flocks herds and schools a distributed behavioral model

sum ( squared_displacements, 0 ) alert_distance = 100 far_away = square_distances > alert_distance separations_if_close = np. mean ( positions, 1 ) direction_to_middle = positions - middle velocities -= direction_to_middle * move_to_middle_strength separations = positions - positions squared_displacements = separations * separations square_distances = np.

We’ll build this up in NumPy notation, and eventually, have an animated simulation of our flying boids.ĭef update_boids ( positions, velocities ): move_to_middle_strength = 0.01 middle = np. Our boids will each have an x velocity and a y velocity, and an x position and a y position. Flock Centering: attempt to stay close to nearby flockmates.Velocity Matching: attempt to match velocity with nearby flockmates.Collision Avoidance: avoid collisions with nearby flockmates.Reynolds, “Flocks, Herds, and Schools: A Distributed Behavioral Model”, Computer Graphics 21 4 1987, pp 25-34 See the original paper Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it… The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds. The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world… The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock the birds choose their own course.

Flocks herds and schools a distributed behavioral model