引用本文:魏 毅,夏时洪,王兆其.基于物理的人体空中运动仿真.软件学报,2008,19(12):3228-3236
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基于物理的人体空中运动仿真
魏 毅1,2, 夏时洪1, 王兆其1
1.中国科学院 计算技术研究所 智能信息处理重点实验室,北京 100190;2.中国科学院 研究生院,北京 100049
摘要:
根据给定的处于关键时刻的人体关键姿态,运用优化方法生成满足牛顿力学的人体运动是人体运动仿真研究的重要问题.由于牛顿力学的强非线性和数值优化方法只能找到局部最优解,将牛顿力学约束直接作为约束条件的优化模型在实践中的收敛性不好.通过将牛顿力学约束转化为目标函数,同时增加对关键时刻的优化,提高了模型的收敛性,使其不依赖于被仿真运动的类型、人体质量参数和关键时刻准确程度等因素.仿真出来的人体运动尽可能地满足了牛顿力学.通过仿真7类复杂的蹦床运动验证了新模型的有效性,同时还将该模型用于人体受力分析和体育运动的新动作设计.
关键词:  关键帧插值  四元数  时空约束  非线性约束优化
DOI:
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基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60533070, 60603082 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant Nos.2006AA01Z336, 2007AA01Z320 (国家高技术研究发展计划(863)); the National Basic Re
Physics-Based Simulation of Human Motion in Flight
WEI Yi,XIA Shi-Hong,WANG Zhao-Qi
Abstract:
According to the given keyframes and associated timing, how to simulate the human motion that conforms to Newton’s law by optimization is an important problem. The convergence property of current optimization models where the constraint of Newton’s law is regarded as the direct constraint condition is not satisfactory in practice. There are 2 reasons for that. The first is that the nonlinearity of Newton’s law is strong. The second is that the current optimization methods can only find local minimizers. By converting the constraint of Newton’s law into the objective function and adding the optimization of given timing, the convergence property of which is better than that of the current optimization models and is independent of the type of simulated motion, the error of mass parameters of human body and the error of the given timing. The simulated human motion can conform to Newton’s law as much as possible. The efficacy of the new model is validated by simulating seven types of somersaults on the trampoline. Furthermore, this model has been applied to the analysis of forces acting on human body and the design of new motions in sports.
Key words:  interpolation between keyframes  quaternion  spacetime constraint  nonlinear constrained optimization