Insects posses an incredible ability to navigate their environment at high speed, despite
having small brains and limited visual acuity. Through selective pressure they have
evolved computationally efficient means for simultaneously performing navigation tasks
and instantaneous control responses. The insect’s main source of information is visual,
and through a hierarchy of processes this information is used for perception; at the
lowest level are local neurons for detecting image motion and edges, at the higher level
are interneurons to spatially integrate the output of previous stages. These higher
level processes could be considered as models of the insect's environment, reducing the
amount of information to only that which evolution has determined relevant. The scope
of this thesis is experimenting with biologically inspired visual control of flying robots
through information processing, models of the environment, and flight behaviour.
In order to test these ideas I developed a custom quadrotor robot and experimental
platform; the 'wasp' system. All algorithms ran on the robot, in real-time or better,
and hypotheses were always verified with flight experiments.
I developed a new optical flow algorithm that is computationally efficient, and able
to be applied in a regular pattern to the image. This technique is used later in my
work when considering patterns in the image motion field.
Using optical flow in the log-polar coordinate system I developed attitude estimation
and time-to-contact algorithms. I find that the log-polar domain is useful for
analysing global image motion; and in many ways equivalent to the retinotopic arrange-
ment of neurons in the optic lobe of insects, used for the same task.
I investigated the role of depth in insect flight using two experiments. In the first
experiment, to study how concurrent visual control processes might be combined, I
developed a control system using the combined output of two algorithms. The first
algorithm was a wide-field optical flow balance strategy and the second an obstacle
avoidance strategy which used inertial information to estimate the depth to objects in
the environment - objects whose depth was significantly different to their surround-
ings. In the second experiment I created an altitude control system which used a model
of the environment in the Hough space, and a biologically inspired sampling strategy,
to efficiently detect the ground. Both control systems were used to control the flight
of a quadrotor in an indoor environment.
The methods that insects use to perceive edges and control their flight in response
had not been applied to artificial systems before. I developed a quadrotor control
system that used the distribution of edges in the environment to regulate the robot
height and avoid obstacles. I also developed a model that predicted the distribution of
edges in a static scene, and using this prediction was able to estimate the quadrotor
altitude.
| Identifer | oai:union.ndltd.org:canterbury.ac.nz/oai:ir.canterbury.ac.nz:10092/8729 |
| Date | January 2013 |
| Creators | Stowers, John Ross |
| Publisher | University of Canterbury. Electrical and Computer Engineering |
| Source Sets | University of Canterbury |
| Language | English |
| Detected Language | English |
| Type | Electronic thesis or dissertation, Text |
| Rights | Copyright John Ross Stowers, http://library.canterbury.ac.nz/thesis/etheses_copyright.shtml |
| Relation | NZCU |
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