Person:
Johnson,
Eric N.
Publication Search Results
Vision-Based Optimal Landing On a Moving Platform
Vision-Based Closed-Loop Tracking Using Micro Air Vehicles
Person:
Johnson,
Eric N.
1 - 2 of 2
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This paper describes a vision-based control architecture designed to enable autonomous landing on a moving platform.
The landing trajectory is generated by using the receding-horizon differential dynamic programming (DDP), an
optimal control method. The trajectory generation is aided by the output of a vision-based target tracking system. The
vision system uses multiple extended Kalman filters which allows us to estimate the position and heading of the moving
target via the observed locations. The combination of vision-based target tracking system and the receding-horizon
DDP gives an unmanned aerial vehicle the capability to adaptively generate a landing trajectory against tracking errors
and disturbances. Additionally, by adding the exterior penalty function to the cost of the DDP we can easily
constrain the trajectory from collisions and physically infeasible solutions. We provide key mathematics needed for
the implementation and share the results of the image-in-the-loop simulation and flight tests to validate the suggested
methodology.
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This paper describes the target detection and tracking
architecture used by the Georgia Tech Aerial Robotics team for
the American Helicopter Society (AHS) Micro Aerial Vehicle
(MAV) challenge. The vision system described enables vision-aided
navigation with additional abilities such as target detection
and tracking all performed onboard the vehicles computer.
The author suggests a robust target tracking method that does
not solely depend on the image obtained from a camera, but also
utilizes the other sensor outputs and runs a target location estimator.
The machine learning based target identification method
uses Haar-like classifiers to extract the target candidate points.
The raw measurements are plugged into multiple Extended
Kalman Filters (EKFs). The statistical test (Z-test) is used to
bound the measurement, and solve the corresponding problem.
Using Multiple EKFs allows us not only to optimally estimate
the target location, but also to use the information as one of the
criteria to evaluate the tracking performance. The MAV utilizes
performance-based criteria that determine whether or not to
initiate a maneuver such as hover or land over/on the target.
The performance criteria are closed in the loop which allows the
system to determine at any time whether or not to continue with
the maneuver. For Vision-aided Inertial Navigation System (VINS),
a corner Harris algorithm finds the feature points, and
we track them using the statistical knowledge. The feature
point locations are integrated in Bierman Thornton extended
Kalman Filter (BTEKF) with Inertial Measurement Unit (IMU)
and sonar sensor outputs to generate vehicle states: position,
velocity, attitude, accelerometer and gyroscope biases. A 6-
degrees-of-freedom quadrotor flight simulator is developed to
test the suggested method. This paper provides the simulation
results of the vision-based maneuvers: hovering over the target,
and landing on the target. In addition to the simulation results,
flight tests have been conducted to show and validate the system
performance. The 500 gram Georgia Tech Quadrotor (GTQ)-
Mini, was used for the flight tests. All processing is done
onboard the vehicle and it is able to operate without human
interaction. Both of the simulation and flight test results show
the effectiveness of the suggested method. This system and
vehicle were used for the AHS 2015 MAV Student Challenge
where the GPS-denied closed-loop target search is required. The
vehicle successfully found the ground target, and landed on the
desired location. This paper shares the data obtained from the
competition.