Proprioception を用いた ロボットのための リアルタイム カスケード状態推定フレームワーク
Botao Liu1, Fei Meng1, Zhihao Zhang1
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100811, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
まとめ
この研究は,自感を用いた新しいロボット状態推定フレームワークを導入します. この方法は,特に足と地面の接触時に,足付きロボットの精度とリアルタイムのパフォーマンスを向上させます.
科学分野:
- ロボット
- 州の見積もり
- 制御システム
背景:
- 正確な状態の見積もりは ロボットの制御に不可欠です
- 独自のセンサは,見積もりのための豊富な情報源を提供します.
- 現存する方法は 移動中の衝撃のダイナミクスに 苦労しています
研究 の 目的:
- ロボット用プロイオセプションのカスケード状態推定フレームワークを開発する.
- ロボット状態の推定の精度とリアルタイムのパフォーマンスを向上させる.
- ロボットが足で動いているときの 衝撃の音を効果的に処理する.
主な方法:
- 汎用モメンタムベースのカルマンフィルター (GMKF) は,地面反応力を推定する.
- エラー状態カルマンフィルター (ESKF) は,事前の状態の推定値を提供します.
- 移動地平線推定 (MHE) の問題は,リー群で策定され,並列リアルタイムイテレーション (Para-RTI) で解決される.
主要な成果:
- 提案されたフレームワークは,マニフォールドに緊密に結合された見積もりを実現します.
- 既存の方法と比較して,より高い精度とリアルタイムのパフォーマンスを示しました.
- 脚のあるロボットの足と地面との接触時の衝撃音を効果的に軽減します.
結論:
- カスケードプロイオセプションベースのフレームワークはロボット状態の推定に優れたアプローチを提供します.
- この方法は特に 動的環境で移動する 脚のロボットには有効です
- BQR3 ロボットでの実験的な検証は,フレームワークの有効性を確認しています.
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