Charles Doomany- Nest: Generative Tables

by cdoomany @ 2:00 pm 9 May 2011

NEST:

Inspired by genetic inheritance and the selective process of evolution, Nest was conceieved as tool for creating funtional and generative furniture with unexpected variation.

HOW IT WORKS:

1) Using a simulated physics environment, sticks that will later constitute the lower framewok of the table, are dropped and form random configurations (or piles). This stage introduces random variation to the table design.

2) Once the sticks are at rest, a table top is than placed onto the the random configuration.

3) Each of the generated tables from the simlation are then evaluated based on their “fitness” according to established design criteria. The fitness of each table is determined by parameters such as: levelness, height, and the amount of material used.

4) The tables designs that best satisfy the criteria (that are most fit) will survive to be passed onto the next generation of tables; their formal characteristics will be inherited by the successive generation.

5) The resulting output is a population of tables that confrom to the design criteria and exhibit some interesting variation.

 

PROCESS:

above: (a) first version of  the genetic algorithm to determine the optimal solution for a parametrically constructed table, (b) experiments w/ jbullet for collision detection and modeling caltrops, (c) random table configurations produced with updated version of the jbullet simulation, (d) final output from physics simulation

 

CONCLUSION:

Currently I have two versions of the program: one which handles the physics simulation and the other which consists of the genetic algorithm. Ideally these would be integrated so that the 3D configurations could be simulated, evaluated, and then sorted by the algorithm. My main obstacle was working out the simulation component -unfamiliarity w/ Jbullet and its methods (specifically collision detection and creating the appropriate compound shape for the “sticks”), which in turn prevented me from creating the appropriate output for the genetic algorithm. Although I haven’t had the time to work out the simulation yet, I have plans to get the final program up and running soon.

 

 

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