Optimizing Indoor Navigation Policies For Spatial Distancing

TL;DR

This paper presents an optimization framework combining genetic algorithms and simulated annealing to improve indoor navigation policies, enhancing spatial distancing among agents in a simulated environment.

Contribution

It introduces a novel hybrid optimization method to modify navigation policies for better spatial distancing in indoor environments.

Findings

Improved spatial distancing metrics achieved in simulations.

Effective modification of navigation graphs using the proposed method.

Potential applications in designing safer indoor spaces.

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.