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A neural network approach to complete coverage path planning.

Simon X Yang1, Chaomin Luo

  • 1Advanced Robotics and Intelligent Systems Laboratory, School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada. syang@uoguelph.ca

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This study introduces a new neural network for cleaning robots to plan paths that cover entire areas while avoiding obstacles. The computationally simple method generates collision-free paths in dynamic environments.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Complete coverage path planning (CCPP) is crucial for autonomous robots in tasks like cleaning, detection, and harvesting.
  • Existing CCPP methods often struggle with non-stationary environments and real-time obstacle avoidance.
  • Robotic applications requiring CCPP include vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners.

Purpose of the Study:

  • To propose a novel neural network approach for complete coverage path planning (CCPP) in cleaning robots.
  • To enable autonomous obstacle avoidance in non-stationary environments.
  • To develop a computationally simple algorithm for generating efficient robot paths.

Main Methods:

  • A topologically organized neural network with neurons modeled by shunting equations (derived from Hodgkin-Huxley membrane equation).
  • Local lateral connections among neurons to process environmental information.
  • Autonomous path generation based on the neural network's activity landscape and the robot's previous location.

Main Results:

  • The proposed neural network model successfully generated collision-free complete coverage paths for cleaning robots.
  • The algorithm demonstrated effectiveness in non-stationary environments.
  • Simulation results validated the model's capability for robust path planning.

Conclusions:

  • The novel neural network approach provides an effective solution for complete coverage path planning with obstacle avoidance.
  • The method's computational simplicity makes it suitable for real-time applications in cleaning robots and other robotic systems.
  • This research contributes to advancing autonomous navigation in complex and dynamic environments.