A review on modeling, identification and servo control of robotic excavator

Yan, Jun; Li, Bo; Zeng, Yong; Qian, Haibo

doi:10.4314/ijest.v5i4.2

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…There exist early works on autonomous excavation that focus on parametric modeling of soil-tool interactions [10,11] for control development, and real-time identification of soil parameters for control optimization [12,13,14]. However, these models are not applicable to fragmented rock, which is significantly different from soil and other homogenous material.…”

Section: Further Related Workmentioning

confidence: 99%

What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning

Fernando

Marshall

2020

Automation in Construction

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The ability of robotic excavators to acquire meaningful knowledge about materials during digging can augment their autonomous functionality, as well as optimize downstream operations in construction and mining. Some material properties, such as rock sizes, can be determined visually, but these methods cannot see what lies beneath. In this work, a classification methodology that utilizes only proprioceptive force data acquired from an autonomous digging system and machine learning algorithms is proposed for excavation material identification. The consistent performance synonymous with autonomous digging systems allows for the use of basic features extracted from the force data for classification. A proof of concept of this novel approach to excavation material classification is demonstrated through a binary classification of rock and gravel materials. Force data were obtained from full-scale autonomous loading trials with a 14-tonne capacity load-haul-dump machine at a mining and construction test facility. Preliminary results achieved a classification accuracy of 90 %.

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