End-to-end learning for off-road terrain navigation using the chrono open-source simulation platform

DOI10.1007/s11044-022-09816-1OpenAlexW4220794941WikidataQ115146152 ScholiaQ115146152MaRDI QIDQ2142333

Jay Taves, Aaron Young, Dan Negrut, Alessandro Tasora, Asher Elmquist, Simone Benatti, Radu Serban

Publication date: 27 May 2022

Published in: Multibody System Dynamics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11044-022-09816-1

zbMATH Keywords

simulationreinforcement learningdeformable terrainoff-road autonomous vehicles

Mathematics Subject Classification ID

Dynamics of multibody systems (70E55)

Uses Software

• Matlab
• SWIG
• OpenAI Gym
• Chrono
• PyTorch
• GAZEBO
• Adam
• Unity3D
• GitHub
• Unreal Engine
• MuJoCo
• AirSim
• Baselines
• CARLA
• TORCS
• NVIDIA PhysX
• LGSVL

Cites Work

• Billion degree of freedom granular dynamics simulation on commodity hardware via heterogeneous data-type representation
• Modeling granular material dynamics and its two-way coupling with moving solid bodies using a continuum representation and the SPH method
• Chrono: An Open Source Multi-physics Dynamics Engine
• A Precision Analysis of Camera Distortion Models
• Unnamed Item

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