Swarms of UAVs and fighter aircraft

2010

We review the state-of-the-art in the modelling of the aggregation and collective behavior of interacting agents of similar size and body type, typically called swarming. Starting with individual-based models based on "particle"-like assumptions, we connect to hydrodynamic/macroscopic descriptions of collective motion via kinetic theory. We emphasize the role of the kinetic viewpoint in the modelling, in the derivation of continuum models and in the understanding of the complex behavior of the system.

Communications in Applied and Industrial Mathematics

The honeybee swarming process is steered by few scout individuals, which are the unique informed on the location of the target destination. Theoretical and experimental results suggest that bee coordinated flight arises from visual signals. However, how the information is passed within the population is still debated. Moreover, it has been observed that honeybees are highly sensitive to conflicting directional information. In fact, swarms exposed to fast-moving bees headed in the wrong direction show clear signs of disrupted guidance. In this respect, we here present a discrete mathematical model to investigate different hypotheses on the behaviour both of informed and uninformed bees. In this perspective, numerical realizations, specifically designed to mimic selected experiments, reveal that only one combination of the considered assumptions is able to reproduce the empirical outcomes, resulting thereby the most reliable mechanism underlying the swarm dynamics according to the pro...

PLOS ONE, 2018

In this work, a swarm behaviour for multi-rotor Unmanned Aerial Vehicles (UAVs) deployment will be presented. The main contribution of this behaviour is the use of a virtual device for quantitative sematectonic stigmergy providing more adaptable behaviours in complex environments. It is a fault tolerant highly robust behaviour that does not require prior information of the area to be covered, or to assume the existence of any kind of information signals (GPS, mobile communication networks. . .), taking into account the specific features of UAVs. This behaviour will be oriented towards emergency tasks. Their main goal will be to cover an area of the environment for later creating an ad-hoc communication network, that can be used to establish communications inside this zone. Although there are several papers on robotic deployment it is more difficult to find applications with UAV systems, mainly because of the existence of various problems that must be overcome including limitations in available sensory and on-board processing capabilities and low flight endurance. In addition, those behaviours designed for UAVs often have significant limitations on their ability to be used in real tasks, because they assume specific features, not easily applicable in a general way. Firstly, in this article the characteristics of the simulation environment will be presented. Secondly, a microscopic model for deployment and creation of ad-hoc networks, that implicitly includes stigmergy features, will be shown. Then, the overall swarm behaviour will be modeled, providing a macroscopic model of this behaviour. This model can accurately predict the number of agents needed to cover an area as well as the time required for the deployment process. An experimental analysis through simulation will be carried out in order to verify our models. In this analysis the influence of both the complexity of the environment and the stigmergy system will be discussed, given the data obtained in the simulation. In addition, the macroscopic and microscopic models will be compared verifying the number of predicted individuals for each state regarding the simulation.

Frontiers in Robotics and AI

This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group. At the lowest level, each MAV should operate safely. Robustness is often hailed as a pillar of swarm robotics, and a minimum level of local reliability is needed for it to propagate to the global level. An MAV must be capable of autonomous navigation within an environment with sufficient trustworthiness before the system can be scaled up. Once the operations of the single MAV are sufficiently secured for a task, the subsequent challenge is to allow the MAVs to sense one another within a neighborhood of interest. Relative localization of neighbors is a fundamental part of self-organizing robotic systems, enabling behaviors ranging from basic relative collision avoidance to higher level coordination. This ability, at times taken for granted, also must be sufficiently reliable. Moreover, herein lies a constraint: the design choice of the relative localization sensor has a direct link to the behaviors that the swarm can (and should) perform. Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight, and require constant relative excitation. At the swarm level, the final outcome is thus intrinsically influenced by the on-board abilities and sensors of the individual. The real-world behavior and operations of an MAV swarm intrinsically follow in a bottom-up fashion as a result of the local level limitations in cognition, relative knowledge, communication, power, and safety. Taking these local limitations into account when designing a global swarm behavior is key in order to take full advantage of the system, enabling local limitations to become true strengths of the swarm.

