2026-09-20T08:53:21Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/122055 2024-03-01 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Amato, Nancy M 2023-12 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms The student, Felipe Arias, accepted the attached license on 2023-12-04 at 14:35. The student, Felipe Arias, submitted this Thesis for approval on 2023-12-04 at 14:46. This Thesis was approved for publication on 2023-12-05 at 09:53. DSpace SAF Submission Ingestion Package generated from Vireo submission #20129 on 2024-03-01 at 13:15:24 Motion pattern prediction in dynamic environments Arias, Felipe Felix Robotics Navigation Motion Planning Dynamic Environments Multi-agent Systems Machine Learning 2023-12-05 Traditionally, motion planning algorithms have tackled the challenge of navigation in dynamic environments by approximating a robot's configuration space through a graph representation. This involves predicting or computing the trajectories of obstacles and finding feasible paths via a pathfinding algorithm. In our work, we strive to enhance the performance of these subproblems by learning to identify regions critical to dynamic environment navigation. We present a novel methodology for constructing sparse probabilistic roadmaps, a two stage approach that combines a self-supervised learning method for recognizing the topology and geometries indicative of motion patterns in dynamic settings, and a sampling-based strategy for leveraging these learned features. The result is the creation of neural networks capable of predicting the probability of occupancy of a given region and Avoidance Critical Probabilistic Roadmaps (ACPRMs), which leverage these insights to significantly improve navigation performance. ACPRMs have shown remarkable performance, demonstrating up to five orders of magnitude improvement over grid-sampling in multi-agent scenarios and surpassing competitive baselines by up to ten orders of magnitude in multi-query situations. Text eng https://hdl.handle.net/2142/122055 Copyright 2023 Felipe Arias M.S. Thesis Computer Science University of Illinois at Urbana-Champaign Computer Science