Iterative Learning-Based Admittance Control for Autonomous Excavation

Fernando, Heshan; Marshall, Joshua A.; Larsson, Johan

doi:10.1007/s10846-019-00994-3

Cited by 30 publications

(30 citation statements)

References 19 publications

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“…A typical loading cycle, with a bucket excavator or a wheel loader, has time duration in the range between 15-30 s [18,19]. The natural timescales of the rigid body motion is about 1-10 Hz and the control systems typically operate at a frequency below 100 Hz [20]. These multiple and separated length-and timescales are important to consider when modelling soil and granular media interacting with earthmoving equipment.…”

Section: Modelling and Simulation Of Soil And Earthmoving Equipmentmentioning

confidence: 99%

A Multiscale Model of Terrain Dynamics for Real-Time Earthmoving Simulation

Servin

Berglund

Nystedt

2020

Preprint

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A multiscale model for real-time simulation of terrain dynamics is explored. To represent the dynamics on different scales the model combines the description of soil as a continuous solid, as distinct particles and as rigid multibodies. The models are dynamically coupled to each other and to the earthmoving equipment. Agitated soil is represented by a hybrid of contacting particles and continuum solid, with the moving equipment and resting soil as geometric boundaries. Each zone of active soil is aggregated into distinct bodies, with the proper mass, momentum and frictional-cohesive properties, which constrain the equipment's multibody dynamics. The particle model parameters are pre-calibrated to the bulk mechanical parameters for a wide range of different soils. The result is a computationally efficient model for earthmoving operations that resolve the motion of the soil, using a fast iterative solver, and provide realistic forces and dynamic for the equipment, using a direct solver for high numerical precision. Numerical simulations of excavation and bulldozing operations are performed to validate model and measure the computational performance. Reference data is produced using coupled discrete element and multibody dynamics simulations at relatively high resolution. The digging resistance and soil displacements with the real-time multiscale model agree with the reference model up to 10-25%, and run more than three orders in magnitude faster.

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Section: Modelling and Simulation Of Soil And Earthmoving Equipmentmentioning

confidence: 99%

A Multiscale Model of Terrain Dynamics for Real-Time Earthmoving Simulation

Servin

Berglund

Nystedt

2020

Preprint

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“…In our design and experiments, and based on previous works about autonomous excavation [5], [6], the excavation process consists of the following finite states: 1) Pre-Pile. The boom and bucket are moved to pile entry positions, seen in 2) Pile.…”

Section: Related Work a Autonomous Excavation Backgroundmentioning

confidence: 99%

“…Control during the excavation process is described below, building upon on previous works within our lab regarding autonomous excavation [5], [6] Once the bucket has fully curled in, digging is complete…”

Section: Loader Controlmentioning

confidence: 99%

Hand Gesture-Based Control of a Front-End Loader

Tiesenhausen

Artan

Marshall

et al. 2020

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

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In this paper, we present the design and use of an instrumented glove consisting of a 9-DOF inertial measurement unit (IMU) and resistive flex sensors. The glove is used as a unique human-machine interface to control a Kubota R520s front-end loader, through input gestures, for the excavation of a fragmented rock pile. Raw sensor data from the glove is recorded and transmitted to a computer for gesture recognition. Recognized gestures are then used to command the loader to switch between dig states and control the excavation process. The system allows an operator to observe the entire process from beside the loader, providing them with valuable information about interactions between the loader bucket and rock pile not usually available when seated in the vehicle's cab. Preliminary experiments show that a novice operator was able to improve their performance by using the proposed system, evaluated based on metrics of total and dig completion times, as well as payload.

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“…A fully autonomous solution for robotic excavation must be able to identify and adapt to these variable conditions in order to achieve consistent bucket filling performance. Such a system would have obvious applications in construction [5], military [6], mining [7], and space exploration/development [8] where humans cannot be present for safety, logistical, and/or cost reasons.…”

Section: Introductionmentioning

confidence: 99%

“…One promising approach to autonomous excavation is a learning-based dig control strategy that has been developed through extensive field experimentation for robotic wheel loaders [3,7,9]. The dig controller is an admittance-based interaction control strategy, which utilizes force feedback to regulate bucket motion for scooping material.…”

Section: Introductionmentioning

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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Iterative Learning-Based Admittance Control for Autonomous Excavation

Cited by 30 publications

References 19 publications

A Multiscale Model of Terrain Dynamics for Real-Time Earthmoving Simulation

A Multiscale Model of Terrain Dynamics for Real-Time Earthmoving Simulation

Hand Gesture-Based Control of a Front-End Loader

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

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