ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware
<|reference_start|>ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware: Autonomous racing in robotics combines high-speed dynamics with the necessity for reliability and real-time decision-making. While such racing pushes software and hardware to their limits, many existing full-system solutions necessitate complex, custom hardware and software, and usually focus on Time-Trials rather than full unrestricted Head-to-Head racing, due to financial and safety constraints. This limits their reproducibility, making advancements and replication feasible mostly for well-resourced laboratories with comprehensive expertise in mechanical, electrical, and robotics fields. Researchers interested in the autonomy domain but with only partial experience in one of these fields, need to spend significant time with familiarization and integration. The ForzaETH Race Stack addresses this gap by providing an autonomous racing software platform designed for F1TENTH, a 1:10 scaled Head-to-Head autonomous racing competition, which simplifies replication by using commercial off-the-shelf hardware. This approach enhances the competitive aspect of autonomous racing and provides an accessible platform for research and development in the field. The ForzaETH Race Stack is designed with modularity and operational ease of use in mind, allowing customization and adaptability to various environmental conditions, such as track friction and layout. Capable of handling both Time-Trials and Head-to-Head racing, the stack has demonstrated its effectiveness, robustness, and adaptability in the field by winning the official F1TENTH international competition multiple times.<|reference_end|>
@article{baumann2024forzaeth,
    title={ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully
  Commercial off-the-Shelf Hardware},
    author={Nicolas Baumann, Edoardo Ghignone, Jonas K"uhne, Niklas Bastuck,
  Jonathan Becker, Nadine Imholz, Tobias Kr"anzlin, Tian Yi Lim, Michael
  L"otscher, Luca Schwarzenbach, Luca Tognoni, Christian Vogt, Andrea Carron,
  Michele Magno},
    journal={arXiv preprint arXiv:2403.11784},
    year={2024},
    doi={10.1002/rob.22429},
    number={ROB22429},
    archivePrefix={arXiv},
    eprint={2403.11784},
    primaryClass={cs.RO cs.SE cs.SY eess.SY}
}