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DeepHaul: a deep learning and reinforcement learning-based smart automation framework for dump trucks by Danish Ali; Samuel Frimpong is a Computer Science article available to read on EtoBox.

What is DeepHaul: a deep learning and reinforcement learning-based smart automation framework for dump trucks about?

In surface mining operations, the haul truck contributes the largest share to the overall operating cost, along with being a significant contributor to the injuries and fatalities to the operators; therefore, measures need to be taken for improving truck haulage safety and efficiency. In the absence of any major advancement in the automation technology for the mining sector, this study attempted to eliminate the existing technology lull by developing a novel DeepHaul framework for inducing smartness and intelligence within any dump truck, using the advanced algorithmic knowledge of artificial intelligence and machine learning. The DeepHaul framework consisted of two major components: first, inducing an object recognition ability by using deep learning methodology, for any dump truck. Experiments were conducted with different deep learning architectures, different training batch size ranges, and various image sizes for developing an optimum deep learning model with state-of-the-art performance for the haul truck. The second component consisted of the steering action decision making ability for the dump truck based on the given state of the haulage route. A reinforcement learning-bas

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Author

Danish Ali; Samuel Frimpong

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)