The objective of this paper is to explore the feasibility of using multiple low-altitude, short endurance (LASE) unmanned air vehicles (UAVs) to cooperatively monitor and track the propagation of large forest fires. A real-time algorithm for tracking the perimeter of a fire with an on-board infrared sensor is developed. Using this algorithm, we develop a decentralized multiple-UAV approach to monitoring the perimeter of the fire. The UAVs are assumed to have limited communication and sensing range. The effectiveness of the approach is demonstrated in simulation using a 6 DOF dynamic model for the UAV and numerical propagation model for the forest fire. Salient features of the approach include the ability to monitor a changing fire perimeter, the ability to systematically add and remove UAVs from the team, and the ability to supply time-critical information to forest fire fighters.
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.
The objective of this paper is to describe the design and implementation of a small semiautonomous fixed-wing unmanned air vehicle. In particular we describe the hardware and software architectures used in the design. We also describe a low weight, low cost autopilot developed at Brigham Young University and the algorithms associated with the autopilot. Novel PDA and voice interfaces to the UAV are described. In addition, we overview our approach to real-time path planning, trajectory generation, and trajectory tracking. The paper is augmented with movie files that demonstrate the functionality of the UAV and its control software.
Small unmanned air vehicles (UAVs) and micro air vehicles (MAVs) have payload and power constraints that prohibit heavy sensors and powerful processors. This paper presents real-time attitude and position estimation solutions that use small, inexpensive sensors and low-power microprocessors. In connection with an Extended Kalman Filter attitude estimation scheme, a novel method for dealing with latency in real-time is presented using a distributed-in-time architecture. Essential to small UAV or MAV missions is the ability to navigate precisely. To reduce computational overhead and to simplify design, a cascaded filter approach to position estimation is used. The design is insensitive to noise and to loss of GPS lock. Simulation and hardware tests show that the algorithms operate in real-time and are suitable for control, stabilization, and navigation.
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