米洛，一款完全自主的室内/室外机器人导盲犬
Milo, a Fully Autonomous Indoor/Outdoor Robotic Guide Dog
浏览论文内容
中文总结 AI 辅助
研究旨在开发机器人导盲犬平台，核心方法是构建含改装机器人、感知与导航堆栈的系统，主要贡献是实现完全自主、室内外可用的米洛平台，开源软硬件，且导航表现优于基线。
中文摘要 AI 辅助
许多盲人和视力低下者依靠导盲犬进行即时导航，如保持在路线上、避开障碍物和行人。但导盲犬获取和维护成本高，等待名单长且寿命相对短。现有机器人导盲犬方法有缺陷。本文介绍米洛，首个开源、低成本的机器人导盲犬平台。它完全自主，无需环境先验知识，计算全部板载，适用于室内外导航。系统由改装机器人、感知堆栈和导航堆栈组成。在室内外障碍课程中评估，与基于代价地图的基线比较，导航更平稳且与使用者碰撞更少。还开源了硬件指令和软件堆栈。
英文摘要
Many Blind and Low-Vision (BLV) people rely on guide dogs for moment-to-moment navigation, such as staying on path and avoiding obstacles and pedestrians. However, guide dogs are expensive to acquire and maintain (approximately \$50k USD plus ongoing costs), often involve long waiting lists, and have relatively short life expectancies. While robot guide dogs offer a promising alternative, existing approaches exploring this idea suffer from several drawbacks: They often lack the autonomy required for real-world deployment, relying on prior 3D scans of the environment, external computation, or limited awareness of the handler. In this work, we present Milo, the first open-source, low-cost (approximately \$2k USD) robotic guide dog platform capable of fulfilling the basic collaborative navigation role expected of a guide dog. Milo is fully autonomous, requiring no a priori knowledge of the environment, completely self-contained with all computation performed onboard, and suitable for both indoor and outdoor navigation while avoiding obstacles and pedestrians. Our system consists of a modified Unitree Go2 robot (equipped with onboard compute, sensors, and a handle), a perception stack combining voxel mapping with floor, obstacle, and pedestrian detection, and a navigation stack based on an obstacle-avoidance policy trained in a custom bird's-eye-view simulator. We evaluate Milo in real indoor and outdoor obstacle courses and compare it against a costmap-based baseline, demonstrating smoother navigation and fewer handler collisions. To maximize accessibility for BLV users, we release both the robot hardware instructions and the complete software stack as open source.
发表机构
- Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）
- Polytechnique Montreal（蒙特利尔理工大学）
机构由 AI 辅助整理，请以论文原文为准。
