動きのランダム性やエネルギー配分を考慮して,異質なロボットの継続的な監視
1Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China; Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China.
ISA transactions
|August 21, 2025
まとめ
この研究は,環境検査のために異質なロボット (UGVとUAV) を使った新しい継続的な監視システムを導入します. 開発されたアルゴリズムは,測定効率とプライバシーを向上させながら,強固で予測不可能なロボットのナビゲーションのためのエネルギー配分を最適化します.
科学分野:
- ロボットと自律システム
- 環境監視
- オプティマイゼーション理論
背景:
- 継続的な監視には ロボットが動きとエネルギーを管理しながら 継続的に監視する必要があります
- 異質なロボットチーム (UGVやUAV) は,環境検査にユニークな利点を提供します.
- 既存の方法では プライバシーを保ち ランダムな動きで エネルギー効率の良い 継続的な監視に 十分な解決策がありません
研究 の 目的:
- 異質なロボットのプライバシー保護のための継続的な監視枠組みを開発する.
- ダイナミックで予測不可能なロボットの監視で エネルギー分配の課題に取り組むこと
- リアルタイム環境検査アプリケーションにおける測定効率の向上
主な方法:
- 無人航空機 (UAV) を利用するフレームワークで,確率測定のためのマルコフチェーンベースのストキャスティック運動.
- 非凸な最適化問題として,移動のランダム性とエネルギー配分 (PSREA) の持続的な監視の策定.
- 作業ネットワークの簡素化のためのクラスタリングベースのアルゴリズムの開発と,最適化問題の解決のための繰り返し2段階アルゴリズムの開発.
主要な成果:
- 提案されたアルゴリズムは,複雑な監視シナリオで多数のタスクノードを効果的に処理します.
- これらの方法は,PSREA最適化問題の非凸性に対応しています.
- 数値的な結果は,ベンチマークアプローチと比較して,検査のパフォーマンスの有意な改善を示しています.
結論:
- 開発されたフレームワークとアルゴリズムは,プライバシーを保護し,エネルギー効率の良い継続的な監視のための効果的な解決策を提供します.
- このアプローチは,異質なロボットを用いて環境検査の作業の測定効率と信頼性を高めます.
- この研究は複雑なロボットの監視操作を最適化するための新しい方法に貢献しています.
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