SnT, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg.
FSTM/DCS, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg.
Sensors (Basel). 2020 Apr 30;20(9):2566. doi: 10.3390/s20092566.
In this article, we propose a new mobility model, called Attractor Based Inter-Swarm collaborationS (ABISS), for improving the surveillance of restricted areas performed by unmanned autonomous vehicles. This approach uses different types of vehicles which explore an area of interest following unpredictable trajectories based on chaotic solutions of dynamic systems. Collaborations between vehicles are meant to cover some regions of the area which are unreachable by members of one swarm, e.g., unmanned ground vehicles on water surface, by using members of another swarm, e.g., unmanned aerial vehicles. Experimental results demonstrate that collaboration is not only possible but also emerges as part of the configurations calculated by a specially designed and parameterised evolutionary algorithm. Experiments were conducted on 12 different case studies including 30 scenarios each, observing an improvement in the total covered area up to 11%, when comparing ABISS with a non-collaborative approach.
在本文中，我们提出了一种新的移动模型，称为基于吸引子的群间协作（ABISS），用于提高无人驾驶车辆对受限区域的监控能力。该方法使用不同类型的车辆，根据动态系统的混沌解，沿着不可预测的轨迹探索感兴趣的区域。车辆之间的协作旨在覆盖一个区域的一些无法被一个群体成员到达的区域，例如，水面上的无人地面车辆，使用另一个群体的成员，例如，无人机。实验结果表明，协作不仅是可能的，而且是通过专门设计和参数化的进化算法计算出的配置的一部分。在 12 个不同的案例研究中进行了实验，每个案例研究包括 30 个场景，观察到与非协作方法相比，ABISS 可将总覆盖面积提高 11%。
