ロボット群による仮想フェロモンを伴う粒子群最適化を用いた探索アルゴリズムの研究

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門

発行日: 2022/02/01

タイトル(英語): Research on Search Algorithm Using Particle Swarm Optimization with Virtual Pheromone for Swarm Robots

著者名: 稲原　大翔（神戸大学 海事科学研究科），元井　直樹（神戸大学 海事科学研究科）

著者名(英語): Hiroto Inahara (Graduate School of Maritime Sciences, Kobe University), Naoki Motoi (Graduate School of Maritime Sciences, Kobe University)

キーワード: 移動ロボット群，群知能，粒子群最適化，モーションコントロール　　mobile robots，swarm intelligence，PSO，motion control

要約(英語): This paper proposes a search algorithm using particle swarm optimization (PSO) with virtual pheromone for swarm robots. Swarm robots are attracting attention in disaster relief works to search for victims. The search algorithm involves a combination of global and local searching. The conventional search method consists of random walk as the global search and PSO as the local search. However, random walk is not efficient in complex environments. For efficient searching, PSO with virtual pheromone is used for the global search. The virtual pheromone drives the swarm robots to an unsearched area, dose not need map data, and has low calculation cost. In addition, it is not necessary in the proposed method to switch algorithms between global and local searching. The validity of the proposed method was confirmed from the simulation results.

本誌: 電気学会論文誌D（産業応用部門誌） Vol.142 No.2 （2022）

本誌掲載ページ: 86-94 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejias/142/2/142_86/_article/-char/ja/