The current generation of micro air vehicles (MAVs) has reached a plateau in its utility, due to reliance on single-mode locomotion. A micro vehicle capable of both aerial AND terrestrial locomotion would enjoy great utility in first responder and reconnaissance applications. To this end, BioRobots, LLC, University of Florida (UF), Case Western Reserve University, and Naval Postgraduate School (NPS) have undertaken the development of MMALV, the Morphing Micro Air-Land Vehicle. MMALV combines the UF-MAV flexible wing technology with Mini-Whegs terrestrial running gear, developed at Case. Two generations of MMALV have been developed that are capable of flying, landing, walking, and regaining flight by walking off a two-story building. To our knowledge, MMALV is the only vehicle of its size that integrates all of these functions.

A sensor platform has been developed that is capable of both aerial and terrestrial locomotion, as well as transitioning between the two. The morphing micro air-land vehicle (MMALV) implements biological inspiration in both flying and walking. MMALV integrates the University of Florida's micro air vehicle (MAV) technology with the terrain mobility of Mini-Whegs/spl trade/. Fabricated of lightweight carbon fiber, the UF-MAV employs a flexible wing design to achieve improved stability over other MAVs of similar size. Mini-Whegs/spl trade/ employs the patented (pending) wheel leg running gear that makes the Whegs/spl trade/ and Mini-Whegs/spl trade/ line of robots fast, agile, and efficient. MMALV has a 30.5cm wingspan, and is 25.4cm long. Terrestrial locomotion is achieved using two independently controlled wheel legs, which are differentially actuated to perform turning. The vehicle successfully performs the transition from flight to walking. Furthermore, MMALV is capable of transitioning from terrestrial to aerial locomotion by walking off a structure of only 20 feet. A wing retraction mechanism improves the portability of the vehicle, as well as its terrestrial stealth and ability to enter small openings.

Recently, we have implemented a computer-vision based horizon-tracking algorithm for flight stability and autonomy in micro air vehicles (MAVs) [S. M. Ettinger et al., 2002]. Occasionally, this algorithm fails in scenarios where the underlying Gaussian assumption for the sky and ground appearances is not appropriate. Therefore, in this paper, we present a general statistical image modeling framework which we have use to build prior models of the sky and ground. Once trained, these models can be incorporated into our existing horizon-tracking algorithm. Since the appearances of the sky and ground vary enormously, no single feature is sufficient for accurate modeling: as such, we rely both on color and texture as critical features in our modeling framework. Specifically, we choose hue and intensity for our color representation, and the complex wavelet transform (CWT) for our texture representation. We then use hidden Markov tree (HMT) models, which are particularly well suited for the CWT's inherent tree structure, as our underlying statistical models over our feature space. With this approach, we have achieved reliable and robust image segmentation of flight images from on-board our MAVs as well as on more difficult-to-classify sky/ground images.

Substantial progress has been made recently towards designing, building and test-flying remotely piloted Micro Air Vehicles (MAVs) and small UAVs. We seek to complement this progress in overcoming the aerodynamic obstacles to flight at very small scales with a vision-guided flight stability and autonomy system, based on a robust horizon detection algorithm. In this paper, we first motivate the use of computer vision for MAV autonomy, arguing that given current sensor technology, vision may be the only practical approach to the problem. We then describe our statistical vision-based horizon detection algorithm, which has been demonstrated at 30 Hz with over 99.9% correct horizon identification. Next, we develop robust schemes for the detection of extreme MAV attitudes, where no horizon is visible, and for the detection of horizon estimation errors, due to external factors such as video transmission noise. Finally, we discuss our feedback controller for self-stabilized flight, and report results on vision-based autonomous flights of duration exceeding ten minutes.