ESDBO:UGVの都市路線計画のための多戦略強化されたダングビートル最適化アルゴリズム
Chenhui Wei1, Zhifang Wei1, Yanlan Li2
1School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|February 13, 2026
まとめ
この研究は,無人地上車両 (UGV) の経路計画のための糞虫最適化 (DBO) アルゴリズムを強化しています. 新しいESDBOアルゴリズムは,複雑な都市環境におけるより正確で安定した経路計画を実現します.
科学分野:
- ロボット技術と人工知能
- コンピューティング・インテリジェンスと最適化
背景:
- 無人地上車両 (UGV) は,都市環境で複雑なルート計画課題に直面しています.
- Dung Beetle Optimization (DBO) アルゴリズムはUGVの経路計画に人気がありますが,収束精度とローカルオピタミアが悪いです.
- 既存のDBO方法は,ダイナミックな都市環境での堅実な性能のために強化する必要があります.
研究 の 目的:
- 標準のDBOの限界を克服するために,マルチ戦略強化DBOアルゴリズム (ESDBO) を提案する.
- UGV経路計画のためのコンバージェンス精度,グローバル検索能力,およびローカル検索能力を改善する.
- 自動航行のための最適で安全な経路の生成におけるESDBOの有効性を検証する.
主な方法:
- 解決策の多様性を高めるために,集団初期化にシナプスマッピングを導入しました.
- ダイナミック・コンバージェンス・グローバル・サーチ・バランシングのための適応情報揮発変異戦略を開発した.
- 地元の検索能力と安定性を向上させるために,多メカニズム共同進化戦略を設計しました.
- アルゴリズムの検証のために実施されたアブレーション実験とベンチマークテスト (CEC2017)
- ランダムなMAPFベンチマークマップで経路計画実験を行った.
主要な成果:
- ESDBOは,標準のDBOと比較して優れた最適化能力と収束精度を実証しました.
- 強化されたアルゴリズムは,安定性が向上し,局所最適への感受性が低下したことを示した.
- 経路計画実験では,ESDBOが短距離,最小の曲がり,高い安全性を持つ全般的に最適な経路を生成する能力を確認しました.
- 様々な障害物密度や地図スケールで効果的なパフォーマンスを観察した.
結論:
- 提案されたESDBOアルゴリズムは,複雑な都市路線計画のための伝統的なDBOに比べて,著しく改善しています.
- ESDBOは,困難な環境におけるUGVの自律的なナビゲーションのための効率的かつ信頼性の高いソリューションを提供します.
- マルチ戦略の強化は,元のDBOアルゴリズムの収束精度とローカル最適の問題に効果的に対処します.
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