College - Author 1
College of Engineering
Department - Author 1
Electrical Engineering Department
Degree Name - Author 1
BS in Electrical Engineering
College - Author 2
College of Engineering
Department - Author 2
Electrical Engineering Department
Degree - Author 2
BS in Electrical Engineering
Date
6-2025
Primary Advisor
Siavash Farzan, College of Engineering, Electrical Engineering Department
Abstract/Summary
This report presents the preliminary design for a distributed localization framework for a multi-robot system. Many robotics research papers provide simulations of proposed algorithms in regards to formation control and task allocation. However, it is often that these proposals are without hardware experiments, being limited only to simulation. The objective of this framework is to provide a hardware implementation of a distributed Kalman filtering algorithm for multi-agent localization, as well as provide grounds for future multi-agent experiments. The framework is implemented on a swarm of three Turtlebot3 mobile robots. The robots can accurately localize themselves with respect to other agents within a 0.5 m radius with up to 1 cm of accuracy. Using a private WiFi network, the robots can communicate and exchange information between each other, utilizing the node/topic and publisher/subscriber scheme within the ROS 2 platform. The multi-agent system operates in an ideal 2D environment, one that is flat and free of obstacles. To evaluate system performance, the root mean square error (RMSE) of the localization data, as well as the measured positional data, is plotted by a central computer. The RMSE of 3 agents is found to be under 0.02 m after 30 iterations. These results present significant validity of this algorithm for multi-agent localization. Many formation control and task allocation algorithms assume localization as a prerequisite for collision avoidance. While many localization algorithms have been proposed and have undergone valid simulation, this framework may serve as a hardware proof of concept for a selected distributed Kalman filtering algorithm and enable future hardware testing of formation control and task allocation.
URL: https://digitalcommons.calpoly.edu/eesp/692