This paper presents the development and field validation of an iterative learning-based admittance control algorithm for autonomous excavation in fragmented rock using robotic wheel loaders. An admittance control strategy is augmented with iterative learning, which automatically updates control parameters based on the error between a target bucket fill weight and the measured fill weight at the end of each excavation pass. The algorithm was validated through full-scale autonomous excavation experiments with a 14-tonne capacity load-haul-dump (LHD) machine and two different types of excavation materials: fragmented rock and gravel. In both excavation scenarios, the iterative learning algorithm is able to update the admittance control parameters for a specified target bucket fill weight, eliminating the need to manually re-tune control parameters as material characteristics change. These results have practical significance for increasing the autonomy of robotic wheel loaders used in mining and construction.
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The authors would like to thank Håkan Almqvist for his assistance with the field experiments; as well as Ola Petersson, Jörgen Appelgren and Robert Lundh for many useful discussions; and Qing Huang for assistance with programming.
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This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) under project RGPIN-2015-04025, the Swedish Knowledge Foundation (KK-stiftelsen) under project 20150282, and by Epiroc Rock Drills AB (Sweden).
This work was completed while the second author was a KKS International Visiting Professor at the Centre for Applied Autonomous Sensor Systems (AASS) in the School of Science and Technology at Örebro University, Sweden.
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Fernando, H., Marshall, J.A. & Larsson, J. Iterative Learning-Based Admittance Control for Autonomous Excavation. J Intell Robot Syst 96, 493–500 (2019). https://doi.org/10.1007/s10846-019-00994-3
- Autonomous excavation
- Iterative learning
- Admittance control
- Mining robotics