Previsualization

Latona allows choreographers and displacement artists to create, view and modify displacement patterns before they are implemented in the real world. This provides a huge increase in productivity and savings of costly rehearsals space and the time and energies of the dancers or devices. Because patterns can be modeled so efficiently, more interesting and complex patterns can be implemented. New unprecedented art can be created.

Monte Carlo Simulations and Hierarchical Fail-up

Latona algorthms can generate movement through random processes. Sometimes these processes run into problems. Maybe all the tracks ended in the corner and cannot move as described in the next phrase of the music. As a result, we created a hierarchal structure for trying new things within limits and failing up to previous versions of the run.  Building on this, we also created a system for optimization where we define criteria we are looking for in the pattern. If we have a successful run, this run is ranked in terms of the criteria we are targeting. The longer we let the algorthmm run, the better is our target value. The better the target value, the more interesting the pattern is likely to be artistically.  As an example, we have a optimation for X and Y axis crosses. We can optimize so that patterns are generated that maximize the number of times the audience see object dancers cross each other in the site plain.

Latona algorthms can generate movement through random processes. Sometimes these processes run into problems. Maybe all the tracks ended in the corner and cannot move as described in the next phrase of the music. As a result, we created a hierarchal structure for trying new things within limits and failing up to previous versions of the run.

Building on this, we also created a system for optimization where we define criteria we are looking for in the pattern. If we have a successful run, this run is ranked in terms of the criteria we are targeting. The longer we let the algorthmm run, the better is our target value. The better the target value, the more interesting the pattern is likely to be artistically.

As an example, we have a optimation for X and Y axis crosses. We can optimize so that patterns are generated that maximize the number of times the audience see object dancers cross each other in the site plain.

Collision Detection And Correction

Latona accurately considers the space an object is consuming during the dance. These considerations include the object’s dimensions, rates of acceleration, velocity limits and its orientation (state of rotation) at every millisecond. Without accurate consideration for these factors, accurate collision detection is not possible.

Latona has techniques for avoiding and correcting for collisions. This video shows some of these techniques.

Latona has algorithms for selecting the best method for the situation or offering the variations as parameters for the artist.