发表机构
neobotics（尼奥机器人）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究推出开源标准化1:12比例自主赛车NeoRacer，兼具高算力与低成本，可作为自主赛车算法的基准测试平台，还可用于相关教育场景，已通过试点部署验证可行性。
AI 中文摘要
许多科学领域依赖标准基准与共享平台来提升研究的可评审性与可复现性，但自主系统研究仍缺乏被广泛接受的开源硬件。在已实现标准化的领域，研究进展会显著加快，自主赛车领域便是典型例证：该领域的团队常构建定制系统或购买小众且昂贵的车辆，这使得控制与机器人学研究及教育难以比较和复现；高昂的成本也限制了资金不足的实验室之外的群体获取相关资源，而平价的教育机器人通常算力不足。为解决这一缺口，我们推出了NeoRacer，一款开源的1:12比例自主赛车平台，其核心组件包括NVIDIA Jetson Orin Nano（算力67 TOPS）、270°激光雷达、120帧全局快门相机以及9轴IMU。NeoRacer预组装后的售价为2699美元，其算力是同类平台的3倍以上，而价格仅为最近的预组装替代产品的一半不到。该平台由Neobotics基金会与Seeed Studio联合开发，由Seeed Studio制造，将开源硬件与软件设计及可扩展、可重复的生产相结合。这款模块化、可扩展的平台为各机构的自主赛车算法提供了标准化的基准测试环境。我们描述了其硬件/软件架构、两次试点部署（MIT IAP项目15名学生参与，BU CPS实验室10名学生参与）的设计决策，以及关键的成本-性能权衡。该平台的硬件采用CERN-OHL-S v2许可，软件采用GPLv3许可，所有设计文件、固件及ROS2软件包均已公开可访问。
英文摘要
Many scientific fields rely on standard benchmarks and shared platforms to improve review and reproducibility, but autonomous systems research still lacks widely accepted open hardware. Where standardization has emerged, progress has accelerated. This is especially evident in autonomous racing, where teams often build custom systems or buy niche, expensive vehicles, making control and robotics research and education hard to compare and reproduce. High costs also limit access outside well-funded labs, while affordable educational robots are often underpowered. To address this gap, we present NeoRacer, an open-source 1:12 scale autonomous racing platform. It is built around an NVIDIA Jetson Orin Nano (67 TOPS), a 270° LiDAR, a 120 fps global-shutter camera, and a 9-axis IMU. NeoRacer ships pre-assembled for USD 2,699, offering over 3x the compute of comparable platforms at less than half the cost of the nearest pre-assembled alternative. Co-developed by the Neobotics Foundation and Seeed Studio, and manufactured by Seeed Studio, NeoRacer combines open hardware and software design with scalable, repeatable production. The modular, extensible platform provides a standardized benchmarking environment for autonomous racing algorithms across institutions. We describe the hardware/software architecture, design decisions from two pilot deployments (MIT IAP, 15 students; BU CPS Lab, 10 students), and key cost-performance tradeoffs. Hardware is licensed under CERN-OHL-S v2 and software under GPLv3, with all design files, firmware, and ROS2 packages publicly accessible.
