发表机构
Shanghai Jiao Tong University; Midea Group（上海交通大学; 美的集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对四足机器人在拥挤环境中导航难题，提出结合VO几何安全与端到端学习敏捷性的VOP-Nav系统，利用LiDAR数据隐式编码约束并预测安全速度区，VO预测兼具推理输入与训练奖励双重作用，实验验证其高效性与稳健性。
AI 中文摘要
在动态和拥挤环境中导航对四足机器人来说是重大挑战，因为存在严重传感器遮挡和不可预测的人类运动。现有方法存在权衡：基于模型的方法理论上保证安全，但依赖准确的障碍物运动估计，在密集人群中常失败；端到端学习方法稳健但缺乏障碍物运动预测能力。为此提出VOP-Nav，结合VO的几何安全性和端到端学习的敏捷适应性。仅使用本地车载观测，避免显式障碍物检测和跟踪管道。VOP-Net处理多帧LiDAR数据隐式编码动态约束并预测安全速度区域。VO预测在推理时作为导航策略输入，训练时作为奖励信号。在Isaac Gym中的评估表明VOP-Nav在平衡运动速度和避免碰撞方面比所有基线有更高成功率。在Unitree Go2四足机器人上的实际部署进一步验证了系统在复杂室内外动态环境中的稳健性和效率。
英文摘要
Navigating dynamic and crowded environments presents significant challenges for quadruped robots due to severe sensor occlusion and unpredictable human motion. Existing approaches face a trade-off: model-based methods, such as Velocity Obstacles (VO), theoretically guarantee safety but rely on accurate obstacle motion estimates that often fail in dense crowds, while end-to-end learning methods offer robustness but lack motion prediction capability of obstacles, leading to collisions or conservative behaviors. To solve this, we propose VOP-Nav, a novel navigation system that combines the geometric safety of VO with the agile adaptability of end-to-end learning. Using only local onboard observations, our system avoids explicit obstacle detection and tracking pipelines. The VOP-Net processes multi-frame LiDAR data to implicitly encode dynamic constraints and predict a safe velocity region derived from Velocity Obstacle theory. Importantly, the VO predictions serve a dual role: they are used as input to the navigation policy during inference and as a reward signal during training to encourage safe motion. Evaluations in Isaac Gym demonstrate that VOP-Nav achieves higher success rates than all baselines while balancing locomotion speed and collision avoidance. Real-world deployment on a Unitree Go2 quadruped robot further validates the system's robustness and efficiency in complex indoor and outdoor dynamic environments.
