Mark Neal and Frédéric Labrosse.
Rotation-invariant appearance based maps for robot
navigation using an artificial immune network
In Proceedings of the Congress on Evolutionary Computation,
Portland, Oregon, USA, 2004.
The treatment of image data for robotic applications such as
navigation, path planning and localization has always been
problematic when working in image space (using the appearance of
the environment) rather than in Cartesian space (using the
geometry of the environment). This is due to both computational
overhead introduced by the large amount of data that needs to be
manipulated and the high-dimensionality of the image space.
We present results from a novel approach using an artificial
immune network construction algorithm which dramatically
reduces the dimensionality of the image space and generates
network structures useful for navigation and localization. The
technique uses the artificial immune network mechanism to link
images with similar properties, thus corresponding to similar
poses of the robot, into a network which can be displayed in
two dimensions. This generates an intuitive representation of
the environment which the robot has experienced in a way which
can also be traversed in order to perform path-planning in the
space of visual experiences.
A network generated as a mobile robot moves around in its
environment is presented, and related topologically to the
movements made by the robot. Properties of the networks
produced are discussed with relation to the visual complexity
of the environment experienced by the robot. In general,
regions of the environment which appear homogeneous produce
fewer nodes and edges in the network, and regions of a more
heterogeneous appearance produce denser, more highly
connected network structures.