We consider the positioning problem of aerial drone systems for efficient three-dimensional (3-D) coverage. Our solution draws from molecular geometry, where forces among electron pairs surrounding a central atom arrange their positions. In this paper, we propose a 3-D clustering algorithm for autonomous positioning (VBCA) of aerial drone networks based on virtual forces. These virtual forces induce interactions among drones and structure the system topology. The advantages of our approach are that (1) virtual forces enable drones to self-organize the positioning process and (2) VBCA can be implemented entirely localized. Extensive simulations show that our virtual forces clustering approach produces scalable 3-D topologies exhibiting near-optimal volume coverage. VBCA triggers efficient topology rearrangement for an altering number of nodes, while providing network connectivity to the central drone. We also draw a comparison of volume coverage achieved by VBCA against existing approaches and find VBCA up to 40 more efficient.
There are various approaches for the 3-D coordination and positioning of aerial networks. The aerial network presented by Elston and Frew @cite_20 contains a central ship with multiple drones, which use field tracking for the hierarchy. Dumiak @cite_24 proposes a coordination mechanism for aerial networks to complete the tasks by multiple drones. UAVNet is an autonomous deployment framework for FANETs by @cite_0 . UAVNet aims to provide an efficient way to construct a communication network, which can be controlled by a single remote user. The mobility prediction clustering algorithm @cite_19 uses the dictionary tree structure prediction algorithm with link expiration time mobility model to overcome the challenge of frequent cluster updates by predicting the network topology updates. @cite_2 propose a positioning and collision avoidance strategy for UAVs in search scenarios, which uses received signal strength (RSS) from the onboard communication module. De @cite_4 propose a prognostics and health monitoring based multi-UAV task assignment approach to include system probability of failure into task assignment in a drone system. This method assigns tasks based on the drone health condition using the Receding Horizon Task Assignment (RHTA) algorithm.
@cite_4 @cite_24 @cite_19 @cite_0 @cite_2 @cite_20
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This paper is relating to the application of Integrated Vehicle Health Management (IVHM) concepts based on Prognostics and Health Monitoring (PHM) techniques to Multi-UAV systems. Considering UAV as a mission critical system, it is expected and required to accomplish its operational objectives with minimal unscheduled interruptions. So that, it does make sense for UAV to take advantage of those techniques as enablers for the readiness of multi-UAV. The main goal of this paper is to apply information from a PHM system to support decision making through an IVHM framework. PHM system information, in this case, comprises UAV remaining useful life (RUL) estimations. UAV RUL is computed by means of a fault tree analysis that it is fed by a distribution function from a probability density function relating time and failure probability for each UAV critical components. The IVHM framework, in this case, it is the task assignment based on UAV health condition (RUL information) using the Receding Horizon Task Assignment (RHTA) algorithm. The study case was developed considering a team of electrical small UAVs and pitch control system was chosen as the critical system. In recent years, with the increasingly widespread application of unmanned aerial vehicle (UAV), the network technology of UAV has also caused for concern. In this paper, according to the background of related technologies of UAV, a mobility prediction clustering algorithm (MPCA) relying on the attributes of UAV is proposed. The dictionary Trie structure prediction algorithm and link expiration time mobility model are applied in this clustering algorithm to solve the difficulty of high mobility of UAV. The simulation shows that the reasonable clusterhead electing algorithm and on-demand cluster maintenance mechanism guarantee the stability of the cluster structure and the performance of the network. We developed UAVNet, a framework for the autonomous deployment of a flying Wireless Mesh Network using small quadrocopter-based Unmanned Aerial Vehicles (UAVs). The flying wireless mesh nodes are automatically interconnected to each other and building an IEEE 802.11s wireless mesh network. The implemented UAVNet prototype is able to autonomously interconnect two end systems by setting up an airborne relay, consisting of one or several flying wireless mesh nodes. The developed software includes basic functionality to control the UAVs and to setup, deploy, manage, and monitor a wireless mesh network. Our evaluations have shown that UAVNet can significantly improve network performance. Unmanned aerial vehicles (UAVs) play an invaluable role in information collection and data fusion. Because of their mobility and the complexity of deployed environments, constant position awareness and collision avoidance are essential. UAVs may encounter and or cause danger if their Global Positioning System (GPS) signal is weak or unavailable. This paper tackles the problem of constant positioning and collision avoidance on UAVs in outdoor (wildness) search scenarios by using received signal strength (RSS) from the onboard communication module. Colored noise is found in the RSS, which invalidates the unbiased assumptions in least squares (LS) algorithms that are widely used in RSS-based position estimation. A colored noise model is thus proposed and applied in the extended Kalman filter (EKF) for distance estimation. Furthermore, the constantly changing path-loss factor during UAV flight can also affect the accuracy of estimation. To overcome this challenge, we present an adaptive algorithm to estimate the path-loss factor. Given the position and velocity information, if a collision is detected, we further employ an orthogonal rule to adapt the UAV predefined trajectory. Theoretical results prove that such an algorithm can provide effective modification to satisfy the required performance. Experiments have confirmed the advantages of the proposed algorithms. This paper presents a hierarchical control architecture that enables cooperative surveillance by a heterogeneous aerial robot network comprised of mothership unmanned aircraft and daughtership micro air vehicles. Combining the endurance, range, and processing capabilities of the motherships with the stealth, flexibility, and maneuverability of swarms of daughterships enables robust control of aerial robot networks conducting collaborative operations. The hierarchical control structure decomposes the system into components that take advantage of the abilities of the different types of vehicles. The motherships act as distributed databases, fusion centers, negotiation agents, and task supervisors while daughtership control is achieved using cooperative vector field tracking. This paper describes the overall architecture and then focuses on the assignment and tracking algorithms used once sub- teams of daughtership vehicles have been deployed. A summary of the communication, command, and control structure of a heterogeneous unmanned aircraft system is also given in this paper along with hardware-in-the-loop and software simulation results verifying several components of the distributed control architecture.