This paper discusses customizing a popular robot development platform "Turtlebot3" for Odor Source Localization (OSL) task. OSL technology allows a robot to locate and navigate to odor sources in an unknown environment. Turtlebot3 is an agile robot platform that includes Raspberry Pi for on-device computation and an Open-source Control module for ROS (OpenCR) board for additional sensor connection. It runs on Ubuntu and “Robot Operating System (ROS)” that allows it to send sensor data to and receive heading commands from a remote computer that can run complex algorithms. In combination, this robotics platform can be customized to perform a wide variety of robot tasks. This paper focuses on the additional olfactory sensor installation for the OSL experiment. It also discusses an olfactory-based navigation algorithm named moth-inspired algorithm for OSL task. The algorithm was applied to real-world experiments with varying conditions. The experiments show that the moth-inspired algorithm successfully navigates to the odor source in laminar airflow environments. The paper also discusses the future scope of adding vision sensors and machine learning methods.
We used Turtlebot3 as the base for our robotic agent. In addition to the onboard vision sensors, we added an anemometer and a chemical sensor for olfactory detection.
Robot Operating System (ROS) connects the robot platform to a remote computer over a local area network. ROS publishes the sensor readings from the robot, which are subscribed to by the navigation algorithm running on the remote computer. The algorithm uses these readings to calculate and publish heading commands that the robot then executes.
We used the moth-inspired navigation algorithm for robotic odor source localization.
@inproceedings{hassan2023multi,
title={Multi-Modal Robotic Platform Development for Odor Source Localization},
author={Hassan, Sunzid and Wang, Lingxiao and Mahmud, Khan Raqib},
booktitle={2023 Seventh IEEE International Conference on Robotic Computing (IRC)},
pages={59--62},
year={2023},
organization={IEEE}
}