継続的な強化学習による四旋翼機の軌道追跡コントローラ
Yanhui Liu1, Lina Hao1, Shuopeng Wang1
1School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.
Sensors (Basel, Switzerland)
|August 28, 2025
まとめ
この研究は,変化する風の条件で四旋翼の軌道を改善するための継続的な強化学習の枠組みを導入します. この方法は,標準アルゴリズムと比較して適応性を高め,エラーを減らすことができます.
科学分野:
- ロボット
- 人工知能
- 制御システム
背景:
- ダイナミックな風域で四旋翼軌道追跡の精度は低下し,従来のコントローラに挑戦します.
- 既存のデータに基づいた方法は 壊滅的な忘却に直面し 環境への適応を制限しています
- 複雑な環境ミッションでは 変動する風の条件下で 強力な制御が不可欠です
研究 の 目的:
- ダイナミックな風域での強力な四旋翼追跡のための継続的な適応による強化学習の枠組みを開発する.
- 風の干渉に対処する従来のデータベースのアプローチの限界に対処する.
- クアッドローターの環境適応性と追跡性能を向上させる.
主な方法:
- 継続的な補強学習の枠組みで 継続的な反転と補強学習を統合します
- 風のない状態での初期トレーニング,その後,ユーティリティの評価によるダイナミックニューロンリセット.
- 訓練の精度と効率を向上させる 多目的報酬機能
- Gazebo/PX4のシミュレーションプラットフォームを用いた検証で,段階的およびストキャスティックな風の変動が確認されました.
主要な成果:
- 標準的な近接政策最適化 (PPO) アルゴリズムと比較して軌道追跡の平方平均誤差の減少が実証されました.
- 構造的なニューロンリセットを通して ディープ・アンフォースメント・ラーニングで プラスティシティ・損失の問題を解決しました
- ダイナミックな風力場におけるクアドロータの継続的な適応能力を大幅に向上させる.
結論:
- 提案されたフレームワークは,時間的に変化する風の乱れでクアドロータの軌道を追跡するための堅固な解決策を提供します.
- 構造化されたニューロンリセットによる継続的な適応は,ネットワークの可塑性を維持し,パフォーマンスを改善します.
- このアプローチは複雑な環境ミッションにおける クワドローターの信頼性を向上させます
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