マルチロボットシステムの分散型レジリエントソース検索
IEEE transactions on cybernetics
|September 3, 2025
まとめ
この研究は,サイバー攻撃に直面しているマルチロボットシステムに対する 回復力のあるソース検索アルゴリズムを導入します. この方法は,ミックスされた不適切な行動とサービス拒否攻撃でも,ロボットのソースを見つけることを保証します.
科学分野:
- ロボット
- 制御システム
- サイバーセキュリティ
背景:
- マルチロボットシステム (MRS) は,サイバー攻撃によるソース検索の課題に直面しています.
- 混合攻撃には,不正な行動 (偽データ,バイザンティーン) とサービス拒否 (DoS) が含まれ,ネットワーク接続を妨害します.
研究 の 目的:
- 混合サイバー攻撃による二次MRSの回復力のあるソース検索アルゴリズムを開発する.
- ネットワークの脆弱性にもかかわらず,信頼性の高い調整とソースのローカライゼーションを確保する.
主な方法:
- 新しいアルゴリズムは ソース検索問題を 補助ポイント追跡問題に変えます
- 参照速度ガイドは,過去最適位置を使用する無害なロボットです.
- 制御信号は,DoS攻撃の際に最新の攻撃前のデータを使用して維持されます.
主要な成果:
- 提案されたアルゴリズムは 補助点が源に収束し 良性ロボットをクラスタリングします
- このシステムは,不適切な行動とDoS攻撃の組み合わせに対して回復力を発揮しています.
- 安定性のための十分な条件が厳格に確立されています.
結論:
- アルゴリズムは,既存のレジリエントコンセンサスの方法を,離散時間2次元のMRSに拡張します.
- 複雑で多様なサイバー脅威に対する 回復力のあるソース検索に効果的に取り組んでいます
- シミュレーションと実験の結果は アルゴリズムの有効性を検証します
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