2026-09-20T10:30:30Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/130069 2026-02-10 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01 The student, Haoyuan You, accepted the attached license on 2025-07-24 at 20:31. The student, Haoyuan You, submitted this Thesis for approval on 2025-07-24 at 20:41. This Thesis was approved for publication on 2025-07-25 at 09:27. DSpace SAF Submission Ingestion Package generated from Vireo submission #22732 on 2025-10-21 at 10:06:21 Subdimensional expansion method for multi-agent path finding with long narrow corridors You, Haoyuan 2025-07-25 Driggs-Campbell, Katie Multi-agent Path Finding Robotics eng Multi-agent path finding, a problem largely related to the field of robotics, has been proven to be an NP-Hard problem and computationally heavy. Experience-based planning method is a sample-based method that uses a pre-computed database to reduce the planning time. We propose an algorithm that extends based on a prior experience-based framework to target specifically multi-agent path planning problems with long narrow corridors. By leveraging graph homomorphism, we expand a database for doorway problems to cover corridors with various shapes and sizes. The algorithm can find solutions for MAPF problems with a consistent 60-70% higher success rate compared to multiple baselines in environments with multiple narrow corridors without sacrificing performance. The proposed algorithm shows the ability to plan the paths for about a hundred robots in congested simulated environments with and without narrow corridors within a few seconds. 2025-08 Text https://hdl.handle.net/2142/130069 Copyright 2025 Haoyuan You Electrical & Computer Eng Electrical & Computer Engr University of Illinois Urbana-Champaign M.S. Thesis