An Overview of Autonomous Loading of Bulk Material

Hemami, Ahmad; Hassani, Ferri

doi:10.22260/isarc2009/0013

Cited by 32 publications

(19 citation statements)

References 28 publications

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“…Automation of a manipulator under constant and forceful contact with an uncertain environment has proved, however, to be a challenging control problem. Autonomous excavation is one of the oldest applications in field robotics (see the reviews [1]- [3]) yet, despite almost three decades of research, no commercial deployment of a fully autonomous excavator has been reported to date.…”

Section: Introductionmentioning

confidence: 99%

Combined ILC and Disturbance Observer for the Rejection of Near-Repetitive Disturbances, With Application to Excavation

Maeda

Manchester

Rye

2015

IEEE Trans. Contr. Syst. Technol.

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This paper proposes a new control structure for tasks where explicit disturbance compensation is not only critical for overcoming poor feedback performance but is also challenging due to the complexity and nonrepetitive nature of the interaction between the plant and the environment. The approach proposed uses a particular form of iterative learning control (ILC) to estimate the previous disturbances, which are used as a preview of the disturbance in the next iteration. A disturbance observer is used to compensate for the difference between the ILC prediction and the true disturbance. The controller is evaluated and compared with a proportional controller, with ILC, and with an observer-based controller in extensive field trials using an automated excavator.The generic problem addressed in this paper concerns applications where an existing feedback controller provides insufficient disturbance rejection due, for example, to bandwidth 1063-6536

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Section: Introductionmentioning

confidence: 99%

Combined ILC and Disturbance Observer for the Rejection of Near-Repetitive Disturbances, With Application to Excavation

Maeda

Manchester

Rye

2015

IEEE Trans. Contr. Syst. Technol.

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“…where the joint velocity vector for the wheel, boom link and bucket isq = [φ w ,φ l ,φ b ] T and the joint angles are defined as in [2].…”

Section: Machine Modelmentioning

confidence: 99%

“…The system and loading operation models, described in Sec. 2 Dynamics [30] and run with the settings listed in…”

Section: Simulationmentioning

confidence: 99%

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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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“…The leading machine properties during operation are productivity, energy efficiency and operability [3]. There are three key technical challenges [4] about automatizing the LHD: (1) obtaining bulk material pile models, (2) planning optimal shoveling trajectory, and (3) developing a controller that responds quickly to rapidly changing external forces [5]. We focus on the second challenge.…”

Section: Introductionmentioning

confidence: 99%

Bucket Trajectory Optimization under the Automatic Scooping of LHD

et al. 2019

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We propose an optimal planning scheme of the bucket trajectory in the LHD (Load-Haul-Dump) automatic shoveling system to improve the effectiveness of the scooping operation. The research involves simulation of four typical shoveling methods, optimization of the scooping trajectory, establishment of a reaction force model in the scooping process and determination of optimal trajectory. Firstly, we compared the one-step, step-by-step, excavation and coordinated shoveling method by the Engineering Discrete Element Method (EDEM) simulation. The coordinated shoveling method becomes the best choice on account of its best comprehensive performance among the four methods. Based on the coordinated shoveling method, the shape of the optimized trajectory can be roughly determined. Then, we established a model of bucket force during the shoveling process by applying Coulomb's passive earth pressure theory for the purpose of calculating energy consumption. The trajectory is finally determined through optimizing the minimum energy consumption in theory. The theoretical value is verified by the EDEM simulation.In Section 2, we introduced the resistance force received by bucket, which is widely used in the process of scooping. In Section 3, we performed and compared four typical shoveling method simulations. In Section 4, we formulated the relation between energy consumption and insertion depth and then optimized the trajectory with minimum energy consumption.

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An Overview of Autonomous Loading of Bulk Material

Cited by 32 publications

References 28 publications

Combined ILC and Disturbance Observer for the Rejection of Near-Repetitive Disturbances, With Application to Excavation

Combined ILC and Disturbance Observer for the Rejection of Near-Repetitive Disturbances, With Application to Excavation

Computational exploration of robotic rock loading

Bucket Trajectory Optimization under the Automatic Scooping of LHD

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