Genetic programming for robot vision

  • Authors:
  • Martin C. Martin

  • Affiliations:
  • Artificial Intelligence Laboratory, Massachusetts Institute of Technology

  • Venue:
  • ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
  • Year:
  • 2002

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Abstract

Genetic Programming was used to create the vision subsystem of a reactive obstacle avoidance system for an autonomous mobile robot. The representation of algorithms was specifically chosen to capture the spirit of existing, hand written vision algorithms. Traditional computer vision operators such as Sobel gradient magnitude, median filters and the Moravec interest operator were combined arbitrarily. Images from an office hallway were used as training data. The evolved programs took a black and white camera image as input and estimated the location of the lowest non-ground pixel in a given column. The computed estimates were then given to a handwritten obstacle avoidance algorithm and used to control the robot in real time. Evolved programs successfully navigated in unstructured hallways, performing on par with hand-crafted systems.