Indoor Multi-Vehicle Flight Testbed for Fault Detection, Isolation, and Recovery

Abstract

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.

Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 kHz

“…Valetti, Bethke et al [1] describe a platform based on the RcToys Draganflyer used for experiments at Aerospace Controls Laboratory, MIT. This platform is controlled autonomously using a motion capture system.…”

Vision-based guidance and control of a hovering vehicle in unknown, GPS-denied environments

“…The MIT Real-time indoor Autonomous Vehicle test ENvironment (RAVEN) [12], [13] provides the hardware foundation for the developments presented. RAVEN combines the tracking capabilities of a Vicon-MX camera system [14] with R/C hardware and an array of networked servers, comprising an ideal rapid-development environment.…”

Embedding Health Management into Mission Tasking for UAV Teams

“…A number of multi-vehicle tests have been flown using the RAVEN at MIT (as described in [10]) to demonstrate the mission, task assignment and control level health management algorithms. In this test suite, three UAVs equipped with cameras and 2000 mAh batteries were used to search for ground vehicles in the test area.…”

“…First, note that most planning techniques are decomposed into a multi-tiered architecture where missions, tasks and vehiclespecific activities are managed by different components to meet real-time computational deadlines. Similar architectures have been used in other multi-vehicle systems and testbeds (see references in [10], [11]); however, most systems lack health monitoring components to evaluate the subsystem performance. As a result, adding system health monitors to a simple hierarchical design (as shown in Figure 2) can improve a system's run-time capabilities by ensuring that it maintains a basic, functional capability during a mission in accordance with system goals.…”

“…In addition, the average flight time of electric-powered helicopters and quadrotors (such as the Draganflyer V Ti Pro [14]) are limited by the motors used and desired payload capacity. As part of our testing, the relationships between power usage, battery charge and other parameters where examined for a set of off-the-shelf electric-powered quadrotor vehicles in the MIT RAVEN [10]. This testing showed that there is a strong correlation between the battery voltage and the average collective stick position value.…”