Autonomous robot navigates maze using sensing & ML.
Sponsor: NXP
Student Team: Shivani Mruthyunjaya, David Tavarez, Darryl Murray, Eignar Cienfuegos
Project Advisor(s): Chris Brown
Faculty Instructor: Mr. Mark Welker
Our project is an autonomous robot designed to navigate a maze by using computer vision to interpret external visual indicators and sensors to detect walls for collision avoidance. The robot will begin at a predetermined starting position and autonomously traverse through the maze while making decisions at junctions when multiple movement directions are available. Using a machine learning image classification algorithm, the robot will recognize and classify maze images as left, right, or forward indicators and follow them until it reaches a designated finish image. During the initial exploration run, the robot will record and map the route, allowing it to complete a second traversal more efficiently using the generated map. This project is important due to its potential applications in exploring inaccessible environments and assisting with automated transportation of goods.
E2.06 Presentation
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E2.06 Poster Pitch
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Team Contact Information
Team Project Manager: Shivani Mruthyunjaya, aon9@txstate.edu
Faculty Instructor: Mr. Mark Welker, m_w399@txstate.edu
Let us know what you think! You can evaluate our project here: E2.06 Evaluation Form