Chaos-enhanced mobility models for multilevel swarms of UAVs

Abstract

The number of civilian and military applications using Unmanned Aerial Vehicles (UAVs) has increased during the last years and the forecasts for upcoming years are exponential. One of the current major challenges consist in considering UAVs as autonomous swarms to address some limitations of single UAV usage such as autonomy, range of operation and resilience. In this article we propose novel mobility models for multi-level swarms of collaborating UAVs used for the coverage of a given area. These mobility models generate unpredictable trajectories using a chaotic solution of a dynamical system. We detail how the chaotic properties are used to structure the exploration of an unknown area and enhance the exploration part of an Ant Colony Optimization method. Empirical evidence of the improvement of the coverage efficiency obtained by our mobility models is provided via simulation. It clearly outperforms state-of-the-art approaches.

Bayesian optimisation to select Rössler system parameters used in Chaotic Ant Colony Optimisation for Coverage

“…1. In order to improve the performance of these approaches, our previous work [3] proposed to combine ACO with the chaotic behaviour of a dynamical system resulting in the CACOC mobility model: Chaotic Ant Colony optimisation for Coverage. More precisely, CACOC replaces the random components of the pheromone-based mobility model by a chaotic behaviour such that the exploration capabilities of the UAV swarm is improved.…”

“…Several works have recently demonstrated that replacing random processes by chaotic dynamics can enhance the performance of optimisation algorithms [1,2]. This approach was recently used in the field of Unmanned Aerial Vehicles (UAVs) with the CACOC (Chaotic Ant Colony optimisation for Coverage) [3]. This algorithm uses a specific chaotic dynamic obtained from the Rössler system [4] in order to optimise the coverage of an unknown area by a swarm of UAVs.…”

An Inverse Pheromone Approach in a Chaotic Mobile Robot’s Path Planning Based on a Modified Logistic Map

“…Furthermore, to improve the grid coverage and also to reduce the number of times that the robot revisits previous cells, the idea of an inverse pheromone method is used [24]. The use of a pheromone is inspired from ant colonies, where ants use pheromone to track their path and attract other ants to it [45][46][47]. Here, the inverse of this technique is used.…”

Survivable Networks via UAV Swarms Guided by Decentralized Real-Time Evolutionary Computation

“…Early studies on mobility discussed models as simple as random mobility [11], which later generated the more complex concept of chaos enhanced mobility [12], [13], [14]. Other studies proposed analytic and/or parametric approaches with fixed pre-tuned parameters [15], [8], [9], [16], [17], [10], [18].…”