Artificial Neural Network Approach to Mobile Robot Localization
David Swords
Aalto University / School of Electrical Engineering
Department of Automation and Systems Technology
Thesis submitted in partial fulfillment of the requirements for the degree of
Master of Science in Technology
In the robotics community, localization is considered a solved problem, however, the topic is still open to investigation. Mobile robot localization has been focused on developing low-cost approaches and there has been great success using probabilistic methods. Parallel to this, and to a much lesser extent, artificial neural networks (ANNs) have been applied to the problem area with varying success.
A system is proposed in this thesis where the typical probabilistic approach is replaced with one based purely on ANNs. This type of localization attempts to harness the simplicity, scalability and adaptability that ANNs are known for. The ANN approach allows for the encapsulation of a number of steps and elements well known in a probabilistic approach, resulting in the elimination of an internal explicit map, providing pose estimate on network output and network update at runtime.
First, a coordinate-based approach to localization is explored: 1D and 2D trained maps with pose estimates. Second, the coordinate-based approach is eliminated in an effort to replicate a more biologically inspired localization. Finally, a path-finding algorithm applying the new localizaiton approach is presented
....
The FANN library (FANN) provides tools implementing, designing, visualizing
and simulating ANNs.
...
FANN provides many commandline functions and visualization tools for creating,
training and simulating ANNs. With the availablity of visualization tools,
like FANNTool, make it much easier to deveop ANNs and applying them to
common tasks like curve fitting, pattern recognition, and clustering.
...
FANNTool is a GUI to the FANN library which allows ease of use without the
need to program the function calls necessary to perform simple tasks. FANNTool
is an open source project, supported by a community of FANN users.
David Swords
Aalto University / School of Electrical Engineering
Department of Automation and Systems Technology
Thesis submitted in partial fulfillment of the requirements for the degree of
Master of Science in Technology
In the robotics community, localization is considered a solved problem, however, the topic is still open to investigation. Mobile robot localization has been focused on developing low-cost approaches and there has been great success using probabilistic methods. Parallel to this, and to a much lesser extent, artificial neural networks (ANNs) have been applied to the problem area with varying success.
A system is proposed in this thesis where the typical probabilistic approach is replaced with one based purely on ANNs. This type of localization attempts to harness the simplicity, scalability and adaptability that ANNs are known for. The ANN approach allows for the encapsulation of a number of steps and elements well known in a probabilistic approach, resulting in the elimination of an internal explicit map, providing pose estimate on network output and network update at runtime.
....
The FANN library (FANN) provides tools implementing, designing, visualizing
and simulating ANNs.
...
FANN provides many commandline functions and visualization tools for creating,
training and simulating ANNs. With the availablity of visualization tools,
like FANNTool, make it much easier to deveop ANNs and applying them to
common tasks like curve fitting, pattern recognition, and clustering.
...
FANNTool is a GUI to the FANN library which allows ease of use without the
need to program the function calls necessary to perform simple tasks. FANNTool
is an open source project, supported by a community of FANN users.
