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Advanced obstacle avoidance for a laser based wheelchair using optimised Bayesian neural networks.

Hoang T Trieu1, Hung T Nguyen, Keith Willey

  • 1University of Technology, Sydney, Broadway, NSW, Australia. thtrieu@eng.uts.edu.au

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
PubMed
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This study introduces an advanced obstacle avoidance system for intelligent wheelchairs using optimized Bayesian neural networks. The new method navigates smoother paths compared to the VFH algorithm.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Intelligent wheelchairs require sophisticated navigation systems for safe and efficient operation.
  • Existing obstacle avoidance algorithms may lack the adaptability for complex indoor environments.

Purpose of the Study:

  • To develop and evaluate an advanced obstacle avoidance method for laser-based intelligent wheelchairs.
  • To improve navigation path smoothness and optimality using optimized Bayesian neural networks.

Main Methods:

  • Utilized three specialized Bayesian neural networks for distinct navigation sub-tasks: doorway passage, corridor navigation, and general obstacle avoidance.
  • Integrated real-time mapping with actual wheelchair dimensions to determine usable accessible space.
  • Employed a Bayesian framework for optimal neural network structure determination and Bayesian rule-based training.

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Main Results:

  • The proposed Bayesian neural network system demonstrated superior performance compared to the Vector Field Histogram (VFH) algorithm.
  • The system navigated smoother paths, closely approximating optimal trajectories in experimental trials.
  • Accurate determination of usable space enhanced navigation precision.

Conclusions:

  • Optimized Bayesian neural networks offer a robust and effective solution for intelligent wheelchair obstacle avoidance.
  • The developed method provides a significant advancement in autonomous navigation for assistive devices.
  • This approach enhances safety and user experience in intelligent wheelchair applications.