深層強化学習を用いた不整地における脚式ロボットの適応的動作計画
1Computer Science and Engineering Department, East West University, Aftabnagar, Dhaka, 1212, Badda, Bangladesh. uddin@ewubd.edu.
Scientific reports
|January 6, 2026
まとめ
本研究では、深層強化学習(DRL)を用いて訓練された四足歩行ロボットの適応型移動フレームワークを紹介します。このシステムは、最小限の足滑りで困難な地形を安定かつエネルギー効率よく歩行し、ロボットの移動能力を向上させます。
背景:
- 脚式ロボットは、車輪付きプラットフォームと比較して、不整地での移動能力に優れています。
- 脚式ロボットの動作計画は、不安定な地面、限られたセンシング、および動的な体の動きのために複雑です。
- 古典的な制御方法は、地形や摩擦の予測不可能な変化に対処するのが難しいです。
結論:
- 提案された適応型移動フレームワークは、四足歩行ロボットが困難な不整地を効果的にナビゲートできるようにします。
- 深層強化学習は、脚式ロボットのロバストで適応性の高い移動ポリシーを訓練するための実行可能な方法を提供します。
- 学習された行動は、ロボットの安定性、エネルギー効率、および回復力を向上させ、探索および救助、および探査におけるアプリケーションへの道を開きます。
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