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Reactive navigation in dynamic environment using a multisensor predictor.

K T Song1, C C Chang

  • 1Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2008
PubMed
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This study presents a reactive navigation system for autonomous mobile robots using a neural network to predict obstacle motion, enabling real-time collision avoidance in dynamic environments.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Autonomous mobile robots require robust navigation in complex, dynamic environments.
  • Predicting and avoiding moving obstacles is a critical challenge for real-time robot operation.

Purpose of the Study:

  • To develop and validate a reactive navigation system for autonomous mobile robots.
  • To enable effective obstacle avoidance in unstructured, dynamic environments through motion prediction.

Main Methods:

  • A multisensor-based obstacle predictor combining CCD camera and ultrasonic range finder data.
  • Utilizing an off-line trained neural network for real-time prediction of obstacle positions.
  • Implementing a virtual force-based navigation method for collision prevention.

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Main Results:

  • Successful prediction of obstacle positions at the next sampling instant.
  • Verification of the navigation system's effectiveness through simulations with multiple moving objects.
  • Demonstration of successful real-world navigation on an experimental mobile robot.

Conclusions:

  • The proposed reactive navigation system effectively prevents collisions with moving obstacles.
  • The integration of sensor data and neural network prediction enables real-time autonomous robot navigation.
  • The system shows promise for applications in dynamic and unstructured environments.