论文链接：https://ieeexplore.ieee.org/document/10980470

视频链接：https://www.bilibili.com/video/BV1nSEQz1Ebw

本文提出了一种新颖的运动规划方法，通过构建时空约束速度障碍（STVO）区域，实现无人地面车辆（UGV）在行人密集环境中的安全导航。相较于现有基于速度障碍（VO）类方法，本研究的创新性主要体现在两个方面：1）在速度空间中引入时间约束，该约束同时作为自适应碰撞时间阈值，可根据实时感知的行人密度动态调整速度障碍区域；2）通过判断UGV与VO区域的位置关系，提出空间约束机制以预判并规避潜在的被围困风险。此外，本研究还设计了速度重采样机制以防止UGV陷入局部困境。该方法显著增强了UGV的导航灵活性与安全性，从而大幅提升了在动态复杂行人环境中的实际导航成功率。大量仿真实验和真实场景测试，尤其是在行人高度集中的校园食堂进行的实验，充分验证了本方法优异的实用性能。

        Y. Song, R. Wang*, Q. Bi, Z. Pan, H. Gao and X. Zhang. STVO: Spatial-Temporal Constrained Velocity Obstacle for Safe Navigation among Pedestrians, IEEE Transactions on Vehicular Technology, 2025.

Abstract

This paper presents a novel motion planning method that safely navigates the unmanned ground vehicle (UGV) among pedestrian-rich environments by constructing the spatial-temporal constrained velocity obstacle (STVO) region. Compared with existing velocity obstacle (VO) type-based methods, the novelty of this work is twofold: 1) An innovative temporal constraint is introduced into the velocity space, also playing the role as an adaptive collision time constraint to dynamically adjust the velocity obstacle region according to real-time perceived pedestrian density; 2) The spatial constraint is proposed to foresee and evade potential entrapments for the UGV, by carefully considering the position relationship between the UGV and the VO region. Additionally, a velocity resampling mechanism is further designed to prevent the UGV from being trapped. Above techniques augments the navigation flexibility and safety of the UGV, thereby significantly improves the success rate of practical navigation in dynamic and challenging pedestrian environments. Extensive simulations and real-world experiments have been conducted, especially the experiment in pedestrian-rich school cafeteria demonstrating satisfactory practicality of our approach.