An AI-Assisted Autonomous IoRT Agent for Smart Spaces

The Internet of Robotic Things (IoRT) integrates autonomous robots into smart environments and enables advanced applications in various domains such as smart cities, warehouses, and factories. In this study, we implement a modular IoRT system that can be adapted to many application domains. Our system uses a differential drive autonomous robot with ROS (Robot Operating System) for localization and navigation along with an orchestrator for interoperability with IoT devices and processes environmental sensor data to dynamically assist the robot's actions. To enhance the robot's autonomy and decision- making, we use artificial intelligence (AI) in two areas: (i) real-time human detection using AI computer vision to improve spatial awareness, and (ii) determining and following the shortest path for navigation. When a human is detected at the smart station (designated area equipped with an IoT ultrasonic sensor and an IoT camera), the robot autonomously navigates there. This implementation illustrates an exemplary use of IoRT in the transportation domain. The novel contributions of this work are the integration of IoT devices, an autonomous robot, and AI-based methods in an IoRT system, modularity that supports adaptation to different applications, and real-time system monitorina throuah WebSockets.

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Publication Details

Published
2024-10-26
DOI
https://doi.org/10.1109/ubmk63289.2024.10773582
Citations
1
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
0.68
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article

An AI-Assisted Autonomous IoRT Agent for Smart Spaces

Sanem Kabadayı, Yakup Kayataş
1 citations
Human Mobility and Location-Based Analysis
0.68
article

An AI-Assisted Autonomous IoRT Agent for Smart Spaces

Sanem Kabadayı, Yakup Kayataş
article en
1 citations

Abstract

The Internet of Robotic Things (IoRT) integrates autonomous robots into smart environments and enables advanced applications in various domains such as smart cities, warehouses, and factories. In this study, we implement a modular IoRT system that can be adapted to many application domains. Our system uses a differential drive autonomous robot with ROS (Robot Operating System) for localization and navigation along with an orchestrator for interoperability with IoT devices and processes environmental sensor data to dynamically assist the robot's actions. To enhance the robot's autonomy and decision- making, we use artificial intelligence (AI) in two areas: (i) real-time human detection using AI computer vision to improve spatial awareness, and (ii) determining and following the shortest path for navigation. When a human is detected at the smart station (designated area equipped with an IoT ultrasonic sensor and an IoT camera), the robot autonomously navigates there. This implementation illustrates an exemplary use of IoRT in the transportation domain. The novel contributions of this work are the integration of IoT devices, an autonomous robot, and AI-based methods in an IoRT system, modularity that supports adaptation to different applications, and real-time system monitorina throuah WebSockets.

Istanbul Technical University (TR)
Openalex Percentile: Top 20%
Human Mobility and Location-Based Analysis
0.68
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