Development of an Integrated Tracking Control Algorithm for Digging Operations of an Excavator

Reginald, Niraj; Seo, Jaho; Abdullah, Rohani

doi:10.23919/iccas50221.2020.9268434

Cited by 4 publications

(2 citation statements)

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“…The intensity and force index provide a reference (desired) value for force tracking control that varies according to the ground conditions, and thus can be used to adaptively compensate for the ground resistive force. Therefore, the 5D map can fundamentally contribute to enhancing tracking control performance during digging operations [ 27 , 28 ].…”

Section: Resultsmentioning

confidence: 99%

Development of Integrative Methodologies for Effective Excavation Progress Monitoring

Abdullah

Seo

Khajepour

2021

Sensors

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Excavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity that are the ongoing issues in this area. This paper covers three aspects of effective excavation progress monitoring that include excavation volume estimation, occlusion area detection, and 5D mapping. The excavation volume estimation component enables estimating the bucket volume and ground excavation volume. To achieve mapping of the hidden or occluded ground areas, integration of proprioceptive and exteroceptive sensing data was adopted. Finally, we proposed the idea of 5D mapping that provides the info of the excavated ground in terms of geometric space and material type/properties using a 3D ground map with LiDAR intensity and a ground resistive index. Through experimental validations with a mini excavator, the accuracy of the two different volume estimation methods was compared. Finally, a reconstructed map for occlusion areas and a 5D map were created using the bucket tip’s trajectory and multiple sensory data with convolutional neural network techniques, respectively. The created 5D map would allow for the provision of extended ground information beyond a normal 3D ground map, which is indispensable to progress monitoring and control of autonomous excavation.

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Section: Resultsmentioning

confidence: 99%

Development of Integrative Methodologies for Effective Excavation Progress Monitoring

Abdullah

Seo

Khajepour

2021

Sensors

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“…Soil parameter estimation enables prediction of interaction forces by changing tool parameters such as the depth of cut, the attack angle of the tool, and digging strategies [55]. Recent effort has incorporated the soil-tool interaction also in designing an optimal bucket to achieve the highest productivity of the tool and the platform as a whole [56].…”

Section: Tool-soil Interactionmentioning

confidence: 99%

Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey

Nguyen

2022

Robotica

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There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.

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Application of physics-informed machine learning for excavator working resistance modeling

Li,

Wang,

Chen

et al. 2024

Mechanical Systems and Signal Processing

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Development of an Integrated Tracking Control Algorithm for Digging Operations of an Excavator

Cited by 4 publications

References 20 publications

Development of Integrative Methodologies for Effective Excavation Progress Monitoring

Development of Integrative Methodologies for Effective Excavation Progress Monitoring

Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey

Application of physics-informed machine learning for excavator working resistance modeling

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