The successful operation of small unmanned aircraft systems (sUAS) in dynamic environments demands robust stability in the presence of exogenous disturbances. Flying insects are sensor-rich platforms, with highly redundant arrays of sensors distributed across the insect body that are integrated to extract rich information with diminished noise. This work presents a novel sensing framework in which measurements from an array of accelerometers distributed across a simulated flight vehicle are linearly combined to directly estimate the applied forces and torques with improvements in SNR. In simulation, the estimation performance is quantified as a function of sensor noise level, position estimate error, and sensor quantity.
Commercially available speed controllers, motors, and propellers typically comprise the powertrains of many micro aerial robotic systems, such as quadrotor vehicles. As on board state sensing and processing improves, actuation bandwidth is becoming a significant bottleneck that limits the performance of the entire closed loop system. The performance of the commercial products can be greatly enhanced through the implementation of classical control methods directly at the powertrain level. In this paper, reduced order open loop models for three representative commercially available powertrains were estimated and were compared with closed loop equivalents. Further performance improvement is realized by the addition of a static inverse to mitigate the steady state structured uncertainty of the system.
Visual sensing is an attractive method to allow small, palm-sized flying vehicles to navigate complex environments without collisions. Visual processing for unmanned vehicles, however, is typically computationally intense. Insects are able to extract structural information about the environment by appropriate control of self-motion and efficient processing of the visual field. This paper presents a methodology that attempts to capture the insect’s ability to do this by constructing a nonlinear observer with provable stability via a Lyapunov analysis. Furthermore, the persistency of excitation condition for the observer illustrates the need for a zig-zagging flight style exhibited by certain insects.
A 12 gram fly-inspired flapping wing micro air vehicle was stabilized in the yaw degree of freedom using insectbased
wing kinematic for lift generation and control actuation. The characteristic parameters of biological insect
flapping flight are described. The integration of this parametric understanding of biological flight into the design of
the vehicle is also discussed.
This paper presents the development of a static estimator for obtaining state information from optic flow and
radar measurements. It is shown that estimates of translational and rotational speed can be extracted using a
least squares inversion. The approach is demonstrated in a simulated three dimensional urban environment on an
autonomous quadrotor micro-air-vehicle (MAV). The resulting methodology has the advantages of computation
speed and simplicity, both of which are imperative for implementation on MAVs due to stringent size, weight,
and power requirements.
We envision situational awareness developed through warfighters deployment of a system of diverse mobile,
communicating platforms that cooperate to provide full coverage of interior and exterior spaces. The goal of the
ARL-MAST Center on Microsystem Mechanics is to perform the fundamental research that will enable flying
and ambulating platforms to achieve the required mobility for the proposed missions and environments. In this
paper the fundamental issues and challenges associated with achieving this goal will be discussed.