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A Compressed Sensing Approach for Multiple Obstacle Localisation Using Sonar Sensors in Air.

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This study introduces a new method for autonomous navigation using inverse problems and compressed sensing to improve obstacle detection and mapping. This approach overcomes limitations of traditional sonar techniques, offering enhanced robotic mapping capabilities.

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Area of Science:

  • Robotics
  • Signal Processing
  • Applied Physics

Background:

  • Traditional autonomous navigation systems rely on sonar time-of-flight, which faces challenges with ultrasonic reflection interference.
  • Multiple reflections in complex environments necessitate numerous acquisitions for accurate mapping.

Purpose of the Study:

  • To present a novel approach for obstacle detection and localization in autonomous navigation.
  • To address the limitations of traditional sonar methods using inverse problems and compressed sensing.

Main Methods:

  • Utilized inverse problems and compressed sensing concepts for obstacle detection and localization.
  • Conducted experiments with a custom hardware platform featuring four ultrasonic transducers.
  • Compared compressed sensing (Orthogonal Matching Pursuit) with traditional image reconstruction methods.

Main Results:

  • Successfully reconstructed 2D images representing the environment's cross-section.
  • Demonstrated promising results for robotic mapping tasks compared to traditional methods.
  • Showcased the effectiveness of compressed sensing in handling complex ultrasonic signal interference.

Conclusions:

  • Compressed sensing offers a robust alternative to traditional sonar techniques for autonomous navigation.
  • The proposed method enhances obstacle detection and localization accuracy in complex environments.
  • This research paves the way for more efficient and reliable robotic mapping systems.