Decentralized Cooperative Aerial Surveillance Using Fixed-Wing Miniature UAVs

Abstract

Numerous applications require aerial surveillance. Civilian applications include monitoring forest fires, oil fields and pipelines, and tracking wildlife. Applications to homeland security include border patrol and monitoring the perimeter of nuclear power plants. Military applications are numerous. The current approach to these applications is to use a single manned vehicle for surveillance. However, manned vehicles are typically large and expensive. In addition, hazardous environments and operator fatigue can potentially threaten the life of the pilot. Therefore, there is a critical need for automating aerial surveillance using unmanned air vehicles (UAVs). This paper gives an overview of a cooperative control strategy for aerial surveillance that has been successfully flight tested on small (48 inch wingspan) UAVs. Our approach to cooperative control problems can be summarized in four steps: (1) the definition of a cooperation constraint and cooperation objective; (2) the definition of a coordination variable as the minimal amount of information needed to effect cooperation; (3) the design of a centralized cooperation strategy; and (4) the use of consensus schemes to transform the centralized strategy into a decentralized algorithm. The effectiveness of the solution will be shown using both high fidelity simulation and actual flight tests.

Real-Time Optimized Rendezvous on Nonholonomic Resource-Constrained Robots

“…Also, R 1 and R 2 have to avoid each other. Formally, we have O j = 1 and o (1) k =x 12 (k∆t) for R 1 and o (1) k =x 21 (k∆t) for R 2 . This scenario not only tests collision avoidance, but also how each robot is able to rendezvous with a fixed goal position.…”

“…In a network of robots, solving the consensus problem on the position of each agents refers to the task of controlling them as to reach a common rendezvous point. The ability to meet or to rendezvous has indeed many practical applications such as formation control [11], flocking [7], attitude alignment [25] or cooperative aerial surveillance [1]. Additionally, although this paper specifically addresses the rendezvous of differentialwheeled robots, its general concept may be applied the wider range of consensus problems.…”

UAVs Dynamic Mission Management in Adversarial Environments

“…The complexities of managing teamed UAVs in stochastic adversarial terrain require new UAV designs, new techniques for navigation and control, and new collaborative methodologies, as well as interfaces with the human operator. Collaboration of multiple UAVs in complicated environments is also an area of wide scope and great interest, including planning, scheduling, and resource allocation for multiUAV, multitask missions [1][2][3][4], coordinated area surveillance [5,6], collision avoidance [7], etc. New concepts of operation are emerging for UAVs in SEAD mission, as described in [8][9][10][11], and facilitated by the Interactive Warfare Simulation (IWARS) [8], Boeing C4ISim Open Experimentation platform (Boeing OEP) [9], and Flexible Analysis Modeling and Exercise System (FLAMES) software [11].…”

On-Board Relative Bearing Estimation for Teams of Drones Using Sound

“…T EAMS of drones have many potential applications, such as, a rapidly deployable communication network for disaster areas [1], sensing and mapping of chemical clouds [2], searching for forest fires [3], aerial surveillance [4], aerial transportation [5], building constructions [6] and detecting targets of interest [7]. Rapid progress in the design and control of multi-drone systems has been observed over the past years [6], [8].…”