Thesis Defense Announcement for Eli Riser – 11/26/2024 at 10:30 A.M.
November 18, 2024
Thesis Title: Digital Twin Technology for Multi-Robot Navigation using Proximal Policy Optimization-Enhanced RRT*-SMART Algorithm
When: 11/26/2024 10:30 a.m.
Where: Simrall 228 or on Microsoft Teams Join the meeting now Meeting ID: 271 726 555 546 Passcode: rQyWAr
Candidate: Eli Riser
Degree: Master of Science in Electrical & Computer Engineering
Committee Members: Dr. Chaomin Luo, Dr. John E. Ball, Dr. Bryan A. Jones
Abstract:
Digital twin technology can play a significant role in multiple robots’ navigation by providing a virtual representation of the physical environment, robots, and their interactions. This high detail simulation can allow efficient and accurate navigation in difficult scenarios while enabling cost effective robot solutions. In this research a digital twin-based framework is proposed to facilitate multi-robot navigation throughout partially known, static environments, while making use of the strengths of a centralized multi-robot system.
The virtual complement digital twin of a real-world environment is first generated using previously known details such as static obstacles, walls, and passageways. The framework utilizes an improved version of RRT*-SMART for path planning, where Proximal Policy Optimization based reinforcement learning is trained using numerous planning trials of said simulation, slowly updating the algorithm’s parameters to fit the specific environment. During runtime, the digital twin system constantly updates itself in real-time using robot sensor data, allowing a dynamic window approach-based navigation algorithm to path each robot to their respective destinations as well as improving any future path generation. The overall system is validated through the use of both path planning comparison studies as well as a Real-Life simulation study of navigation through a warehouse.