|Published (Last):||9 December 2013|
|PDF File Size:||9.10 Mb|
|ePub File Size:||12.29 Mb|
|Price:||Free* [*Free Regsitration Required]|
If group members need to be aware of other members and their surroundings, the most appropriate model for simulating the movement is a model of flocking behaviour. Such simulations save a lot of time at the expense of precise control over individual group members. To achieve the desired movement of the group, flocking simulation settings are iteratively changed until a satisfactory result is achieved. This thesis tests and evaluates the use of genetic algorithms for selecting the optimal parameters of simulated flocking.
The aim was to simulate group movement without collisions between group members or with the surroundings to get an authentic animation of group members' behaviour. A programme for flocking simulations was implemented and tested on a short scenario. A group of objects scattered across a designated starting area had to pass an obstacle course to reach a final destination. The programme was run at different settings, and collisions and frame calculation times were recorded.
The results show that the programme was able to set optimal parameters for flocking simulations in the scenario. Measurements show that groups of up to members can be simulated without collisions using the programme. Additionally, the use of a genetic algorithm reduced the time needed to calculate the simulation.
Voting is allowed only to logged in users.
Genetski algoritmi za raspore đ ivanje rukovatelja gra đ evinskih strojeva