• Title/Summary/Keyword: MAKLINK

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Path Planning Method Using the the Particle Swarm Optimization and the Improved Dijkstra Algorithm (입자 군집 최적화와 개선된 Dijkstra 알고리즘을 이용한 경로 계획 기법)

  • Kang, Hwan-Il;Lee, Byung-Hee;Jang, Woo-Seok
    • 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.

Path Planning Algorithm Using the Particle Swarm Optimization and the Improved Dijkstra Algorithm

  • Kang, Hwan-Il;Lee, Byung-Hee;Jang, Woo-Seok
    • 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].

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Development of a New Optimal Path Planning Algorithm for Mobile Robots Using the Ant Colony Optimization Method (개미 집단 최적화 기법을 이용한 이동 로봇 최적 경로 생성 알고리즘 개발)

  • Ko, Jong-Hoon;Kim, Joo-Min;Kim, Dae-Won
    • Proceedings of the KIEE Conference
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    • 2009.07a
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    • pp.1827_1828
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    • 2009
  • In this paper proposes a new algorithm for path planning using the ant colony optimization algorithm. The proposed algorithm is a new hybrid algorithm that composes of the features of the ant colony algorithm method and the Maklink graph method. At first, paths are produced for a mobile robot in a static environment, and then, the midpoints of each obstacles nodes are found using the Maklink graph method. Finally, the shortest path is selected by the ant colony optimization algorithm.

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DEVELOPMENT OF A NEW OPTIMAL PATH PLANNING ALGORITHM FOR MOBILE ROBOTS USING THE ANT COLONY OPTIMIZATION METHOD (개미 집단 최적화 기법을 이용한 이동로봇 최적 경로 생성 알고리즘 개발)

  • Lee, Jun-Oh;Ko, Jong-Hoon;Kim, Dae-Won
    • Proceedings of the KIEE Conference
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    • 2007.10a
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    • pp.311-312
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    • 2007
  • This paper proposes a new algorithm for path planning and obstacles avoidance using the ant colony optimization algorithm. The proposed algorithm is a new hybrid algorithm that composes of the ant colony algorithm method and the Maklink graph method. At first, we produce the path of a mobile robot a the static environment. And then we find midpoints of each path using the Maklink graph. Finally the ant colony optimization algorithm is adopted to get a shortest path. In this paper, we prove the performance of the proposed algorithm is better than that of the Dijkstra algorithm through simulation.

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DEVELOPMENT OF A NEW PATH PLANNING ALGORITHM FOR MOBILE ROBOTS USING THE ANT COLONY OPTIMIZATION AND PARTICLE SWARM OPTIMIZATION METHOD (ACO와 PSO 기법을 이용한 이동로봇 최적화 경로 생성 알고리즘 개발)

  • Lee, Jun-Oh;Ko, Jong-Hoon;Kim, Dae-Won
    • 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.

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