3 minute read
The Flocking Algorithm
Flocking is the natural behaviour visible when a group of agents, called a flock, are foraging or in flight.
The set of rules that stand behind this behaviour can also generally be applied to the “flocking” behaviour of other species. As a result, the term “flocking” is sometimes applied, in computer science, to species other than birds.
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It is considered an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination.
In the next pages the main rules we learned to control and customise during the workshop are summarised
(our customised code lines in purple)
2D Studies
Base Rules
Workshop II Workshop II
Our experimentation in search for emergent patterns starts from basic geometrical concepts
The simple starting shape is tested together with additional perceptive analogs in order to understand how different starting position can show similar trajectories
Dec 2022
Our experimentation in search for emergent patterns starts from basic geometrical concepts
The simple starting shape is tested together with additional perceptive analogs in order to understand how different starting position can show similar trajectories
Our experimentation in search for emergent patterns starts from basic geometrical concepts
The simple starting shape is tested together with additional perceptive analogs in order to understand how different starting position can show similar trajectories
Clearer central symmetries in the patterns, compared to triangular starting point
Populating on broader areas creates more branching
Additional effect of the bouncing to create
Additional population in the center not affected by repellers – can provide additional strenght to the trail
Workshop II
Workshop II
Parameter Study
Agents Pinball
Directionality
and Repeller sets Combinig lessons learned from pattern, directionality and parameters studies.
Combinig lessons learned from pattern, directionality and parameters studies.
At the end of the 2D experimentation a system of gradients has also been explored, speculating on a subsequent possible outcome in the 3D space
3D Studies
Test C - Acceleration
Repeller
Attractor
Eroding Behaviours
Adding parameters to Individual agents instead of global behavior_Acceleration Having agents with different speed can create layering effect or spear -shaped form. Velocity Vector multiplied by Acceleration Vector ’s magnitude
Variables: Neighbor Boundary
Constant Speed Behavior
Flock behavior with one-direction velocity
Constant Acceleration Behavior
Constant Speed Behavior
Flock behavior with one-direction velocity
Adding Acceleration, updating the speed
Constant Acceleration Behavior
Adding Acceleration, updating the speed
Adding Acceleration (local) and observing the change of pattern and behavior (Global)
A color range indicating the agent’s speed based on position. Blue indicating slower agents and Red indicating the faster.
Eroding Behaviours
Different group sizes as Starting points
Four groups in different size accelerated according to flock size.
Local Rule; Acceleration Force
Coordinates as Starting points
Two planes moving with delay creating intertwined pattern.
Neighbor-based Acceleration
Maximum Range of Neighbors Applied
Maximum Range of Neighbors Applied
Waves and Branches
The Smaller the group (fewer Neighbors) the Faster. Having a min and max range, creating multiple waves based on the number of neighbors.
Eroding Behaviours
Branching from Within
The Larger the group (more Neighbors) the Faster. The corners with fewer neighbors and the center with more, causing different speeds in one flock.
Local Rule; Acceleration Force
- Vertical Flock - Horizontal Flock
Privious 2D experiments with range affecting the flow and pattern.
Phase of Branching
Showing effects on the Final pattern and behavior
Segmenting the environment into pieces with different acceleration vectors based on distance.
Local Rule; Acceleration Force
Dividing the tunnel into three segments with different acceleration.
Pink Planes representing increase in acceleration and white decrease in acceleration.
The chosen 2D pattern from previous experiments based on branching capacity and diversity and rearranging it in the 3D environment.
Horizontal Flow in Grid-Based Extruded Frame
Vertical Flow in Grid-Based Extruded Frame
Horizontal Flow in Grid-Based Extruded Wide Tunnel
Vertical Flow in Grid-Based Extruded Surface
Difining the boundries; Adding a pattern of attractors and repellers based on previous 2D experiments in Multiple layers with grid setting to various environments to analys the behaviour of the flock.
3D Boundaries Categorization
Less control in case of directionality and observing similar patterns to 2D environments
Approaching the tested volume in a vertical manner and observing new patterns and behaviors. The flock’s reaction to dense attractor and repeller pattern prevents vast branching through the volume.
Rule; Acceleration Force
The Vein Less Separated Centralized Branching
The Lines Horizontal Alignment of the branches.
The Table 5-Point Concentrated Branching
The Multiple Hives Having Cohesive Flocks
Patterns in various depths of the volume from surface to the bottom are emerged according to a change of strength while a vertical wave of agents (the flock) face the setting of attractors and repellers and dodge in the redefined boundries.
The Sandbox Local Rule; Acceleration Force
- Vertical Flock
Dodged & Emerged
Flock high seperation resulting a pattern emerged from individual behaviour of agents.
Behavior of Two Various sizes of groups resulting in mixture of patterns in a single flock.
Symmetrical Behavior
Similar to 2D pattern experiments, the flock creating curves avoiding the center.
Branching through Flock’s decision to gather in large groups to avoid obstacles.
Patterns emerged from the dodging behaviour of agents in wider environment for horizontally moving flock. Multiple forms of branching with various initial decision of the flock.
The Sandbox Local Rule; Acceleration Force
- Horizontal Flock
Final experiments on weak boundaries
