Uncertainty control of head shaking for virtual human based on cloud model
基于云模型的虚拟人摇头动作不确定性控制

Currently, certainty methods are adopted to synthesize body actions of virtual human or humanoid robot. However, the process people expressing deep information such as emotion, motivation, and mental state, etc. through body languages, has uncertainty characteristic of fuzziness and randomness. Cloud theory can integrate the two uncertainties. Taking the example of expressing negative information with head shaking of virtual human, three cloud models are designed, which are the maximal head shaking angle negative cloud, the head shaking times negative cloud, and the head shaking speed negative cloud. Their drops are generated by respective positive cloud generation algorithm, to obtain maximal head shaking angle, head shaking times, and head shaking speed. The three control parameters distribute normally around their respective expected value. Results of simulation experiment show that the proposed cloud-based head shaking algorithm can generate control curves of head shaking action serials with disciplinary differences. Feature values of the three kinds of cloud models are proposed.