Interaction with the world is necessary for both animals and robots to complete
tasks. This interaction requires a sense of self, or the orientation of the robot or
animal with respect to the world. Creating and maintaining this model is a task
which is easily maintained by animals, however can be difficult for robots due to
the uncertainties in the world, sensing, and movement of the robot. This estimation
difficulty is increased in sensory deprived environments, where no external, inputs
are available to correct the estimate. Therefore, self generated cues of movement
are needed, such as vestibular input in an animal, or accelerometer input in a robot.
In spite of the difficulties, animals can easily maintain this model. This leads to the
question of whether we can learn from nature by examining the biological mechanisms
for pose estimation in animals. Previous work has shown that neural fields coupled
with a mechanism for updating the estimate can be used to maintain a pose estimate
through a sustained area of activity called a packet. Analysis of this mechanism
however has shown conditions where the field can provide unexpected results or break
down due to high accelerations input into the field. This analysis illustrates the
challenges of controlling the activity packet size under strong inputs, and a limited
speed capability using the existing mechanism. As a result of this, a novel weight
combination method is proposed to provide a higher speed and increased robustness.
The results of this is an increase of over two times the existing speed capability, and
a resistance of the field to break down under strong rotational inputs.
This updated neural field model provides a method for maintaining a stable pose
estimate. To show this, a novel comparison between the proposed neural field model
and the Kalman filter is considered, resulting in comparable performance in pose
prediction. This work shows that an updated neural field model provides a biologically
plausible pose prediction model using Bayesian inference, providing a biological
analogue to a Kalman filter.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/21377 |
| Date | 22 February 2013 |
| Creators | Connors, Warren Anthoney |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
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