2006

Since we are simulating the I-SWARM scenario completely nothing is realnot the robots themselves, not the accelerations, and not the communication. However, we want to distinguish between the real world, like the robots that we are simulating, and totally virtual concepts that will exist later in the 4 CHAPTER 1

2011

A computational method that automatically builds dynamical models of swarming systems from positional data is introduced. As an initial test for the approach, the classical Vicsek model is used to make samples for the computer algorithm and retrieve a model. Time dependent separation measures are introduced in order to characterize the dynamics of a system and then compare the behaviors of the source and retrieved model. Cases of low and high density interactions are considered to verify the generality of the models. The results show the retrieved models are capable of emulating the collective behavior well, especially when the interaction structure resembles the one of the source model.

2004

This technical memorandum provides an overview of the state of the art of control system design for swarming UAVs. An overview of trends and future needs for military applications of UAVs is presented first. Linear controller design for aircrafts is then reviewed in the context of UAV systems. Comparative analysis of flight, collision avoidance and mission control approaches for swarming UAVs is provided. Then, advanced nonlinear UAV control designs including several feedback linearization techniques, Neural Network implementation, Fuzzy Logic application incorporated with Linear and Nonlinear Model Predictive Control for swarming UAVs are analysed. Finally, the importance of Hardware in the Loop Simulation is discussed. Simulation and experimental validation results will be presented in subsequent reports. Résumé Ce document technique donne une vue d'ensemble de l'état actuel des connaissances en matière de conception des systèmes de commande d'engins télépilotés volant en groupe. Une vue d'ensemble des tendances et des besoins futurs pour les applications militaires des engins télépilotés est d'abord présentée. Une analyse comparative des vols, des évitements d'abordage et des approches relatives au contrôle des missions des engins télépilotés volant en groupe est fournie. Puis, des modèles de contrôle non linéaire évolués pour les engins télépilotés, y compris plusieurs techniques de linéarisation de la rétroaction, la mise en oeuvre de réseaux neuronaux et un application de la logique floue intégrée au contrôle prédictif des modèles linéaires et non linéaires des engins télépilotés volant en groupe sont analysés. Enfin, l'importance du matériel dans la simulation en boucle est abordée. Les résultats des simulations et de la validation des expériences seront présentés dans des rapports subséquents.

Winter Simulation Conference, 2005

Unmanned Aerial Vehicle (UAV) research is an increasingly important pillar of national security and military interest. A high fidelity discrete event simulation is prerequisite to any systems implementation. The Synchronous Parallel Environment for Emulation and Discrete Event Simulation (SPEEDES) is a versatile and powerful tool that can be used for realization of this objective. A suite of five experiments measures the efficiency a parallel UAV swarming SPEEDES application. Results indicate that the conservative time management produces more than twice the speedup as optimistic time management.

Self published on Linkedin, 2024

The concept of autonomous drone swarms is gaining significant attention. This project aims to develop an experimental platform for testing drone swarms in a simulated environment. The drones' flight patterns are modeled using the boids algorithm and implemented via the Repast Simphony agent-based modeling toolkit. Equipped with cameras, the drones can identify and engage targets within their designated areas. To enhance the realism of the simulated environment, Three.js is utilized for displaying 3D animated objects. The project's codebase is available on GitHub at drone_swarm. The article was originally published on Linkedin.

2006

The purpose of this paper and the experiments condu cte herein is to assess the impact of variation in particle inertia (w), particle increment ( c1) and global increment ( c2), on the fitness of particles and there impact upo n particle swarm optimization in a dynamic environmen t. I this paper a 3-dimensional parabolic function is utilized to illustrate an example of a dynamic environment. Results show that the impact of PSO in this environm e t will not be significantly enhanced by the alteration of w, c1 or c2.