• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1092-1097
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• 2014

In this paper, we propose the grid-based output control method for wind farm. To do this, we propose the output control method using the PSO(Particle Swarm Optimization) algorithm. Secondly, we propose the method for detecting the harmonics using STFT(Short-Time Fourier Transform) algorithm. And last, we propose the method for compensating the harmonics using neural network. Finally, we show the effectiveness and feasibility of the proposed method though some simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.285-292
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• 2018

In this paper, we propose a distributed search of a cluster robot using tree structure in an unknown environment. In the proposed method, the cluster robot divides the unknown environment into 4 regions by using the LRF (Laser Range Finder) sensor information and divides the maximum detection distance into 4 regions, and detects feature points of the obstacle. Also, we define the detected feature points as Voronoi Generators of the Voronoi Diagram and apply the Voronoi diagram. The Voronoi Space, the Voronoi Partition, and the Voronoi Vertex, components of Voronoi, are created. The generated Voronoi partition is the path of the robot. Voronoi vertices are defined as each node and consist of the proposed tree structure. The root of the tree is the starting point, and the node with the least significant bit and no children is the target point. Finally, we demonstrate the superiority of the proposed method through several simulations.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.77-78
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• 2008

This paper proposes a new algorithm for path planning and obstacles avoidance using the ant colony optimization algorithm and the particle swarm optimization. The proposed algorithm is a new hybrid algorithm that composes of the ant colony algorithm method and the particle swarm optimization method. At first, we produce paths of a mobile robot in the static environment. And then, we find midpoints of each path using the Maklink graph. Finally, the hybrid algorithm is adopted to get a shortest path. We prove the performance of the proposed algorithm is better than that of the path planning algorithm using the ant colony optimization only through simulation.

• ETRI Journal
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• v.40 no.4
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• pp.446-457
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• 2018

Robot navigation allows robot mobility. Therefore, mobility is an area of robotics that has been actively investigated since robots were first developed. In recent years, interest in personal service robots for homes and public facilities has increased. As a result, robot navigation within the home environment, which is an indoor environment, is being actively investigated. However, the problem with conventional navigation algorithms is that they require a large computation time for their building mapping and path planning processes. This problem makes it difficult to cope with an environment that changes in real-time. Therefore, we propose a humanoid robot navigation algorithm consisting of an image processing and optimization algorithm. This algorithm realizes navigation with less computation time than conventional navigation algorithms using map building and path planning processes, and can cope with an environment that changes in real-time.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.181-193
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• 2009

This paper presents a framework for controlling mobile multiple robots connected by communication networks. This framework provides novel methods to control coordinated systems using mobile agents. The combination of the mobile agent and mobile multiple robots opens a new horizon of efficient use of mobile robot resources. Instead of physical movement of multiple robots, mobile software agents can migrate from one robot to another so that they can minimize energy consumption in aggregation. The imaginary application is making "carts," such as found in large airports, intelligent. Travelers pick up carts at designated points but leave them arbitrary places. It is a considerable task to re-collect them. It is, therefore, desirable that intelligent carts (intelligent robots) draw themselves together automatically. Simple implementation may be making each cart has a designated assembly point, and when they are free, automatically return to those points. It is easy to implement, but some carts have to travel very long way back to their own assembly point, even though it is located close to some other assembly points. It consumes too much unnecessary energy so that the carts have to have expensive batteries. In order to ameliorate the situation, we employ mobile software agents to locate robots scattered in a field, e.g. an airport, and make them autonomously determine their moving behaviors by using a clustering algorithm based on the Ant Colony Optimization (ACO). ACO is the swarm intelligence-based methods, and a multi-agent system that exploit artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments have provided a favorable result. In this paper, we focus on the implementation of the controlling mechanism of the multi-robots using the mobile agents.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.77-88
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• 2018

This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.212-215
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• 2008

In this paper, we develop the optimal 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. The MAKLINK is a set of edges which consist of the convex set. Some of the edges come from the edges of the obstacles. 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] through the experiment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.185-188
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• 2009

In this paper, Position Controller for balance of Seesaw System design using PID Algorithm. Seesaw System is that it's system use widely to analyze of ship or flight dynamics, Inverted Pendulumand, Robot System, manage system for theory of modern control system and all sorts of analysis. In case of Seesaw System, it's necessity that understand and analysis of system and correct selection of parameter because the system is strong nonlinear control system. It guarantees efficiency and stability to adapt quickly for disturbance or change of controller from PID Algorithm of guarantee safe from simple and long history and PSO(Particle Swarm Optimization) that sort of metaheuristic optimization that need to accuracy and fast PID parameter tuning.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.10
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• pp.857-863
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• 2015

The need for human body posture robots has led researchers to develop dexterous design of exoskeleton robots. Quantitative techniques to assess human motor function and generate commands for robots were required to be developed. In this paper, we present a passivity based adaptive control algorithm for upper limb assist exoskeleton. The proposed algorithm can adapt to different subject parameters and provide efficient response against the biomechanical variations caused by subject variations. Furthermore, we have employed the Particle Swarm Optimization technique to tune the controller gains. Efficacy of the proposed algorithm method is experimentally demonstrated using a seven degree of freedom upper limb assist exoskeleton robot. The proposed algorithm was found to estimate the desired motion and assist accordingly. This algorithm in conjunction with an upper limb assist exoskeleton robot may be very useful for elderly people to perform daily tasks.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.687-693
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• 2012

In this paper, we propose a optimal deployment method for cooperative exploration of swarm robots. The proposed method consists of two parts such as optimal deployment and path planning. The optimal area deployment is proposed by the K-mean Algorithm and Voronoi tessellation. The path planning is proposed by the potential field method and A* Algorithm. Finally, the numerical experiments demonstrate the effectiveness and feasibility of the proposed method.