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Development of an Audio-based Virtual Gaming Environment to Assist with Navigation Skills in the Blind
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Reactive navigation in extremely dense and highly intricate environments.

Javier Antich Tobaruela1, Alberto Ortiz Rodríguez1

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Summary

This study combines two reactive navigation approaches to improve autonomous robot control. The new method effectively navigates complex environments, overcoming limitations of individual techniques.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Reactive navigation enables robots to make control decisions using sensor data, suitable for limited hardware and unknown environments.
  • A key limitation of reactive navigation is the potential for robots to become trapped in concave or cluttered areas.
  • Significant research has focused on improving reactive navigation to handle complex obstacle scenarios.

Purpose of the Study:

  • To enhance reactive navigation by combining the strengths of existing approaches for cluttered and intricate environments.
  • To develop a robust navigation strategy for autonomous mobile robots facing challenging obstacle configurations.

Main Methods:

  • Selected the optimal reactive approach for densely cluttered environments.
  • Selected the optimal reactive approach for circumventing large, intricate obstacles.
  • Integrated these two approaches to leverage their combined capabilities.

Main Results:

  • The combined approach demonstrated superior performance compared to the individual methods.
  • Experimental validation in both simulated and real-world scenarios confirmed the effectiveness.
  • The integrated strategy successfully navigated complex environments where individual methods faltered.

Conclusions:

  • The proposed combined reactive navigation approach offers a significant improvement over existing methods.
  • This integrated strategy enhances the ability of autonomous robots to navigate challenging environments.
  • The findings suggest a promising direction for developing more capable and reliable autonomous mobile robots.