Monte Carlo Analysis of Boid Simulations with Obstacles: A Physics-Based Perspective

TL;DR

This paper extends the classic Boids model by incorporating obstacle avoidance rules and analyzes flocking behavior using Monte Carlo simulations from a physics perspective, providing insights into natural flock formation.

Contribution

Introduces two new rules for the Boids model to simulate obstacle avoidance and applies Monte Carlo analysis to study flocking behavior from a physics standpoint.

Findings

Flocking behavior can be explained through physics-based metrics.

Obstacle avoidance rules improve realism in flock simulations.

Monte Carlo analysis reveals why animals prefer flocking over solitary movement.

Abstract

Boids, developed by Craig W. Reynolds in 1986, is one of the earliest emergent models where the global pattern emerges from the interaction between many individuals within the local scale. In the original model, Boids follow three rules: separation, alignment, and cohesion; which allow them to move around and create a flock without intention in the empty environment. In the real world, however, the Boids' movement also faces obstacles preventing the flock's direction. In this project, I propose two new simple rules of the Boids model to represent the more realistic movement in nature and analyze the model from the physics perspective using the Monte Carlo method. From those results, the physics metrics related to the forming of the flocking phenomenon show that it is reasonable to explain why birds or fishes prefer to move in a flock, rather than sole movement.