(Georgia Institute of Technology, 2005-09)
Wu, Allen D.; Johnson, Eric N.; Proctor, Alison A.
Many onboard navigation systems use the Global Positioning System to bound the errors
that result from integrating inertial sensors over time. Global Positioning System information,
however, is not always accessible since it relies on external satellite signals. To this
end, a vision sensor is explored as an alternative for inertial navigation in the context of an
Extended Kalman Filter used in the closed-loop control of an unmanned aerial vehicle. The
filter employs an onboard image processor that uses camera images to provide information
about the size and position of a known target, thereby allowing the flight computer to derive
the target's pose. Assuming that the position and orientation of the target are known a priori,
vehicle position and attitude can be determined from the fusion of this information with inertial
and heading measurements. Simulation and flight test results verify filter performance
in the closed-loop control of an unmanned rotorcraft.
(Georgia Institute of Technology, 2005-06)
Calise, Anthony J.; Johnson, Eric N.; Sattigeri, Ramachandra J.; Watanabe, Yoko; Madyastha, Venkatesh
This paper discusses estimation and guidance
strategies for vision-based target tracking. Specific
applications include formation control of multiple unmanned
aerial vehicles (UAVs) and air-to-air refueling. We assume
that no information is communicated between the aircraft,
and only passive 2-D vision information is available to
maintain formation. To improve the robustness of the
estimation process with respect to unknown target aircraft
acceleration, the nonlinear estimator (EKF) is augmented with
an adaptive neural network (NN). The guidance strategy
involves augmenting the inverting solution of nonlinear line-of-sight (LOS) range kinematics with the output of an
adaptive NN to compensate for target aircraft LOS velocity.
Simulation results are presented that illustrate the various
approaches.
(Georgia Institute of Technology, 2005)
Johnson, Eric N.; Kannan, Suresh K.
For autonomous helicopter flight, it is common to separate the flight control problem into an inner loop that
controls attitude and an outer loop that controls the translational trajectory of the helicopter. In previous work,
dynamic inversion and neural-network-based adaptation was used to increase performance of the attitude control
system and the method of pseudocontrol hedging (PCH) was used to protect the adaptation process from actuator
limits and dynamics. Adaptation to uncertainty in the attitude, as well as the translational dynamics, is introduced,
thus, minimizing the effects of model error in all six degrees of freedom and leading to more accurate position
tracking. The PCH method is used in a novel way that enables adaptation to occur in the outer loop without
interacting with the attitude dynamics. A pole-placement approach is used that alleviates timescale separation
requirements, allowing the outer-loop bandwidth to be closer to that of the inner loop, thus, increasing position
tracking performance. A poor model of the attitude dynamics and a basic kinematics model is shown to be sufficient
for accurate position tracking. The theory and implementation of such an approach, with a summary of flight-test
results, are described.
(Georgia Institute of Technology, 2005-01)
Johnson, Eric N.; Proctor, Alison A.; Ha, Jin-Cheol; Tannenbaum, Allen R.
This paper describes the design, development, and
testing of an Unmanned Aerial Vehicle (UAV) with automated
capabilities: searching a prescribed area, identifying a specific
building within that area based on a small sign located on one
wall, and then identifying an opening into that building. This
includes a description of the automated search system along
with simulation and flight test results. Results include successful
evaluation at the McKenna Military Operations in Urban
Terrain flight test site.