loss rate from 15.7% to 0.2%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.411-414
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• 2003

Particle swarm optimization (PSO) is employed to train fuzzy-neural networks (FNN), which can be employed as an important building block in real life robot systems, controlled by voice-based commands. The FNN is also trained to capture the user spoken directive in the context of the present performance of the robot system. The system has been successfully employed in a real life situation for navigation of a mobile robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.224-229
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• 2009

When swarm robots exist in the same workspace, first we have to decide robots in order to accomplish some tasks. There have been a lot of works that research how to control robots in cooperation. The interest in using swarm robot systems is due to their unique characteristics such as increasing the adaptability and the flexibility of mission execution. When an invader is discovered, swarm robots have to enclose a invader through a variety of path, expecting invader's move, in order to effective enclose. In this paper, we propose an effective swarm robots enclosing and distributed moving algorithm in a two dimensional map.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.296-301
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• 2011

This paper deals with the simultaneous localization and mapping system using cooperative robot. For recognizing environment, swarm robot uses the ultrasonic sensors and vision sensor. Ultrasonic sensors measure the distance information, and vision sensor recognizes the predefined landmark. we used SURF with excellent quality and fast matching in order to recognize landmark. Due to measurement error of sensors, we fusion them using particle filter for accurate localization and mapping. Finally, we show the feasibility of the proposed method through some experiments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.176-179
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• 2007

In this paper, we develop the path planning algorithm using the improved Dijkstra algorithm and the particle swarm optimization. To get the optimal path, at first we construct the MAKLINK on the world environment and then make a graph associated with the MAKLINK. From the graph, we obtain the Dijkstra path between the starting point and the destination point. From the optimal path, we search the improved Dijkstra path using the graph. Finally, applying the particle swarm optimization to the improved Dijkstra path, we obtain the optimal path for the mobile robot. It turns out that the proposed method has better performance than the result in [1].

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.11
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• pp.2152-2156
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• 2011

In this paper, we propose the novel behavior control algorithm by using the efficient searching method based on the characteristic of the swarm robots in unknown space. The proposed method consists of identifying the position and moving state of a robot by the dynamic modelling of a wheel drive vehicle, and planing behavior control rules of the swarm robots based on the sensor range zone. The cooperative search for unknown space is carried out by the proposed behavior control. Finally, some experiments show the effectiveness and the feasibility of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2493-2503
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• 2018

Passive dynamic walking exhibits humanoid and energy efficient gaits. However, optimal design of passive walker at multi-variable level is not well studied yet. This paper presents a Chaotic Particle Swarm Optimization (CPSO) algorithm and applies it to the optimal design of flexible passive walker. Hip torsional stiffness and damping were incorporated into flexible biped walker, to imitate passive elastic mechanisms utilized in human locomotion. Hybrid dynamics were developed to model passive walking, and period-one gait was gained. The parameters global searching scopes were gained after investigating the influences of structural parameters on passive gait. CPSO were utilized to optimize the flexible passive walker. To improve the performance of PSO, multi-scroll Jerk chaotic system was used to generate pseudorandom sequences, and chaotic disturbance would be triggered if the swarm is trapped into local optimum. The effectiveness of CPSO is verified by comparisons with standard PSO and two typical chaotic PSO methods. Numerical simulations show that better fitness value of optimal design could be gained by CPSO presented. The proposed CPSO would be useful to design biped robot prototype.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.5
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• pp.115-120
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• 2019

In this paper, we present the results of the performance statistical analysis of the multi-robot formation control based on receding horizon particle swarm optimization (RHPSO). The formation control problem of multi-robot system can be defined as a constrained nonlinear optimization problem when considering collision avoidance between robots. In general, the constrained nonlinear optimization problem has a problem that it takes a long time to find the optimal solution. The RHPSO algorithm was proposed to quickly find a suboptimal solution to the optimization problem of multi-robot formation control. The computational complexity of the RHPSO increases as the number of candidate solutions and generations increases. Therefore, it is important to find a suboptimal solution that can be used for real-time control with minimal candidate solutions and generations. In this paper, we compared the formation error according to the number of candidate solutions and the number of generations. Through numerical simulations under various conditions, the results are analyzed statistically and the minimum number of candidate solutions and the minimum number of generations of the RHPSO algorithm are derived within the allowable control error.