Machine learning approach to automatic bucket loading

Dadhich, Siddharth; Bodin, Ulf; Sandin, Fredrik; Andersson, Ulf

doi:10.1109/med.2016.7535925

Cited by 33 publications

(20 citation statements)

References 11 publications

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“…Motion control strategies that regulate bucket motion to follow a pre-determined path are ineffective when excavating heterogenous materials, such as fragmented rock [15], because subsurface obstacles in heterogeneous material can cause large position errors that result in saturated actuation. AI-based approaches [4] that use artificial neural networks (ANNs), trained with imperfect expert operator data to determine actuator inputs during autonomous operation, are also not practical because these approaches require significant amounts of data to train and can exhibit unpredictable behaviour or poor performance in untrained situations. Fig.…”

Section: Related Workmentioning

confidence: 99%

Iterative Learning-Based Admittance Control for Autonomous Excavation

Fernando

Marshall

Larsson

2019

J Intell Robot Syst

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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-hauldump (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

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Section: Related Workmentioning

confidence: 99%

Iterative Learning-Based Admittance Control for Autonomous Excavation

Fernando

Marshall

Larsson

2019

J Intell Robot Syst

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“…Reference [13] proposes a machine learning based function for bucket-filling to load medium-course gravel. We build upon this work and see if machine learning models can predict the motion of a wheel loader's pistons in terms of the velocity of lift/tilt cylinders.…”

Section: Automatic Bucket-fillingmentioning

confidence: 99%

From Tele-Remote Operation to Semi-Automated Wheel-Loader

Dadhich¹,

Bodin²,

Sandin³

et al. 2018

IJEETC

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This paper presents experimental results with tele-remote operation of a wheel-loader and proposes a way towards semi-automation. The different components of the tele-remote setup are described in the paper. Short loading cycle is an operation cycle, commonly used at quarry and construction sites for moving gravel from piles onto trucks. We present results from short loading cycle experiments with three operators comparing productivity between teleremote operation and manual operation. Productivity loss of 42% with tele-remote operation motivates the case for more automation. We propose a method to automate the bucketfilling step, which is one of the key functionality of a wheelloader.

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“…This led Marshall et al (2008) to postulate an admittance control scheme for robotic excavation of fragmented rock that regulates bucket motion based on feedback of interaction forces. Compared to intelligence-based approaches to robotic excavation (Dadhich et al, 2016b) that require substantial training data and exhibit unpredictable behaviour in untrained situations, the admittance control scheme proposed by Marshall et al (2008) provides an effective framework for use in practical implementations.…”

Section: Related Workmentioning

confidence: 99%

Towards Controlling Bucket Fill Factor in Robotic Excavation by Learning Admittance Control Setpoints

Fernando

Marshall

Almqvist

et al. 2017

Field and Service Robotics

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This paper investigates the extension of an admittance control scheme toward learning and adaptation of its setpoints to achieve controllable bucket fill factor for robotic excavation of fragmented rock. A previously developed Dig Admittance Controller (DAC) is deployed on a 14-tonne capacity robotic load-haul-dump (LHD) machine, and full-scale excavation experiments are conducted with a rock pile at an underground mine to determine how varying DAC setpoints affect bucket fill factor. Results show that increasing the throttle setpoint increases the bucket fill factor and increasing the bucket's reference velocity setpoint decreases the bucket fill factor. Further, the bucket fill factor is consistent for different setpoint values. Based on these findings, a learning framework is postulated to learn DAC setpoint values for a desired bucket fill factor over successive excavation iterations. Practical implementation problems such as bucket stall and wheel-slip are also addressed, and improvements to the DAC design are suggested to mitigate these problems.

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Machine learning approach to automatic bucket loading

Cited by 33 publications

References 11 publications

Iterative Learning-Based Admittance Control for Autonomous Excavation

Iterative Learning-Based Admittance Control for Autonomous Excavation

From Tele-Remote Operation to Semi-Automated Wheel-Loader

Towards Controlling Bucket Fill Factor in Robotic Excavation by Learning Admittance Control Setpoints

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