The CACOC (Chaotic Ant Colony Optimisation for Coverage) algorithm has been developed to manage the mobility of a swarm of Unmanned Aerial Vehicles (UAVs). Using a specific chaotic dynamic obtained from the Rössler system, CACOC provides waypoints for UAVs that aim to optimise the coverage of an unknown area while having unpredictable trajectories. Since the chaotic dynamics are obtained from a three differential equations system with parameters, it is possible to tune one parameter to obtain another chaotic dynamic, which will result in different UAV mobility behaviours. This work aims at optimising this parameter of the Rössler chaotic system to improve the coverage performance of CACOC. Since each evaluation of a solution requires a full simulation, global optimisation techniques (e.g., population-based heuristics) would be very time-consuming. We therefore considered a surrogate-based method to efficiently explore the parameter space of the Rössler system for CACOC, i.e., Bayesian optimisation. Experimental results demonstrate that this approach permits to improve the speed of coverage of the UAV swarm. In addition an analysis of the dynamical properties of the obtained chaotic system is provided.

已经开发了CACOC（覆盖范围的混沌蚁群优化）算法来管理无人飞行器（UAV）群的移动性。CACOC使用从Rössler系统获得的特定混沌动力学，为无人机提供航路点，以优化未知区域的覆盖范围，同时具有不可预测的轨迹。由于混沌动力学是从具有参数的三阶微分方程系统获得的，因此有可能调整一个参数以获得另一种混沌动力学，这将导致不同的无人机活动性。这项工作旨在优化Rössler混沌系统的参数，以提高CACOC的覆盖性能。由于对解决方案的每次评估都需要完整的模拟，因此全局优化技术（例如，基于总体的启发式算法）将非常耗时。因此，我们考虑了一种基于代理的方法，以有效地探索CACOC的Rössler系统的参数空间，即贝叶斯优化。实验结果表明，这种方法可以提高无人机群的覆盖速度。另外，提供了对所获得的混沌系统的动力学特性的分析。