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Related Experiment Video

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Sequential Localizing and Mapping: A Navigation Strategy via Enhanced Subsumption Architecture.

Kamal M Othman1, Ahmad B Rad1

  • 1Autonomous and Intelligent Systems Laboratory, School of Mechatronic Systems Engineering Simon Fraser University, Surrey, BC V3T 0A3, Canada.

Sensors (Basel, Switzerland)
|August 30, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel navigation strategy for social robots with limited sensors, utilizing a hybrid system of reactive design and a learning knowledge base for home applications.

Keywords:
SLAMreinforcement learningrobotics navigationsocial robotssubsumption architecture

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Social robots require efficient navigation in domestic environments.
  • Limited sensor capabilities pose challenges for robot autonomy.
  • Integrating reactive and knowledge-based systems can enhance robot navigation.

Purpose of the Study:

  • To develop and evaluate a navigation strategy for social robots with limited sensors.
  • To integrate a reactive design with a learning knowledge system.
  • To enable autonomous navigation in home environments.

Main Methods:

  • A subsumption architecture combined with a learning knowledge system.
  • Modules for doorway detection and room localization using convolutional neural networks (CNN).
  • Obstacle avoidance via reinforcement learning and doorway passage using Canny edge detection.
  • Creation of a Directional Semantic Topological Map (DST-Map).

Main Results:

  • The proposed system successfully integrates various navigation modules.
  • Convolutional neural networks (CNN) enabled effective doorway detection and room localization.
  • Reinforcement learning facilitated efficient obstacle avoidance.
  • The Directional Semantic Topological Map (DST-Map) provided an abstract representation for navigation.

Conclusions:

  • The developed navigation strategy is effective for social robots with limited sensors in home environments.
  • The hybrid approach combining reactive control and a learning knowledge system enhances robot navigation capabilities.
  • The system's performance was validated in a virtual environment using the Webots simulator.