Martínez-Clark Rigoberto, Pliego-Jimenez Javier, Flores-Resendiz Juan Francisco, Avilés-Velázquez David
Faculty of Engineering, Administrative, and Social Sciences, Autonomous University of Baja California, Tecate 21460, BC, Mexico.
Electronics and Telecommunications Department, Applied Physics Division, CICESE-CONACYT, Ensenada 22860, BC, Mexico.
Entropy (Basel). 2023 May 26;25(6):853. doi: 10.3390/e25060853.
Heading synchronization is fundamental in flocking behaviors. If a swarm of unmanned aerial vehicles (UAVs) can exhibit this behavior, the group can establish a common navigation route. Inspired by flocks in nature, the k-nearest neighbors algorithm modifies the behavior of a group member based on the k closest teammates. This algorithm produces a time-evolving communication network, due to the continuous displacement of the drones. Nevertheless, this is a computationally expensive algorithm, especially for large groups. This paper contains a statistical analysis to determine an optimal neighborhood size for a swarm of up to 100 UAVs, that seeks heading synchronization using a simple P-like control algorithm, in order to reduce the calculations on every UAV, this is especially important if it is intended to be implemented in drones with limited capabilities, as in swarm robotics. Based on the literature of bird flocks, that establishes that the neighborhood of every bird is fixed around seven teammates, two approaches are treated in this work: (i) the analysis of the optimum percentage of neighbors from a 100-UAV swarm, that is necessary to achieve heading synchronization, and (ii) the analysis to determine if the problem is solved in swarms of different sizes, up to 100 UAVs, while maintaining seven nearest neighbors among the members of the group. Simulation results and a statistical analysis, support the idea that the simple control algorithm behaves like a flock of starlings.
航向同步是群体行为的基础。如果一群无人机能够展现出这种行为，那么整个群体就能建立一条共同的导航路线。受自然界鸟群的启发，k近邻算法会根据k个最接近的队友来改变群体中单个成员的行为。由于无人机的持续移动，该算法会产生一个随时间演变的通信网络。然而，这是一种计算成本高昂的算法，尤其是对于大型群体而言。本文进行了一项统计分析，以确定多达100架无人机群体的最佳邻域大小，该群体使用一种简单的类P控制算法来实现航向同步，为了减少每架无人机的计算量，这在打算在能力有限的无人机上实现时尤为重要，比如在群体机器人技术中。基于关于鸟群的文献，其中确定每只鸟的邻域固定在大约七个队友周围，本文探讨了两种方法：（i）分析来自100架无人机群体中实现航向同步所需的邻居的最佳百分比，以及（ii）分析确定在多达100架无人机的不同规模群体中，在群体成员之间保持七个最近邻的情况下问题是否得到解决。仿真结果和统计分析支持了这样一种观点，即简单控制算法的行为类似于椋鸟群。
