Director of Research (if dissertation) or Advisor (if thesis)
Amato, Nancy M
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Robotics
Navigation
Motion Planning
Dynamic Environments
Multi-agent Systems
Machine Learning
Language
eng
Abstract
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.
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