ロボット・アーム・オリエンテーションを基準として,IMUベースのオリエンテーション推定アルゴリズムの時間および周波数領域分析
Ruslan Sultan1, Steffen Greiser1
1Institute of Management and Technology, Hochschule Osnabrück, Kaiserstr. 10c, 49809 Lingen, Germany.
Sensors (Basel, Switzerland)
|August 28, 2025
まとめ
マッジウィック (MF) フィルターと補完フィルター (CF) は,時間と周波数の両方の領域でIMUベースの方向性推定に優れた性能を提供します. 間接的なカルマン (IKF) フィルターは最悪でしたが,最短の時間遅延を達成しました.
科学分野:
- ロボット
- センサー融合
- 制御システム
背景:
- 慣性測定単位 (IMU) は,ロボットにおける方向性推定に不可欠です.
- 間接的なカルマン (IKF),マッジウィック (MF),補完的な (CF) フィルターなどのさまざまなアルゴリズムが使用されます.
- アルゴリズムの選択には,さまざまな領域におけるパフォーマンス評価が不可欠です.
研究 の 目的:
- IMUベースの指向推定アルゴリズムの性能を分析し比較する.
- ロボットアームを使って 時間と周波数の領域の両方で アルゴリズムを評価します
- フィルターのパラメータを最適に調整する.
主な方法:
- 6関節 UR5e ロボットアームの6DoF IMUを使用し,地面の真実としてロボットの方向性を使用しました.
- テスト用のポーズシーケンスと汎用バイナリーノイズ (GBN) 信号.
- 計算された平方根誤差 (RMSE),最大絶対誤差 (MaxAE),複合周波数応答,コヘランス,平均等価時間遅延 (AETD).
主要な成果:
- マッジウィック (MF) フィルターと補足フィルター (CF) は,時間領域で総合的に最高のパフォーマンスを示した.
- MFとCFは,周波数領域でも比較的に良好でした.
- 間接的なカルマン (IKF) フィルターは一貫して最も低いパフォーマンスを示したが,最も低いAETDを達成した.
結論:
- MFとCFは,IMUベースの強固な方向性推定のために推奨されます.
- IKFは,最小の時間遅延が主な懸念事項である場合に適しています.
- アルゴリズムの選択は,正確性とダイナミクスの特定のアプリケーション要件に依存します.
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