Enhancing smart environments through an ai-assisted IORT agent

Abstract

The development of Internet technologies has led to the Internet of Things (IoT), going beyond connecting only traditional computers and smart phones to also connecting gadgets we use in our daily lives. Devices in various areas such as homes, industry, health care, and transportation can get more integrated and collaborate to answer different needs. Artificial intelligence (AI) powers this transformation by enhancing IoT devices' decision-making and automation capabilities. The synergy between AI and IoT creates smart environments. Within the broader landscape of IoT devices and smart spaces, the Internet of Robotic Things (IoRT) is a relatively new concept that integrates robotics with IoT technology. This integration provides new opportunities and enhances the capabilities of both technologies. IoT can enable remote access to robotic systems and allow robots to communicate and interact with external sensor data. Robots deployed in smart spaces (such as cities, hospitals, warehouses, or industry) make up IoRT. Robotic systems can make real-time decisions by accessing data from external IoT devices, enhancing operational efficiency, interconnectivity, spatial awareness, and adaptability to changing environments and tasks. This study presents a modular IoRT system that applies AI and IoT to enhance the autonomy, spatial awareness, and decision-making of mobile robots. The system is applicable to several areas, such as surveillance, manufacturing, health, warehousing, agriculture, industry, and transportation. We validated this research through a differential drive autonomous robot in a smart transportation context. IoT sensors integrated into a smart station monitor their conditions and provide intelligent information in real time to improve the reliability of the transportation infrastructure. The autonomous mobile agent receives and makes use of this information for its navigation. This system uses a differential drive autonomous robot as the mobile agent, equipped with a Raspberry Pi 4 running ROS (Robot Operating System) for localization, navigation, and mapping along with an orchestrator for interoperability with IoT devices and processes environmental sensor data to dynamically assist the robot's actions. The system integrates affordable IoT modules, such as NodeMCU and ESP32-CAM. The IoT modules (along with several sensors connected to them) are integrated within the smart station that serves as an external data acquisition hub for the overall system. The smart station includes an IoT ultrasonic sensor for detecting the presence of entities inside the smart station and an IoT camera (Esp32-CAM) for capturing images of the entity. When the system detects human presence from captured images, the robot receives navigation instructions to autonomously move to the station. The robot also has a Lidar sensor, an Arduino Nano, DC motors with quadrature encoders, and an onboard camera for live video provisioning during navigation. To enhance the robot's autonomy and decision making, artificial intelligence (AI) is used 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. A web application (implemented using the WebSocket protocol) serves the real-time communications of system nodes and live video feedback from the onboard robot camera. 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 monitoring through WebSockets. We evaluated the system's performance based on specific criteria. First, we assessed object detection accuracy by testing the ability of the detection module at the smart station to correctly identify human figures under different lighting conditions. This was crucial for initiating the robot's navigation process. Second, we analyzed the autonomous navigation performance of the robot. This included its ability to move safely to the target upon detecting a human figure (planning routes, avoiding obstacles, and reaching the target). Third, we analyzed the communication delays between the IoT modules, the orchestrator, and the robot by measuring the time that elapsed from the moment a component sent data to when it was received and processed by the respective system element. Last, we examined the efficiency of information flow and transfer on the web interface. We found that the WebSocket protocol was effective for real-time data transmissions, and it ensured messages and visual information from system components got delivered successfully. Our IoRT system demonstrates the potential for various smart domains, including cities, warehousing, health care, smart industry, agriculture, surveillance, and transportation. Key contributions include the modular design for easy adaptation to different applications, seamless integration of AI and IoT for improved autonomy, and real-time system monitoring using WebSockets. This research validates the effectiveness of the IoRT approach through a practical implementation, showcasing its impact on enhancing robotic operations in smart spaces.