Comparison of optical modeling and neural networks for robot guidance

  • Parasnis S
  • Velidandla S
  • Hall E
 et al. 
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Abstract

A truly autonomous robot must sense its environment and react appropriately. These issues attain greater importance in an outdoor, variable environment. Previous mobile robot perception systems have relied on hand-coded algorithms for processing sensor information. Recent techniques involve the use of artificial neural networks to process sensor data for mobile robot guidance. A comparison of a fuzzy logic control for an AGV and a neural network perception is described in this work. A mobile robot test bed has been constructed using a golf cart base. The test bed has a fuzzy logic controller which uses both vision and obstacle information and provides the steering and speed controls to the robot. A feed-forward neural network is described to guide the robot using vision and range data. Suitable criteria for comparison will be formulated and the hand-coded system compared with a connectionist model. A comparison of the two systems, with performance, efficiency and reliability as the criteria, will be achieved. The significance of this work is that it provides comparative tradeoffs on two important robot guidance methods.

Author-supplied keywords

  • Collision avoidance
  • Computer vision
  • Feedforward neural networks
  • Fuzzy control
  • Fuzzy sets
  • Intelligent robots
  • Mathematical models
  • Mobile robots
  • Motion planning
  • Sensor data fusion
  • Speed control

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Authors

  • Sameer Parasnis

  • Sasanka Velidandla

  • Ernest Hall

  • Sam Anand

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