用加速度障碍物实现机器人动态避障，更安全高效。
From NLVO to NAO: Reactive Robot Navigation using Velocity and Acceleration Obstacles
- 引入加速度障碍物（AO）和非线性加速度障碍物（NAO），考虑机器人动力学约束。
- 多机器人共用同一算法，实时预测轨迹并生成安全避让动作。
- 适合自动驾驶、复杂动态环境中的实时反应式导航应用。
本文提出一种新型机器人导航方法，用于复杂动态环境。该方法在速度障碍物（VO）基础上发展为非线性速度障碍物（NLVO），以处理沿非线性轨迹运动的障碍物。本文进一步扩展至加速度障碍物（AO）和非线性加速度障碍物（NAO），以纳入速度与加速度约束。所有机器人使用相同避障算法，在每个时间步基于当前速度与加速度预测轨迹，计算各自的NLVO、AO与NAO。引入AO与NAO可生成更符合机器人动力学特性的安全避让动作，优于仅使用NLVO的情况。实验表明，该方法能同时实现实时碰撞规避，充分考虑机器人运动学及动力学约束，具备反应迅速、效率高的特点，适用于复杂动态环境中自主车辆的导航。
原文摘要 · Abstract (English)
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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