Autonomous Air Filtration Robot
Course Instructor
Venkittaraman Pallipuram Krishnamani
Abstract
This project presents the design, development, and implementation of a fully autonomous Mobile Air Filtration Robot intended to improve air quality in environments where pollutants, dust, allergens, and particulate matter can accumulate. Regions such as California’s Central Valley have a persistent issue in poor air quality where daily living conditions are often affected by wildlife smokes, agricultural emissions, and stagnant indoor environments. To address this issue, The project combines advanced sensing, real time decision making, SLAM based navigation, and adaptive air filtration control into one system that moves through indoor spaces without any assistance.
The Robot is built on a multidisciplinary engineering foundation involving embedded systems, robotics, mechanical design, and environmental sensing. A Raspberry Pi 5 running ROS 2 Jazzy forms the core of the computational architecture, managing communication between components, sensor integration, navigation, and system control. A 360° LiDAR module is utilized for environmental scanning and mapping, allowing the robot to construct accurate 2D maps and localize itself during movement within a room. The usage of SLAM integrated within ROS 2 allows for real time mapping and path planning, ensuring the robot avoids obstacles, identifies routes, and maintains localization even if the robot is physically moved by a person.
To measure air quality, the system incorporates the PMS5003, a particulate matter sensor, which constantly monitors PM1.0, PM2.5, and PM10 concentrations. Based on collected data, the robot evaluates the indoor Air Quality Index (AQI) levels and determines whether a location requires filtration. When the AQI exceeds a certain threshold, the robot will activate a filtration fan system with different variables of speed capable of adjusting airflow proportionally to detected pollution levels. This dynamic response ensures efficient filtration performance while minimizing power usage when air quality is already acceptable.
In addition to environmental monitoring and filtration, the robot features a fully autonomous recharging mechanism. A custom dock equipped with conductive charging prongs allows the robot to align itself, dock, and recharge without human intervention. Using navigation data and stored map information, the robot can return to its dock when battery levels are low, allowing for continuous operation throughout extended periods. The system also incorporates fail safes such as emergency stop behavior and collision avoidance.
The project highlights modularity and expandability. Each subsystem including power distribution, motor control, sensor suite, filtration assembly, and software stack is designed for independent development and testing while remaining fully integrated within the unified ROS II ecosystem. This allows for future enhancement such as wireless data reporting, smartphone integration, improved localization using AprilTags or ArUco markers, or more advanced environmental analytics.
Overall, this project demonstrates a practical approach to indoor air quality management through many engineering disciplines. By combining environmental sensing with intelligent mobility and adaptive filtration, the Mobile Air Filtration Robot provides scalable, efficient, and user-friendly methods for maintaining healthier indoor environments in homes, classrooms, and offices.
Autonomous Air Filtration Robot
This project presents the design, development, and implementation of a fully autonomous Mobile Air Filtration Robot intended to improve air quality in environments where pollutants, dust, allergens, and particulate matter can accumulate. Regions such as California’s Central Valley have a persistent issue in poor air quality where daily living conditions are often affected by wildlife smokes, agricultural emissions, and stagnant indoor environments. To address this issue, The project combines advanced sensing, real time decision making, SLAM based navigation, and adaptive air filtration control into one system that moves through indoor spaces without any assistance.
The Robot is built on a multidisciplinary engineering foundation involving embedded systems, robotics, mechanical design, and environmental sensing. A Raspberry Pi 5 running ROS 2 Jazzy forms the core of the computational architecture, managing communication between components, sensor integration, navigation, and system control. A 360° LiDAR module is utilized for environmental scanning and mapping, allowing the robot to construct accurate 2D maps and localize itself during movement within a room. The usage of SLAM integrated within ROS 2 allows for real time mapping and path planning, ensuring the robot avoids obstacles, identifies routes, and maintains localization even if the robot is physically moved by a person.
To measure air quality, the system incorporates the PMS5003, a particulate matter sensor, which constantly monitors PM1.0, PM2.5, and PM10 concentrations. Based on collected data, the robot evaluates the indoor Air Quality Index (AQI) levels and determines whether a location requires filtration. When the AQI exceeds a certain threshold, the robot will activate a filtration fan system with different variables of speed capable of adjusting airflow proportionally to detected pollution levels. This dynamic response ensures efficient filtration performance while minimizing power usage when air quality is already acceptable.
In addition to environmental monitoring and filtration, the robot features a fully autonomous recharging mechanism. A custom dock equipped with conductive charging prongs allows the robot to align itself, dock, and recharge without human intervention. Using navigation data and stored map information, the robot can return to its dock when battery levels are low, allowing for continuous operation throughout extended periods. The system also incorporates fail safes such as emergency stop behavior and collision avoidance.
The project highlights modularity and expandability. Each subsystem including power distribution, motor control, sensor suite, filtration assembly, and software stack is designed for independent development and testing while remaining fully integrated within the unified ROS II ecosystem. This allows for future enhancement such as wireless data reporting, smartphone integration, improved localization using AprilTags or ArUco markers, or more advanced environmental analytics.
Overall, this project demonstrates a practical approach to indoor air quality management through many engineering disciplines. By combining environmental sensing with intelligent mobility and adaptive filtration, the Mobile Air Filtration Robot provides scalable, efficient, and user-friendly methods for maintaining healthier indoor environments in homes, classrooms, and offices.
