Optimizing Indoor Navigation Policies For Spatial Distancing

• URL: http://arxiv.org/abs/2207.08860v1
• Date: Sat, 4 Jun 2022 21:57:22 GMT
• Title: Optimizing Indoor Navigation Policies For Spatial Distancing
• Authors: Xun Zhang, Mathew Schwartz, Muhammad Usman, Petros Faloutsos, Mubbasir Kapadia
• Abstract summary: In this paper, we focus on the modification of policies that can lead to movement patterns and directional guidance of occupants. We show that within our framework, the simulation-optimization process can help to improve spatial distancing between agents.
• Score: 8.635212273689273
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we focus on the modification of policies that can lead to movement patterns and directional guidance of occupants, which are represented as agents in a 3D simulation engine. We demonstrate an optimization method that improves a spatial distancing metric by modifying the navigation graph by introducing a measure of spatial distancing of agents as a function of agent density (i.e., occupancy). Our optimization framework utilizes such metrics as the target function, using a hybrid approach of combining genetic algorithm and simulated annealing. We show that within our framework, the simulation-optimization process can help to improve spatial distancing between agents by optimizing the navigation policies for a given indoor environment.

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