Control of Rough Terrain Vehicles Using Deep Reinforcement Learning

We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate. This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle with two frame articulation...

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Vydáno v:	IEEE robotics and automation letters Ročník 7; číslo 1; s. 390 - 397
Hlavní autoři:	Wiberg, Viktor, Wallin, Erik, Nordfjell, Tomas, Servin, Martin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial Intelligence Automatic Control autonomous vehicle navigation Biomedical Engineering Blocking Computational modeling Computer Science Computer Science Applications Computer Vision and Pattern Recognition Control and Optimization Control and Systems Engineering Control methods Controllers datalogi Deep learning Deep learning methods Forestry fysik Human-Computer Interaction Joints Mechanical Engineering model learning for control Operators Physics reglerteknik Reinforcement learning Robotics robotics and automation in agriculture and forestry Robotteknik och automation Rough terrain Skills Solid modeling Suspensions (mechanical systems) Vehicles Virtual environments Wheels
ISSN:	2377-3766, 2377-3766
On-line přístup:	Získat plný text
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