Toward Autonomous Excavation of Fragmented Rock: Full-Scale Experiments

Marshall, Joshua A.; Murphy, PJ; Daneshmend, Laeeque K.

doi:10.1109/tase.2007.912709

Cited by 37 publications

(50 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A notable amount of work has been done on the hauling and dumping stages for underground mining machines (e.g., for a review, see [5] and references therein). However, the autonomous excavation problem, especially in fragmented rock, remains a much less developed area [6], [7]. In mining, the unique challenge of handling large rocks in a rock pile is twofold.…”

Section: A Problem Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Identification of Large Fragments in a Pile of Broken Rock Using a Time-of-Flight Camera

McKinnon

Marshall

2014

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

This paper presents a solution to part of the problem of making robotic or semi-robotic digging equipment less dependant on human supervision. A method is described for identifying rocks of a certain size that may affect digging efficiency or require special handling. The process involves three main steps. First, by using range and intensity data from a time-of-flight (TOF) camera, a feature descriptor is used to rank points and separate regions surrounding high scoring points. This allows a wide range of rocks to be recognized because features can represent a whole or just part of a rock. Second, these points are filtered to extract only points thought to belong to the large object. Finally, a check is carried out to verify that the resultant point cloud actually represents a rock. Results are presented from field testing on piles of fragmented rock. Note to Practitioners-This paper presents an algorithm to identify large boulders in a pile of broken rock as a step towards an autonomous mining dig planner. In mining, piles of broken rock can contain large fragments that may need to be specially handled. To assess rock piles for excavation, we make use of a TOF camera that does not rely on external lighting to generate a point cloud of the rock pile. We then segment large boulders from its surface by using a novel feature descriptor and distinguish between real and false boulder candidates. Preliminary field experiments show promising results with the algorithm performing nearly as well as human test subjects.

show abstract

Section: A Problem Backgroundmentioning

confidence: 99%

“…Second, the structure and composition of rock piles can vary significantly, both locally and from mine to mine. This paper focuses on the dig planning subproblem, and not machine control; for control, see [7] and references therein.…”

Section: A Problem Backgroundmentioning

confidence: 99%

Automatic Identification of Large Fragments in a Pile of Broken Rock Using a Time-of-Flight Camera

McKinnon

Marshall

2014

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…The requirements for robotic scooping using an LHD have been analysed in detail by Hemami (1994). Experimental results can be found in the work of Hemami (1993), Petty (1997), Sarata et al (2008) and Marshall et al (2008). Invariably, all researchers agree that the biggest challenge to robotic excavation is the interaction between the tool and the terrain (ground, pile).…”

Section: Related Researchmentioning

confidence: 99%

“…The more recent work of Marshall et al (2008) and Schmidt et al (2010) present a number of approaches to the dig and dump planning processes. Such approaches utilise a 2.5D (height-field) representation of the environment constructed from a registered point cloud and compare with a desired surface profile to obtain the material to be removed in the dig process.…”

Section: Situational Awarenessmentioning

confidence: 99%

Experiments in Autonomous Earth Moving

Bonchis

Hillier

Ryde

et al. 2011

IFAC Proceedings Volumes

View full text Add to dashboard Cite

This paper presents a technology demonstrator currently under development and describes experiments carried out to date in autonomous bulk material handling using mobile equipment. Our primary platform is a Bobcat S185 skid-steer loader instrumented with an onboard computer, a sensor suite, and a communication link that support various levels of automation, from remote control to supervised autonomy. We present the main system components and discuss the autonomous cleaning of spillage and carryback, common bulk handling task in mining, currently executed exclusively using manually and/or remotely operated loaders. The system architecture is based on Spring, a Robotics Software Framework developed by CSIRO to support rapid development of new robotic systems, distributed as an Open Source package.

show abstract

“…Impedance control [12,13,14,15] is well-suited to tasks like trenching and landscaping, where the final target shape is more important than filling the bucket efficiently. This realization led Marshall in [16] to propose adapting Seraji's general admittance controller [17] for loading by controlling the admittance between the robot and the muck pile. Marshall's proposed admittance controller for loading was never tested, but was ultimately used as the starting point for the ALC presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

Admittance Control for Robotic Loading: Underground Field Trials with an LHD

Dobson

Marshall

Larsson

2016

Springer Tracts in Advanced Robotics

View full text Add to dashboard Cite

In this paper we describe field trials of an admittance-based Autonomous Loading Controller (ALC) applied to a robotic Load-Haul-Dump (LHD) machine at an underground mine nearÖrebro, Sweden. The ALC was tuned and field tested by using a 14-tonne capacity Atlas Copco ST14 LHD mining machine in piles of fragmented rock, similar to those found in operational mines. Several relationships between the ALC parameters and our performance metrics were discovered through the described field tests. During these tests, the tuned ALC took 61 % less time to load 39 % more payload when compared to a manual operator. The results presented in this paper suggest that the ALC is more consistent than manual operators, and is also robust to uncertainties in the unstructured mine environment.

show abstract

Toward Autonomous Excavation of Fragmented Rock: Full-Scale Experiments

Cited by 37 publications

References 14 publications

Automatic Identification of Large Fragments in a Pile of Broken Rock Using a Time-of-Flight Camera

Automatic Identification of Large Fragments in a Pile of Broken Rock Using a Time-of-Flight Camera

Experiments in Autonomous Earth Moving

Admittance Control for Robotic Loading: Underground Field Trials with an LHD

Contact Info

Product

Resources

About