Selective Velocity Obstacle Method for Cooperative Autonomous Collision Avoidance System for Unmanned Aerial Vehicles
Selective Velocity Obstacle Method for Cooperative Autonomous Collision Avoidance System for Unmanned Aerial Vehicles
复制标题
无人机协同自主避碰系统的选择性速度障碍法
DOI：
10.2514/6.2013-4627
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Q. Chu
中科院分区：
文献类型：
--
作者：
Y. I. Jenie;E. Kampen;C. C. Visser;Q. Chu
无人机的自主防撞系统（ACAS）被设定为一种工具，以证明它们可以达到将无人机飞行集成到国家空域系统（NAS）中所需的同等安全水平。本文重点研究了无人机之间的协同避让问题，旨在定义一种能够在一般情况下实现无人机之间协同避让的算法，同时又不受一些共同规则的限制。该算法被命名为选择性速度障碍（SVO）方法，这是速度障碍方法的扩展。该算法为无人机在三种基本模式之间进行选择提供了指导，即，避免、维护或恢复。这三种模式的变化为无人机选择如何躲避提供了灵活性。通过将该算法建模为一个混合系统，对不同的无人机遭遇场景进行了仿真，取得了满意的结果。蒙特卡洛模拟，然后进行总结的性能甚至更多。随机初始参数，包括速度，姿态，位置和回避起点，超过10个遭遇场景进行了测试，涉及多达5架无人机。然后导出一个称为违规概率的参数，显示整个遭遇样本中的零违规。
Autonomous collision avoidance system (ACAS) for Unmanned Aerial Vehicles (UAVs) is set as a tool to prove that they can achieve the equivalent level of safety, required in context of integrating UAVs flight into the National Airspace System (NAS). This paper focus on the cooperative avoidance part, aiming to define an algorithm that can provide avoidance between cooperative UAVs in general, while still be restricted by some common rules. The algorithm is named the Selective Velocity Obstacle (SVO) method, which is an extension of the Velocity Obstacle method. The algorithm gives guidelines for UAVs to select between three basic modes for avoidance, i.e., to Avoid, Maintain, or Restore. The variation of those three modes gives flexibility for UAVs to choose how will they avoid. By modeling the algorithm as a hybrid system, simulations on various UAVs encounters scenario were conducted and shows satisfying result. Monte Carlo simulations are then conducted to conclude the performance even more. Randomizing the initial parameters, including speed, attitude, positions and avoidance starting point, more than 10 encounter scenario were tested, involving up until five UAVs. A parameter called the Violation Probability is then derived, showing zero violations in the entire encounter samples.