Military teams patrolling dangerous urban environments overseas and rescue teams responding to disasters such as earthquakes or floods currently rely on remotely piloted unmanned aerial vehicles to provide a bird’s-eye view of the situation and spot threats that can’t be seen from the ground. But to know what’s going on inside an unstable building or a threatening indoor space often requires physical entry, which can put troops or civilian response teams in danger.
To address these challenges, DARPA issued a Broad Agency Announcement solicitation for the Fast Lightweight Autonomy (FLA) program. FLA focuses on creating a new class of algorithms to enable small, unmanned aerial vehicles to quickly navigate a labyrinth of rooms, stairways and corridors or other obstacle-filled environments without a remote pilot. The solicitation is available here.
Fast Lightweight Autonomy (FLA) - Federal Business Opportunities: Opportunities
The program aims to develop and demonstrate autonomous UAVs small enough to fit through an open window and able to fly at speeds up to 20 meters per second (45 miles per hour)—while navigating within complex indoor spaces independent of communication with outside operators or sensors and without reliance on GPS waypoints.
“Birds of prey and flying insects exhibit the kinds of capabilities we want for small UAVs,” said Mark Micire, DARPA program manager. “Goshawks, for example, can fly very fast through a dense forest without smacking into a tree. Many insects, too, can dart and hover with incredible speed and precision. The goal of the FLA program is to explore non-traditional perception and autonomy methods that would give small UAVs the capacity to perform in a similar way, including an ability to easily navigate tight spaces at high speed and quickly recognize if it had already been in a room before.”
Some Background and Related Developments
Autonomous robotic plane flies indoors at MIT - YouTube
Published on Aug 9, 2012
For
decades, academic and industry researchers have been working on control
algorithms for autonomous helicopters — robotic helicopters that pilot
themselves, rather than requiring remote human guidance. Dozens of
research teams have competed in a series of autonomous-helicopter
challenges posed by the Association for Unmanned Vehicle Systems
International (AUVSI); progress has been so rapid that the last two
challenges have involved indoor navigation without the use of GPS.But MIT's Robust Robotics Group — which fielded the team that won the last AUVSI contest — has set itself an even tougher challenge: developing autonomous-control algorithms for the indoor flight of GPS-denied airplanes. At the 2011 International Conference on Robotics and Automation (ICRA), a team of researchers from the group described an algorithm for calculating a plane's trajectory; in 2012, at the same conference, they presented an algorithm for determining its "state" — its location, physical orientation, velocity and acceleration. Now, the MIT researchers have completed a series of flight tests in which an autonomous robotic plane running their state-estimation algorithm successfully threaded its way among pillars in the parking garage under MIT's Stata Center.
Read more: http://web.mit.edu/newsoffice/2012/au...
How, J.P.; Bethke, B.; Frank, A.; Dale, D.; Vian, J., "Real-time indoor autonomous vehicle test environment," Control Systems, IEEE , vol.28, no.2, pp.51,64, April 2008
doi: 10.1109/MCS.2007.914691
Abstract: To investigate and develop unmanned vehicle systems technologies for autonomous multiagent mission platforms, we are using an indoor multivehicle testbed called real-time indoor autonomous vehicle test environment (RAVEN) to study long-duration multivehicle missions in a controlled environment. Normally, demonstrations of multivehicle coordination and control technologies require that multiple human operators simultaneously manage flight hardware, navigation, control, and vehicle tasking. However, RAVEN simplifies all of these issues to allow researchers to focus, if desired, on the algorithms associated with high-level tasks. Alternatively, RAVEN provides a facility for testing low-level control algorithms on both fixed- and rotary-wing aerial platforms. RAVEN is also being used to analyze and implement techniques for embedding the fleet and vehicle health state (for instance, vehicle failures, refueling, and maintenance) into UAV mission planning. These characteristics facilitate the rapid prototyping of new vehicle configurations and algorithms without requiring a redesign of the vehicle hardware. This article describes the main components and architecture of RAVEN and presents recent flight test results illustrating the applications discussed above.
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4472379&isnumber=4472219
Modeling and Adaptive Control of Indoor Unmanned Aerial Vehicles by Bernard MichiniSM.pdf
Modeling and Adaptive Control of Indoor Unmanned Aerial Vehicles by Bernard Michini
MASSACHUSETTS INSTITUTE OF TECHNOLOGY, September 2009
autonomousindoorhelicopter_iros.pdf
Autonomy and Navigation Technology Center (ANT)
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