This paper presents flight tests of a unique indoor, multi-vehicle testbed that was developed to study long duration UAV missions in a controlled environment. This testbed uses real hardware to examine research questions related to single and multi-vehicle health management, such as vehicle failures, refueling, and maintenance. The primary goal of the project is to embed health management into the full UAV planning system, thereby leading to improved overall mission performance, even when using simple aircraft that are prone to failures. The testbed has both aerial and ground vehicles that operate autonomously in a large test region and can be used to execute many different mission scenarios. The success of this testbed is largely related to our choice of vehicles, sensors, and the system's command and control architecture, which has resulted in a testbed that is very simple to operate. This paper discusses this testbed infrastructure and presents flight test results from some of our most recent single-and multi-vehicle experiments.

This paper presents vehicle models and test flight results for an autonomous fixed-wing airplane that is designed to take-off, hover, transition to and from level-flight modes, and perch on a vertical landing platform in a highly space constrained environment. By enabling a fixed-wing UAV to achieve these feats, the speed and range of a fixed-wing aircraft in level flight are complimented by hover capabilities that were typically limited to rotorcraft. Flight and perch landing results are presented. This capability significantly eases support and maintenance of the vehicle. All of the flights presented in this paper are performed using the MIT Real-time Autonomous Vehicle indoor test ENvironment (RAVEN).2

We present the vision-based estimation and control of a quadrotor vehicle using a single camera relative to a novel target that incorporates the use of moiré patterns. The objective is to acquire the six degree of freedom estimation that is essential for the operation of vehicles in close proximity to other craft and landing platforms. A target contains markers to determine its relative orientation and locate two sets of orthogonal moiré patterns at two different frequencies. A camera is mounted on the vehicle with the target in the field of view. An algorithm processes the images, extracting the attitude and position information of the camera relative to the target utilizing geometry and four single-point discrete Fourier transforms on the moiré patterns. The position and yaw estimations with accompanying control techniques have been implemented on a remote-controlled quadrotor. The flight tests conducted prove the system's feasibility as an option for precise relative navigation for indoor and outdoor operations.

Abstract-Coordinated multi-vehicle autonomous systems can provide incredible functionality, but off-nominal conditions and degraded system components can render this capability ineffective. This paper presents techniques to improve missionlevel functional reliability through better system self-awareness and adaptive mission planning. In particular, we extend the traditional definition of health management, which has historically referred to the process of actively monitoring and managing vehicle sub-systems (e.g., avionics) in the event of component failures, to the context of multiple vehicle operations and autonomous multi-agent teams. In this case, health management information about each mission system component is used to improve the mission system's self-awareness and adapt vehicle, guidance, task and mission plans. This paper presents the theoretical foundations of our approach and recent experimental results on a new UAV testbed.

This paper presents the development and implementation of techniques used to manage autonomous unmanned aerial vehicles (UAVs) performing 24/7 persistent surveillance operations. Using an indoor flight testbed, flight test results are provided to demonstrate the complex issues encountered by operators and mission managers when executing an extended persistent surveillance operation in realtime. This paper presents mission health monitors aimed at identifying and improving mission system performance to avoid down time, increase mission system efficiency and reduce operator loading. This paper discusses the infrastructure needed to execute an autonomous persistent surveillance operation and presents flight test results from one of our recent automated UAV recharging experiments. Using the RAVEN at MIT, we present flight test results from a 24 hr, fully-autonomous air vehicle flight-recharge test and an autonomous, multi-vehicle extended mission test using small, electric-powered air vehicles.