Key challenges in automation of earth-moving machines

Dadhich, Siddharth; Bodin, Ulf; Andersson, Ulf

doi:10.1016/j.autcon.2016.05.009

Cited by 209 publications

(117 citation statements)

References 30 publications

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“…Recent literature overviews of robotic loading of piled and fragmented rock material can be found in [1,2,3]. Hemami introduced kinematic modelling of an LHD as a robotic manipulator and suggested an end-effector force model to study bucket trajectories that minimise energy consumption [4].…”

Section: Introductionmentioning

confidence: 99%

Computational exploration of robotic rock loading

Lindmark

Servin

2018

Robotics and Autonomous Systems

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PreprintThis is the submitted version of a paper published in Robotics and Autonomous Systems.Citation for the original published paper (version of record):Lindmark, D M., Servin, M. (2018) Computational exploration of robotic rock loading Robotics and Autonomous SystemsAbstract A method for simulation-based development of robotic rock loading systems is described and tested. The idea is to first formulate a generic loading strategy as a function of the shape of the rock pile, the kinematics of the machine and a set of motion design variables that will be used by the autonomous control system. The relation between the loading strategy and resulting performance is then explored systematically using contacting multibody dynamics simulation, multiobjective optimisation and surrogate modelling. With the surrogate model it is possible to find Pareto optimal loading strategies for dig plans that are adapted to the current shape of the pile. The method is tested on a load-haul-dump machine loading from a large muck pile in an underground mine, with the loading performance measured by productivity, machine wear and rock debris spill that cause interruptions. 45 model includes actuator models and a control algorithm to execute the motion plan. The actuators have limited force, and the machine may lose ground traction if the pile resistance is large enough to cause frictional slippage. Consequently, the simulated bucket trajectory may deviate from the plan. This 50 provides realism that is not available when using a kinematic model of the vehicle or bucket. For each simulation, the forces and moments in the joints of the machine are measured, as well as the loading cycle time, final bucket fill ratio as well as the resulting state of the pile. Both the actuating forces and the 55 constraint forces keeping the joints in place are measured, and may be analysed for power consumption and mechanical wear, respectively.A surrogate model is constructed for the relationships between the design variables and the objective functions, i.e., be-60 tween the loading strategy and loading performance. This enables systematic exploration of the design space and the possibility to steer the computational resources to the most interesting parts of the design space. Using the surrogate model, it is easy to answer specific questions regarding loading strate-65

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Section: Introductionmentioning

confidence: 99%

Computational exploration of robotic rock loading

Lindmark

Servin

2018

Robotics and Autonomous Systems

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“…An energy-efficient trajectory would be to slice a thin chip of soil until the bucket is filled [5]. Therefore, the bucket filling is a worthwhile task for automation.The key challenges in automation and tele-remote operation of earthmoving machines concerning these levels of automation are summarized in [6]. Especially, the short-loading cycle of a wheel loader with an in-depth review of different automatic bucket loading strategies is analyzed.…”

Section: Levels Of Automationmentioning

confidence: 99%

“…• Path planning for driving • Collision detection, avoidance and navigation • Path planning for the work equipment Thereby the following requirements have to be taken into account [6] • Performance of the work task (fill factor and cycle time)…”

Section: Levels Of Automationmentioning

confidence: 99%

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Cloud-Based System Architecture for Driver Assistance in Mobile Machinery

Koch

Beck

Heß

et al. 2017

Linköping Electronic Conference Proceedings

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Using the example of a wheel loader, this paper presents a cloud-based system architecture enabling intelligent machine behavior. In order to achieve the final goal of a fully automated bucket filling routine, while controlling the loaders engine, travel drive and attachment, different levels of automation are processed gradually. As a first step towards automation, driver assistance can be considered. The paper explains the design choices for a cyber-physicalsystem architecture in the context of construction machinery. This comprises the communication framework and the cloud-application for self-adapting systems (i.e. the MAPE-K loop). As a validation of the architecture and as a demonstrator, a driver assistance functionality has been implemented. Calculations from the cloud-application give the operator feedback about efficiency, loads and task status. A developed visualization app on a tablet serves as user-interface. Concurrent simulation allow an optimization of control algorithms for the machine control and the trajectory planning. Besides changing the parametrization of the underlying models, a solution to change ECU-code at run-time without interrupting the operation is presented. The developed system architecture is the basis for further implementations of adaptive algorithms that improve future machine operation.

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“…The progress toward the automation of moving working machines is discussed in the following five steps: (1) manual operation, (2) in-sight remote operation, (3) teleremote operation (4), assisted teleremote (semiautomation) operation, and (5) fully autonomous operation [8][9][10]. Steps three and four require good video feedback, which is challenging to achieve over wireless networks since the bandwidth can vary frequently and unpredictably.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Video with SCReAM over LTE for Remote‐Operated Working Machines

Johansson

Dadhich

Bodin

et al. 2018

Wireless Communications and Mobile Computing

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Remote operation is a step toward the automation of mobile working machines. Safe and efficient teleremote operation requires good-quality video feedback. Varying radio conditions make it desirable to adapt the video sending rate of cameras to make the best use of the wireless capacity. The adaptation should be able to prioritize camera feeds in different directions depending on motion, ongoing tasks, and safety concerns. Self-Clocked Rate Adaptation for Multimedia (SCReAM) provides a rate adaptation algorithm for these needs. SCReAM can control the compression used for multiple video streams using differentiating priorities and thereby provide sufficient congestion control to achieve both low latency and high video throughput. We present results from the testing of prioritized adaptation of four video streams with SCReAM over LTE and discuss how such adaptation can be useful for the teleremote operation of working machines.

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Key challenges in automation of earth-moving machines

Cited by 209 publications

References 30 publications

Computational exploration of robotic rock loading

Computational exploration of robotic rock loading

Cloud-Based System Architecture for Driver Assistance in Mobile Machinery

Adaptive Video with SCReAM over LTE for Remote‐Operated Working Machines

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