الملخص
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.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Zhao, S., Pan, J. A., Yang, Q., Wang, Z., Chen, C., Xu, Q., & Li, K. (2026). From Target Selection to Digging: A Learning-Based Framework for Continuous Autonomous Excavation. https://omanscience.com/ar/articles/from-target-selection-to-digging-a-learning-based-framework-for-continuous-autonomous-excavation
MLA 9
Zhao, Shuai, et al. "From Target Selection to Digging: A Learning-Based Framework for Continuous Autonomous Excavation." https://omanscience.com/ar/articles/from-target-selection-to-digging-a-learning-based-framework-for-continuous-autonomous-excavation.
شيكاغو (المؤلف–التاريخ)
Zhao, Shuai, Ji-an Pan, Quantao Yang, Zheng Wang, Chaoyi Chen, Qing Xu, and Keqiang Li. 2026. "From Target Selection to Digging: A Learning-Based Framework for Continuous Autonomous Excavation." https://omanscience.com/ar/articles/from-target-selection-to-digging-a-learning-based-framework-for-continuous-autonomous-excavation.
هارفارد
Zhao, S., Pan, J. A., Yang, Q., Wang, Z., Chen, C., Xu, Q. and Li, K. (2026) 'From Target Selection to Digging: A Learning-Based Framework for Continuous Autonomous Excavation', Available at: https://omanscience.com/ar/articles/from-target-selection-to-digging-a-learning-based-framework-for-continuous-autonomous-excavation.
فانكوفر
Zhao S, Pan JA, Yang Q, Wang Z, Chen C, Xu Q, et al. From Target Selection to Digging: A Learning-Based Framework for Continuous Autonomous Excavation. https://omanscience.com/ar/articles/from-target-selection-to-digging-a-learning-based-framework-for-continuous-autonomous-excavation
IEEE
S. Zhao, J. A. Pan, Q. Yang, Z. Wang, C. Chen, Q. Xu, and K. Li, "From Target Selection to Digging: A Learning-Based Framework for Continuous Autonomous Excavation," https://omanscience.com/ar/articles/from-target-selection-to-digging-a-learning-based-framework-for-continuous-autonomous-excavation.