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This study introduces a novel method for obstacle avoidance using bearing-only measurements, even when obstacle motion is unpredictable. It enhances safety by addressing local minima problems and improving obstacle tracking accuracy.

Keywords:
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Area of Science:

  • Robotics and Control Systems
  • Navigation and Perception

Background:

  • Traditional obstacle avoidance methods struggle with model-free, unpredictable obstacle motion.
  • Kalman-like filters are limited by their reliance on known motion models.
  • The local minima problem in artificial potential field methods can lead to collisions.

Purpose of the Study:

  • To develop a robust obstacle avoidance strategy for scenarios with unknown obstacle dynamics.
  • To address the limitations of existing methods in handling model-free obstacle motion.
  • To improve the accuracy and efficiency of obstacle state estimation.

Main Methods:

  • A revised artificial potential field method incorporating an angle-dependent factor to mitigate local minima.
  • An unknown input observer designed to estimate the position and velocity of obstacles with unmodeled dynamics.
  • Numerical simulations to validate the proposed approach.

Main Results:

  • The proposed method effectively avoids collisions caused by local minima.
  • The unknown input observer accurately estimates obstacle position and velocity.
  • Simulations confirm improved estimation accuracy and reduced termination time compared to standard methods.

Conclusions:

  • The novel approach offers a viable solution for obstacle avoidance in complex, unpredictable environments.
  • The integration of an angle-dependent potential field and an unknown input observer enhances navigation safety and efficiency.
  • This work advances the field of autonomous navigation for systems relying on bearing-only measurements.