To accelerate research and development of the autonomous capabilities of micro aerial vehicles we have developed flight control framework, ROSflight, as a research tool. ROSflight makes development of autopilot code easier and more efficient by minimizing the use of embedded systems, incorporating the Robot Operating System and using off-theshelf and open-source hardware and software. Motivation and applications for use in the research community are discussed. Analysis of loop rate and communication bandwidth are presented as well as results from flight demonstration of two multirotor aircraft.
Many current approaches for navigation of micro air vehicles (MAVs) in GPS-degraded environments use a globally-referenced state for estimation and control, even though this state is not observable when GPS is unavailable. By working with respect to a local reference frame, the relative navigation (RN) framework presented in this paper ensures that the state maintains observability and that the uncertainty remains bounded, consistent, and normally-distributed. RN further insulates flight-critical estimation and control processes from the large global updates common in GPSdegraded MAV flight. This paper provides a thorough description of the details needed to successfully implement the RN framework on a MAV. The practicality of RN is demonstrated in several long flight tests in unknown, GPS-denied and GPS-degraded environments. The relative front end is shown to produce low-drift estimates and smooth, stable control while leveraging off-the-shelf algorithms. The system runs in real time with onboard processing, fuses a variety of vision sensors, works indoors and outdoors, and does not require special tuning for particular sensors or environments. RN is also shown to produce globally-consistent, metric, and localized maps by incorporating loop closures and intermittent GPS measurements. This map is used to demonstrate autonomous completion of mission objectives. By subtly restructuring the estimation framework, RN promotes a paradigm shift that avoids many issues inherent in GPS-degraded navigation.
This paper presents an approach for finding possible landing sites for a rotorcraft from an inertially referenced point-cloud model of the environment. To identify potential landing sites that are suitably flat and level, a grid-based random sample consensus algorithm separates the terrain map into discrete areas for plane-fitting analysis. Landing sites are selected that satisfy constraints on flatness and levelness while optimizing the surveillance target's visibility. Flight test results are presented from a small multirotor aircraft flying over a scale-model cityscape. Results from real-time landing-site experiments are presented and discussed.
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