Excavation Learning for Rigid Objects in Clutter

Autonomous excavation for hard or compact materials, especially irregular rigid objects, is challenging due to high variance of geometric and physical properties of objects, and large resistive force during excavation. In this letter, we propose a novel learning-based excavation planning method for...

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Veröffentlicht in:	IEEE robotics and automation letters 2021-10, Vol.6 (4), p.7373-7380
Hauptverfasser:	Lu, Qingkai, Zhang, Liangjun
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Clutter Deep learning in grasping and manipulation Excavation Learning manipulation planning Optimization perception for grasping and manipulation Physical properties Planning Prediction models Predictive models Rocks Task analysis Trajectory Trajectory planning
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