One of the most important design problems for multi-UAV (Unmanned Air Vehicle) systems is the communication which is crucial for cooperation and collaboration between the UAVs. If all UAVs are directly connected to an infrastructure, such as a ground base or a satellite, the communication between UAVs can be realized through the in-frastructure. However, this infrastructure based communication architecture restricts the capabilities of the multi-UAV systems. Ad-hoc networking between UAVs can solve the problems arising from a fully infrastructure based UAV networks. In this paper, Flying Ad-Hoc Networks (FANETs) are surveyed which is an ad hoc network connecting the UAVs. The differences between FANETs, MANETs (Mobile Ad-hoc Networks) and VANETs (Vehicle Ad-Hoc Networks) are clarified first, and then the main FANET design challenges are introduced. Along with the existing FANET protocols, open research issues are also discussed.

Flying Ad-Hoc Networks (FANETs)

The days where swarms of unmanned aerial vehicles (UAVs) will occupy our skies are fast approaching due to the introduction of cost-efficient and reliable small aerial vehicles and the increasing demand for use of such vehicles in a plethora of civil applications. Governments and industry alike have been heavily investing in the development of UAVs. As such it is important to understand the characteristics of networks with UAVs to enable the incorporation of multiple, coordinated aerial vehicles into the air traffic in a reliable and safe manner. To this end, this survey reports the characteristics and requirements of UAV networks for envisioned civil applications over the period 2000–2015 from a communications and networking viewpoint. We survey and quantify quality-of-service requirements, network-relevant mission parameters, data requirements, and the minimum data to be transmitted over the network. Furthermore, we elaborate on general networking related requirements such as connectivity, adaptability, safety, privacy, security, and scalability. We also report experimental results from many projects and investigate the suitability of existing communication technologies for supporting reliable aerial networking.

Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint

Unmanned aerial vehicles (UAVs) have attracted great interest in rapid deployment for both civil and military applications. UAV communication has its own distinctive channel characteristics compared to the widely used cellular or satellite systems. Accurate channel characterization is crucial for the performance optimization and design of efficient UAV communication. However, several challenges exist in UAV channel modeling. For example, the propagation characteristics of UAV channels are under explored for spatial and temporal variations in non–stationary channels. Additionally, airframe shadowing has not yet been investigated for small size rotary UAVs. This paper provides an extensive survey of the measurement methods proposed for UAV channel modeling that use low altitude platforms and discusses various channel characterization efforts. We also review from a contemporary perspective of UAV channel modeling approaches, and outline future research challenges in this domain.

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A Survey of Channel Modeling for UAV Communications

We describe our experiences deploying BikeNet, an extensible mobile sensing system for cyclist experience mapping leveraging opportunistic sensor networking principles and techniques. BikeNet represents a multifaceted sensing system and explores personal, bicycle, and environmental sensing using dynamically role-assigned bike area networking based on customized Moteiv Tmote Invent motes and sensor-enabled Nokia N80 mobile phones. We investigate real-time and delay-tolerant uploading of data via a number of sensor access points (SAPs) to a networked repository. Among bicycles that rendezvous en route we explore inter-bicycle networking via data muling. The repository provides a cyclist with data archival, retrieval, and visualization services. BikeNet promotes the social networking of the cycling community through the provision of a web portal that facilitates back end sharing of real-time and archived cycling-related data from the repository. We present: a description and prototype implementation of the system architecture, an evaluation of sensing and inference that quantifies cyclist performance and the cyclist environment; a report on networking performance in an environment characterized by bicycle mobility and human unpredictability; and a description of BikeNet system user interfaces. Visit [4] to see how the BikeNet system visualizes a user's rides.

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The BikeNet mobile sensing system for cyclist experience mapping

Unmanned aerial vehicles (UAVs) are mainly used by military and government organizations, but with low-cost sensors, electronics, and airframes there is significant interest in using low-cost UAVs among aircraft hobbyists, academic researchers, and industries. Applications such as mapping, search and rescue, patrol, and surveillance require the UAV to autonomously follow a predefined path at a prescribed height. The most commonly used paths are straight lines and circular orbits. Path-following algorithms ensure that the UAV will follow a predefined path in three or two dimensions at constant height. A basic requirement for these path-following algorithms is that they must be accurate and robust to wind disturbances.

Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algorithms for Fixed-Wing Unmanned Aerial Vehicless

General techniques for constructing vector fields for unmanned aircraft guidance are provided that incorporate Lyapunov stability properties to produce simple, globally stable vector fields in three dimensions. Use of these fields to produce circular loiter pattern attractors is illustrated, along with a simple switching algorithm to enable following of arbitrary way point sequences. Alternatively, attractor shape variations are developed by warping the circular loiter, preserving global stability guarantees, and accurate path tracking. An example of this technique is provided that produces a racetrack loiter pattern, and three different variations in the warping technique are compared. Finally, tracking of the vector field is considered, using Lyapunov techniques to show global stability of heading and path position for several types of tracking control laws that are compatible with low cost unmanned aircraft avionics.

Lyapunov Vector Fields for Autonomous Unmanned Aircraft Flight Control

An airborne wireless sensor network (WSN) composed of bird-sized micro aerial vehicles (MAVs) enables low cost high granularity atmospheric sensing of toxic plume behavior and storm dynamics, and provides a unique three-dimensional vantage for monitoring wildlife and ecological systems. This paper describes a complete implementation of our SensorFlock airborne WSN, spanning the development of our MAV airplane, its avionics, semi-autonomous flight control software, launch system, flock control algorithm, and wireless communication networking between MAVs. We present experimental results from flight tests of flocks of MAVs, and a characterization of wireless RF behavior in air-to-air communication as well as air-to-ground communication.

SensorFlock: an airborne wireless sensor network of micro-air vehicles

This paper presents results implementing Lyapunov vector fields for the guidance of unmanned aircraft. The vector fields yield globally stable tracking of circular loiter patterns. These loiter patterns are used in several unmanned aircraft applications including hierarchical micro air vehicle control for cooperative plume tracking, extremum seeking for electronic chaining, and cooperative tracking of moving targets. Extensions of the basic LGVF approach are made for each application including: warping the circular pattern to form other closed orbit patterns; driving the center of the LGVF orbit using virtual dynamics; and spacing multiple unmanned aircraft around a circular orbit. Hardware-in-the- loop simulation results and flight data are given to validate performance.

Lyapunov Guidance Vector Fields for Unmanned Aircraft Applications

General techniques for construct ing vector fields for UAV guidance are provided that incorporate Lyapunov stability properties to produce simple, globally stable vector fields in 3D. Use of these fields in circular loiter patterns is illustrated, along with simple switching algorithms fo r circular loiter vector fields to enable following of arbitrary way point paths or loops. Another variation is also developed, where a simple circular loiter is warped into other shapes, preserving global stability guarantees and accurate path tracking. An example of this technique is provided that produces a “racetrack” loiter pattern, and three different variations in the warping technique are compared. Finally, tracking of the vector field is considered, using Lyapunov techniques to show global stabili ty of heading and path position for several types of tracking control laws that are compatible with low cost UAV avionics.

Lyapunov Vector Fields for Autonomous UAV Flight Control 1

An autopilot system for a small unmanned vehicle is described which consists only of a single axis rate gyro, an absolute pressure sensor, and a GPS receiver. The structure of a control system is described which is able to estimate all necessary information to provide stabilization and guidance for a small flying wing UAV. A vector field guidance system is discussed that is able to navigate the aircraft from any initial location to a stable loiter around a point given only the coordinates of the desired point and the desired radius. Warping of the simple circular loiter into a racetrack shape is discussed. Modifications to the autopilot to achieve autonomous takeoff and landing are discussed. Experimental results of the successful implementation of all aspects of this autopilot system are shown, as well as an example flight where the only human interaction is to send a ‘takeoff’ and ‘land’ command.

William J. Pisano

Papers

Lyapunov Vector Fields for Autonomous Unmanned Aircraft Flight Control

SensorFlock: an airborne wireless sensor network of micro-air vehicles

Lyapunov Guidance Vector Fields for Unmanned Aircraft Applications

Lyapunov Vector Fields for Autonomous UAV Flight Control 1

Autonomous UAV Control Using a 3-Sensor Autopilot