Abstract
In this paper, we propose an evolutionary cognitive architecture to enable a mobile robot to cope with the task of visual navigation. Initially a graph based world representation is used to build a map, prior to navigation, through an appearance based scheme using only features associated with color information. During the next step, a genetic algorithm evolves a navigation controller that the robot uses for visual servoing, driving through a set of nodes on the topological map. Experiments in simulation show that an evolved robot, adapted to both exteroceptive and proprioceptive data, is able to successfully drive through a list of sub-goals minimizing the problem of local minima in which evolutionary process can sometimes get trapped. We also show that this approach is more expressive for defining a simplistic fitness formula yet descriptive enough for targeting specific goals.
Highlights
With respect of vision based robot navigation, most research work is focused on four major areas: map building and interpretation; self-localization; path planning; and obstacleavoidance
We describe a combination of a developmental method for autonomous map building and an evolutionary strategy to verify the results of the map interpretation in terms of navigation usability
This paper explores the advantages of evolutionary subgoal robot navigation with a cognitive map architecture
Summary
With respect of vision based robot navigation, most research work is focused on four major areas: map building and interpretation; self-localization; path planning; and obstacleavoidance. Of these four major research areas, self-localization is of key importance. Our strategy involves two discrete phases: map building and navigation phase. In the first phase an agent freely explores a pre-determined simulated terrain, collecting visual signatures corresponding to positions in the environment. A self-organizing algorithm builds a graph representation of the environment with nodes corresponding to known places and edges to known pathways
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Research in Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
