Demonstrating HOUND: A Low-cost Research Platform for High-speed Off-road Underactuated Nonholonomic Driving
Abstract
Off-road autonomy, crucial for applications such as search-and-rescue, agriculture, and planetary exploration, poses unique problems due to challenging terrains, as well as due to the risk involved in testing or deploying such systems. Accessible platforms have the potential to widen the field to a broader set of researchers and students. Existing efforts in making on-road autonomy more accessible have seen success, yet aggressive off-road autonomy remains underserved. We seek to fill this gap by introducing HOUND, a 1/10th-scale, inexpensive, off-road autonomous car platform that can handle challenging outdoor terrains at high speeds. To aid development speed, we integrate HOUND with BeamNG, a state-of-the-art driving simulator to enable both software in the loop as well as hardware in the loop testing. To reduce the extent of ruggedization required, and thus cost, we integrate a rollover prevention system as a safety feature into the platform. Real-world trials over 50 kilometers demonstrate the platform's longevity and effectiveness over varied terrains and speeds. Build instructions, datasets, and code disseminated via: https://sites.google.com/view/prl-hound/home
Keywords
Cite
@article{arxiv.2311.11199,
title = {Demonstrating HOUND: A Low-cost Research Platform for High-speed Off-road Underactuated Nonholonomic Driving},
author = {Sidharth Talia and Matt Schmittle and Alexander Lambert and Alexander Spitzer and Christoforos Mavrogiannis and Siddhartha S. Srinivasa},
journal= {arXiv preprint arXiv:2311.11199},
year = {2024}
}
Comments
12 pages, 10 figures, accepted for Robotics: Science and Systems 2024