Boids software flocking

In ant colony optimization , the goal is for ants to explore and find the optimal path s from a central colony to one or more sources of food. As with ants in real life, the simulated ants initially travel in random directions, but return to the colony once a food source is found.

The key in the evolution of the simulation is the use of pheromone trails, which compel other ants to follow them. Pheromone trails evaporate over time, so paths which are shorter end up being traveled more often. This results in a positive feedback mechanism which ensures that the entire group of ants will eventually converge on an optimal path. The problem-solving strategy of the ant colony can be applied to a number of different problems involving searches for optimal paths through graph structures.

For instance, ant colony optimization algorithms are suitable for use in the traveling salesman problem and other similar problems. The neighborhood could be considered a model of limited perception as by fish in murky water but it is probably more correct to think of it as defining the region in which flockmates influence a boids steering.

A slightly more elaborate behavioral model was used in the early experiments. It included predictive obstacle avoidance and goal seeking. Obstacle avoidance allowed the boids to fly through simulated environments while dodging static objects.

For applications in computer animation, a low priority goal seeking behavior caused the flock to follow a scripted path. There was a technical paper on boids published at the same conference. Since there have been many other applications of the boids model in the realm of behavioral animation. The Tim Burton film Batman Returns was the first. It contained computer simulated bat swarms and penguin flocks which were created with modified versions of the original boids software developed at Symbolics.

Andrea Losch then at Boss Films and Paul Ashdown created animation of an "army" of penguins marching through the streets of Gotham City. Then we move on to initializing the simulation with some default global options. These options can be toggled by the user once the simulation is running. In the classic Boids simulations, all boids exhibit the exact same behavior. Most of the attributes are numeric, and different numbers will cause boids to behave differently. Below is a list of the attributes that will vary between each boid instance:.

By the end of lab section in week two you must have:. At least 10 boids flocking. This is enough to show that the algorithm is working, but you do not need to have your code optimized by week two. The parameters should be initialized to the following values: turnfactor : 0. Write a ProtoThreads C program which does the following: At reset, the program spawns as many boids as it can while maintainting 30fps animation rate. The program will use the following parameters for the flock of boids: turnfactor : 0.

The flock will immediately begin behaving according to the new parameters. Write a Python program which does the following: Enables the user to specify values for visualRange , protectedRange , centeringfactor , matchingfactor , and avoidfactor sliders work well for this, but you may do whatever you'd like I recommend that the flock start with the default parameter values given above, and that your interface enables the selection of values within the following ranges for each parameter protectedRange : [0, ] visualRange : [0, ] centeringFactor : [0.

Python interface for flock control. ECE students. In addition to all of the above , ECE students must also do the following: Implement at least one predator, with behavior described in previous parts of this document. Use the following parameter values: predatorturnfactor : 0. An example video of a flock with a predator is shown at the bottom of this page. Your written lab report should include the sections mentioned in the policy page , and: A few cool photographs of your flock A heavily commented listing of your code.

Make a more sophisticated predator. Perhaps it moves toward the center of mass of all boids in its visual range. Or, perhaps it picks a single boid in its visual range and moves toward that boid that seems like more realistic behavior to me, but who knows.

The boids will, of course, avoid the predator. Add perching behavior. Draw a background on the TFT a tree branch, a simple ledge, whatever. If a boid gets near that object, have it pause on it for a certain amount of time, and then take off again. Add gravity.

Birds tend to move more quickly when diving than when ascending. Incorporate that into your flock. Add a moving food source or sources that the boids track. And below is a demo video that includes a predator.