Abstract
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.
Highlights
Unmanned Aerial Vehicles (UAV), known as “drones”, have become commonplace devices in the 21st century
We conducted a sanity check comparing our optimisation results against random search and, after that, an analysis of the results achieved were carried out taking into account other mobility models
We present the Random Search (RS) algorithm to be compared with our Evolutionary Algorithm (EA) as the chosen optimisation algorithm in terms of precision and efficiency
Summary
Unmanned Aerial Vehicles (UAV), known as “drones”, have become commonplace devices in the 21st century. One well-known way of collaboration is found in evolutionary game theory (EGT) where individuals compete and sometimes collaborate with each other following evolutionary strategies [12] This behaviour, consisting in achieving a common good while seeking a personal reward, has been applied to solve several problems such as predicting infectious diseases with vaccination strategies [13], making military decisions [14], optimising packet relaying in wireless networks based on reciprocity [15] and, improving surveillance missions sharing pheromone trails. By using ABISS, collaborations between swarms emerge after applying evolutionary bio-inspired techniques, with the common objective of improving area coverage while performing surveillance This is a distributed intelligence system, in which, if some members fail, the rest of the swarm still carries on their surveillance task, increasing the robustness of the proposal.
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