By John Fortier
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As could be seen, some virtual human's parameters are totally random while others parameters follow the group trend. All the individual parameters can be changed depending on their relations with the others. Therefore, we have defined: i) A a virtual human from the group GA and ii) l! a virtual human from the group GB. The agents A and l! QA) < Relation (A,QID: if the relationship between A and QA is smaller than between A and QIl. 90: GB. Domination_value from if the domination value of A is greater than all autonomous virtual humans from group QIl, then A will be the new leader of group QIl and the other virtual humans from this group will have a new tendency to follow it.
We can see the agent avoiding collision by changing his angular velocity. Figure 10: Avoid collision sequence Sometimes, however, the virtual human does not have enough time to modify its direction because the collision position is too close to the current position. 1). We do not intend to present in this work a new collision algorithm but just to present some useful considerations for the crowd case. e, no collision avoidance). The following graphic presents the time data for ten autonomous virtual humans which start at an initial aleatory position and must arrive to a specific final position.
The virtual humans walk within the limits of their group in the sociogram. Using the parameters specified in section 3. we have modeled a sociogram including time information which allow us to see changes in crowd behavior during the simulation. In this example. the flocking behavior is represented by walking in a specific region of each group (each circle). The second example is a crowd which visits a museum. In this simple example. the crowd is formed by different groups. In the beginning of the simulation all virtual humans of the same group visit the museum within the time of their group including.
15th Virginia Cavalry by John Fortier