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A Novel Artificial Organic Control System for Mobile Robot Navigation in Assisted Living Using Vision- and

Hiram Ponce1, Ernesto Moya-Albor1, Jorge Brieva1

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This study introduces a novel navigation system for robots in assisted living (RAL). The system uses artificial organic controllers with vision-based strategies like Hermite optical flow and convolutional neural networks for safe obstacle avoidance.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Robots in assisted living (RAL) offer support for elderly care.
  • Mobile robot navigation in dynamic environments like assisted living facilities is complex.
  • Existing systems often struggle with real-time adaptation and safety.

Purpose of the Study:

  • To develop a novel, nature-inspired control system for mobile RAL navigation.
  • To enhance robot perception and judgment for safer elderly care environments.
  • To effectively avoid both mobile and fixed obstacles using advanced vision techniques.

Main Methods:

  • Proposed a nature-inspired control system for mobile robots in assisted living.
  • Integrated Hermite optical flow (OF) for obstacle motion detection.
  • Utilized convolutional neural networks (CNNs) for obstacle distance estimation, trained with OF and ultrasonic data.
  • Employed a monocular camera in a simulated 3D environment (V-REP).

Main Results:

  • The proposed system successfully navigated and avoided obstacles in simulation.
  • Security and smoothness metrics demonstrated effective performance.
  • Quantitative evaluation confirmed the viability of the vision-based approach.

Conclusions:

  • The novel artificial organic controller with vision-based strategies is effective for mobile RAL navigation.
  • This approach enhances safety and efficiency in assisted living environments.
  • The system shows promise for real-world application in elderly care robotics.