Final Project for CSCI 5611 with Joseph Morelan and Shihao Zhong.
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You control a flock of boids with the mouse position. They need to drink from the blue water, get 'food' from the greyish red circle of food, and 'sleep' on the green grass to stay alive. If any of these parameters drops to 0 the boid dies. Their stats are tracked by each boid color, the more of a resource they have, the more of the associated color they become. When all boids die the game is over.
3D flocking algorithm using Boids simulation model. Each boid is represented as a blue sphere with a smaller green sphere used to visually track the direction vector. The 3 rules used are:
1. boids fly to perceived flock center
2. boids avoid other boids
3. boids match flock direction
Flock center and direction are calculated by looking at each boids' local neighbors, changing the size of this parameter changes the overall flock sizes.
Avoiding other boids uses a 1/x function to determine the multiplier applied to the vector between 2 boids. This means the closer the boids are, the more 'force' repells them. The nearby boids calculation for this rule uses a smaller size than the first and third rules.
In this video we see direction propagation inside a boids flock, as it starts in one end of the formation and propagates throughout. Notice how the boids maintain a respectable distance from each other.
Where it Breaks Down
This video shows 2 boids in direct opposition repelling each other perfecty, as there are no forces to apply variation that would allow them to align.
This video shows the 'strength' of the repelling force, these boids are not perfectly aligned, yet as they get closer the repelling force of rule 2 and the flock direction variable from rule 3 combine to make he boids swerve away from eachother instead of combining.
In addition, the boids jitter a great deal, especially when Rule 2 comes into play. This could be fixed by implementing a maximum turning speed for each boid which should help smooth the way they turn, and implement a better algorithm for dealing with boids intruding too far into each others' space.
Obstacles and Pathfinding
In these videos, black obstacles have been added, along with the a* algorithm from homework 3. To have the boids properly interact with these new objects, 2 new rules are required:
4. boids want to travel to some point on the path
5. boids want to avoid objects
Rule 4 is simply the direction vector pointing to the next point on the path returned by the A* algorithm, and Rule 5 is simply a copy of rule 2, only instead of summing the local boids, uses all objects.
One global path is used for all boids, updated to path to a random point in the obstacle maze whenever the end is reached. This path is shown as a red line, a small red sphere marking the current node the boids are pathing to. Notice how even with these new rules, the boids still travel in flocks.
Where it Breaks Down
You may have noticed in the earlier videos, boids will get stuck on the black obstacles, bouncing back and forth on them, slowly moving around it. This is due to the other Rules upon the boid overpowering Rule 5 until Rule 5 overpowers them, creating a very fast flip-flop effect in the direction vector.
Very occasionally, the point picked randomly by the A* algorithm will situate the boids such that the function used to determine whether the goal is reached will fail to register. This function simply determines if for each boid if the distance from the boid to its perceived flock center is larger than the distance from the current path node to the perceived flock center. This will produce jittery behaviour as the boids try to situate themselves as close as possible to the node wild obeying all the other rules.
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Built a ray tracer for CSCI 5607, supporting depth of field, specularity, circles and triangles, multiple colors, transparency, reflections, refractions, and multiple colored light sources.
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Using Dijkstra's algorithm and Astar with a Probabilistic Road Map to navigate a 3D space. The large green sphere is the goal, the large red sphere is the AI, the large black sphere are obstacles, and the small blue spheres are the points found by the probabilistic road map. Use WASD to move the camera in mode 0, the AI in mode 1, and the goal in mode 2. Drag the mouse to rotate camera. Set modes by pressing the corresponding number. Press enter or return to have the AI search. Press r to reset the AI's position. Press spacebar to swap between using dijkstra's algorithm and astar. In the video, both algorithms find the same path, but Astar ound it in 23 miliseconds, while dijkstra's algorithm found it in 88 milliseconds.
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Using Dijkstra's algorithm and a Probabilistic Road Map to navigate a 3D space. The large green sphere is the goal, the large red sphere is the AI, the large black sphere is an obstacle, and the small green spheres are the points found by the probabilistic road map. Use WASD to move the camera in mode 0, the AI in mode 1, and the goal in mode 2. Drag the mouse to rotate camera. Set modes by pressing the corresponding number. Press enter or return to have the AI search. I accidentally made the obstacle too large, but this does not impact the algorithm much.
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Fireworks simulation as a combination of a moving particle emitter and a radial particle emitter with color changing particles. Audio effects are keyed to play when the firework starts and when it changes from the moving smoke emitter to stationary radial emitter. Turn up volume, sound capture was very quiet.
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Fire simulation very similar to fountain simulation only with altered gravity and initial velocity, and changes color as the particle ages. Particles also fade out as they age.
