arXiv 2506.062552025-06-06cs.RO

从 NLVO 到 NAO：利用速度障碍与加速度障碍的反应式机器人导航

From NLVO to NAO: Reactive Robot Navigation using Velocity and Acceleration Obstacles

Mechanical Engineering and Mechatronics, Ariel University（人工智能与机电工程系，阿维尔大学）

Asher Stern, Zvi Shiller

备注：: 8 pages, 12 figures. arXiv admin note: text overlap with arXiv:2504.13637

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AI总结：

本文将 NLVO 扩展为考虑速度与加速度约束的 AO 和 NAO，提出多机器人共用避障算法的反应式实时导航方法，以更安全地处理复杂动态环境。

摘要：

本文介绍了一种在具有挑战性的动态环境中进行机器人导航的新方法。所提方法建立在 Velocity Obstacles（VO，速度障碍）概念之上，该概念后来被扩展为 Nonlinear Velocity Obstacles（NLVO，非线性速度障碍），以处理沿非线性轨迹运动的障碍物。本文将 NLVO 扩展为 Acceleration Obstacles（AO，加速度障碍）和 Nonlinear Acceleration Obstacles（NAO，非线性加速度障碍），以考虑速度和加速度约束。多机器人导航通过让所有机器人使用相同的避障算法来实现。在每个时间步，根据所有机器人的当前速度和加速度预测其轨迹，从而计算各自的 NLVO、AO 和 NAO。AO 和 NAO 的引入能够生成安全的避障机动，相比仅使用 NLVO，可更好地考虑机器人动力学约束。本文展示了 AO 和 NAO 在具有挑战性的环境中用于机器人导航。结果表明，使用 AO 和 NAO 能够在考虑机器人运动学并直接顾及动力学约束的同时，实现同步实时避障。所提方法支持反应式且高效的导航，并具有应用于在复杂动态环境中运行的自动驾驶车辆的潜力。

英文摘要：

This paper introduces a novel approach for robot navigation in challenging dynamic environments. The proposed method builds upon the concept of Velocity Obstacles (VO) that was later extended to Nonlinear Velocity Obstacles (NLVO) to account for obstacles moving along nonlinear trajectories. The NLVO is extended in this paper to Acceleration Obstacles (AO) and Nonlinear Acceleration Obstacles (NAO) that account for velocity and acceleration constraints. Multi-robot navigation is achieved by using the same avoidance algorithm by all robots. At each time step, the trajectories of all robots are predicted based on their current velocity and acceleration to allow the computation of their respective NLVO, AO and NAO. The introduction of AO and NAO allows the generation of safe avoidance maneuvers that account for the robot dynamic constraints better than could be done with the NLVO alone. This paper demonstrates the use of AO and NAO for robot navigation in challenging environments. It is shown that using AO and NAO enables simultaneous real-time collision avoidance while accounting for robot kinematics and a direct consideration of its dynamic constraints. The presented approach enables reactive and efficient navigation, with potential application for autonomous vehicles operating in complex dynamic environments.

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