Maze Navigation with a Multi-Robot team

Overview

Exploring the domain of robotics, particularly in navigation and goal attainment, presents challenges for multi-robot systems within intricate environments. This project focuses on harnessing the advanced capabilities of ROS 2 Foxy to tackle these challenges. The main goal is to design and evaluate navigation and goal-seeking systems for multiple robots. Key strategies involve implementing and scrutinizing navigation algorithms to improve efficiency, mitigate collision risks, and optimize goal achievement timelines. The overarching objective is to streamline operations, minimize recovery instances, and enhance overall performance across the robot fleet. 

Community Benefit

Our senior project pioneers a cutting-edge maze simulation platform, integrating Gazebo with SLAM navigation on ROS-powered robots. This advances robotics education and research, enabling autonomous robots with 360° LiDAR sensors to learn and solve complex mazes. Our work lays the groundwork for future innovations in search and rescue, transportation, and urban planning. It inspires exploration of robotics and AI, provides an open platform for experimentation, and prepares society for a future with autonomous systems. 

Team Members

• Misael Concepcion
• Miguel Gines
• Valery Noland
• Kevin Pham
• Ryan Sachs 

Sponsored By

• Dr. Xiangnan Zhong