目標選択から掘削まで：連続自律掘削のための学習ベースフレームワーク
From Target Selection to Digging: A Learning-Based Framework for Continuous Autonomous Excavation
LiDAR地形認識による目標選択と強化学習・模倣学習を組み合わせ、連続的な自律掘削を実現するフレームワークを提案し、実機実験で有効性を示した。
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著者: Shuai Zhao, Ji-an Pan, Quantao Yang, Zheng Wang, Chaoyi Chen, Qing Xu, Keqiang Li
分類: cs.RO
原文アブストラクト
Repeated excavation continuously reshapes pile geometry, requiring an autonomous excavator to adapt its digging targets and coordinate motion across successive excavation cycles. We present a learning-based framework for continuous autonomous excavation that integrates terrain-aware target selection with reinforcement- and imitation-learning controllers. The framework separates target-conditioned motion from local digging: a shared task-conditioned RL policy controls waypoint-guided approach and loaded transport, while an IL policy learns vision-based digging and lifting from expert demonstrations. Digging targets are selected from LiDAR elevation maps and converted into bucket-tip waypoints for motion control. The control architecture coordinates the learned policies and deterministic unloading through a shared motion interface. The complete system is deployed on a scaled hydraulic excavator with multimodal sensing and closed-loop actuator control. Offline replay and physical experiments demonstrate more consistent target selection, shorter local motion time, and increased payload compared with the respective baselines. The learned digging policy achieves a mean payload of 6.52 kg per completed cycle, compared with 2.68 kg for Fixed Dig. Three five-scoop runs further demonstrate consecutive autonomous excavation under continuously changing pile geometry.