Robust Agent Collision Processing in StarLogo Nova
Name
bridgeman-kabridge-meng-eecs-2026-thesis.pdf
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3.65 MB
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Adobe PDF
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Author(s)
Bridgeman, Kailey
Advisor(s)
Klopfer, Eric
Date Issued
February 2026
Publisher
Massachusetts Institute of Technology
Abstract
StarLogo Nova (SLN) is an agent-based programming environment that allows users to simulate complex physical systems. While SLN works as a powerful educational tool in exploring physical systems, it is unable to reliably model collisions between agents. SLN’s current collision processing relies on overlap detection between agents at discrete time steps within a discrete space binning system. This setup means that some collisions, particularly those involving fast moving agents or multiple agents, are not processed in a way that appears physically accurate when rendered. We design a novel collision processing system which utilizes the existing discretization of time and space along with continuous collision detection techniques to improve the accuracy of collisions in SLN. We integrate the new hybrid collision processing algorithm into SLN to investigate its performance against the existing collision processing algorithm.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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