Sequential Localizing and Mapping: A Navigation Strategy via Enhanced Subsumption Architecture

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ID: 115616
2020
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Abstract
In this paper, we present a navigation strategy exclusively designed for social robots with limited sensors for applications in homes. The overall system integrates a reactive design based on subsumption architecture and a knowledge system with learning capabilities. The component of the system includes several modules, such as doorway detection and room localization via convolutional neural network (CNN), avoiding obstacles via reinforcement learning, passing the doorway via Canny edge’s detection, building an abstract map called a Directional Semantic Topological Map (DST-Map) within the knowledge system, and other predefined layers within the subsumption architecture. The individual modules and the overall system are evaluated in a virtual environment using Webots simulator.
Reference Key
othman2020sensorssequential Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kamal M. Othman;Ahmad B. Rad;Othman, Kamal M.;Rad, Ahmad B.;
Journal sensors
Year 2020
DOI
10.3390/s20174815
URL
https://doi.org/10.3390/s20174815
Keywords
reinforcement learning slam social robots robotics navigation subsumption architecture

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