Self-organized UAV traffic in realistic environments
Self-organized UAV traffic in realistic environments
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现实环境中的自组织无人机交通
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
G. Vásárhelyi
中科院分区：
文献类型：
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作者：
Csaba Virágh;Máté Nagy;C. Gershenson;G. Vásárhelyi
在真实环境中，考虑传感器噪声、通信延迟、有限的通信距离、有限的传感器更新速率和有限的惯性，研究了开放的2D和3D空间中不同的密集多旋翼无人机交通仿真场景。我们实现了两个基本的自组织算法：一个具有恒定的方向，一个具有恒定的速度偏好，以达到所需的目标。我们在五个基本的交通场景中对两种算法进行了进化优化，并在不同的车辆密度下对优化后的算法进行了测试。给出了最优算法和参数选择准则，并比较了每种方案和情况下的最大流量和碰撞风险。我们发现，i）不同的场景和密度需要不同的算法方法，即，无人机必须在稀疏和密集的环境中表现不同，或者当它们有共同或不同的目标时; ii）在我们的模型中隐含着一种更慢更快的效果，即，在平均速度远离最大值的密度下实现最大通量; iii）通信延迟是最严重的不稳定环境条件，其对性能具有根本影响，并且在设计用于真实的生活中的算法时需要考虑。
We investigated different dense multirotor UAV traffic simulation scenarios in open 2D and 3D space, under realistic environments with the presence of sensor noise, communication delay, limited communication range, limited sensor update rate and finite inertia. We implemented two fundamental self-organized algorithms: one with constant direction and one with constant velocity preference to reach a desired target. We performed evolutionary optimization on both algorithms in five basic traffic scenarios and tested the optimized algorithms under different vehicle densities. We provide optimal algorithm and parameter selection criteria and compare the maximal flux and collision risk of each solution and situation. We found that i) different scenarios and densities require different algorithmic approaches, i.e., UAVs have to behave differently in sparse and dense environments or when they have common or different targets; ii) a slower-is-faster effect is implicitly present in our models, i.e., the maximal flux is achieved at densities where the average speed is far from maximal; iii) communication delay is the most severe destabilizing environmental condition that has a fundamental effect on performance and needs to be taken into account when designing algorithms to be used in real life.