グレイラグ・グース・オプティマイゼーションに基づくロボットアームの振動軌道の計画
1School of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, China.
Sensors (Basel, Switzerland)
|February 13, 2026
まとめ
半導体ウェイファーを扱うロボットは,Gray Goose Optimization (GGO) を使用して振動を抑制します. この方法は,より速く,より滑らかな円盤輸送のための軌道を最適化することによって,位置付けの精度とシステムの安定性を改善します.
科学分野:
- ロボット工学とオートメーション
- 制御システム工学 制御システム工学
- オプティマイゼーション アルゴリズム
背景:
- 半導体製造において,高速で精密な輸送のために,ウェーファー処理ロボットは極めて重要です.
- 軽量で高速なロボットの柔軟な部品は残留振動を引き起こし,精度と安定性を低下させます.
研究 の 目的:
- ウェイファーハンドリングロボットの振動抑制軌道を計画する新しい方法を提案する.
- 半導体製造におけるポジショニング精度とシステムの安定性を高めるために.
主な方法:
- 多目的軌道計画のためにグレイ・グース・オプティマイゼーション (GGO) アルゴリズムを使用しました.
- 運動時間と振動エネルギーのバランスをとる多目的機能を開発しました.
- S形速度プロファイルのための最適な切り替え時間点を決定した.
主要な成果:
- GGOアルゴリズムは,ベンチマーク機能の有効性と堅実性を実証しました.
- 計画された軌道は,干渉下での移動の変動は無視できる程度でした.
- 運動時間と残留振動エネルギーのバランスをとるパレト最適ソリューションを達成しました.
結論:
- GGOベースの方法は,ウェーファーハンドリングロボットの振動を効果的に抑制します.
- このアプローチにより,軌道の滑らかさ,速度,加速制御が向上します.
- これにより,半導体製造におけるポジショニング精度とシステムの安定性が向上します.
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