Synthesizing Close Combat Using Sequential Monte Carlo
| Autor: | I Chiang, 蔣易 |
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| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 This thesis presents a method to synthesize close combat using sequential Monte Carlo. We perform physics-based simulation without motion capture data. The behavior of the character can be classified into attack and defense and is formulated as an objective function. In the attack mode, character aims to hit the critical body regions of the opponent. On the other hand, the main goal of defense is to block the opponent’s blow in order to prevent injuries to critical regions. These principles are designed according to fundamental theory of Chinese martial arts. We use a kd-tree sequential Monte Carlo sampler to find the optimal joint angle trajectories for each character. Each sample in our system contains a coordinate and an objective function value. For each iteration, pruning is performed to keep a few samples with the highest objective function values. Then, a kd-tree is constructed based on those remaining samples. Next, adaptive important sampling is applied to draw new samples from the old ones. Subsequently, we will feed the new generated sample into physics engine to get positions, velocities and contacts of rigid bodies. Finally, by using the information from the physics simulation, we are able to make evaluation on the objective function to determine the scores of the new samples. These samples are added to the kd-tree until a budget is reached, then we will repeat the above steps. Through the evolution, our results can show some simple attack, defense and interaction movements. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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