![]() Here, I simply compute the three rules for a particular agent, and add them to the velocity. Var separation = computeSeparation(agent) Ī += alignment.x + cohesion.x + separation.x Ī += alignment.y + cohesion.y + separation.y The simplest way to do this is as follows: var alignment = computeAlignment(agent) Once these three rules have been implemented, they need to come together. The computed vector needs to be negated in order for the agent to steer away from its neighbors properly. The computation vector is divided by the corresponding neighbor count, but before normalizing, there is one more crucial step involved. When a neighboring agent is found, the distance from the agent to the neighbor is added to the computation vector. The implementation of separation is very similar to that of alignment and cohesion, so I'll only point out what is different. Separation is the behavior that causes an agent to steer away from all of its neighbors. if (neighborCount = 0)įinally, we divide the computation vector by the neighbor count and normalize it (divide it by its length to get a vector of length 1), obtaining the final resultant vector. If no neighbors were found, we simply return the zero vector (the default value of the computation vector). If an agent is found within the radius, its velocity is added to the computation vector, and the neighbor count is incremented. With our variables initialized, we now iterate through all of the agents and find the ones within the neighbor radius - that is, those close enough to be considered neighbors of the specified agent. We'll need two variables: one for storing the vector we'll compute, and another for keeping track of the number of neighbors of the agent. public function computeAlignment(myAgent:Agent):Point Image adapted from Craig Reynolds' articleĪlignment is a behavior that causes a particular agent to line up with agents close by.įirst, we'll make a function that takes an agent and returns a velocity vector. Feel free to download the source if you wish to learn more. The full source code for this demo can be downloaded here, so this article will only highlight the most important aspects of the implementation. ![]() Resultant: The vector obtained from the calculations of the rule.Neighborhood: A certain area around the agent, used to look for other agents.Velocity vector: An agent's current velocity.Before we begin, here's some terminology I'll be using: In this tutorial, I will cover the three main rules used to simulate flocking and explain how to implement each one. Note: Although this tutorial is written using Flash and AS3, you should be able to use the same techniques and concepts in almost any game development environment. This is known as emergent behavior, and can be used in games to simulate chaotic or life-like group movement. Using computers, these patterns can be simulated by creating simple rules and combining them. This phenomenon, also known as flocking, occurs at both microscopic scales (bacteria) and macroscopic scales (fish). ![]() In the natural world, organisms exhibit certain behaviors when traveling in groups.
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