基于光学运动捕捉数据的虚拟人下肢运动控制方法

系统仿真学报 ›› 2015, Vol. 27 ›› Issue (2): 327-335.

基于光学运动捕捉数据的虚拟人下肢运动控制方法

梁丰¹, 张志利¹, 李向阳¹, 汤志波¹, 马超²

1.第二炮兵工程大学兵器发射理论与技术国家重点学科实验室,西安 710025;
2.第二炮兵驻519厂军事代表室,长治 047100

收稿日期:2014-01-24 修回日期:2014-06-28 发布日期:2020-09-02
作者简介:梁丰(1987-),男,湖北天门人,博士生,研究方向为导弹发射系统总体与仿真;张志利(1966-),男,河南濮阳人,博士,教授,博导,研究方向为导弹发射系统总体与仿真;李向阳(1984-),男,河南汝州人,博士,讲师,研究方向为导弹发射系统总体与仿真。

Research on Motion Control Technology of Virtual Human’s Lower Limb Based on Optical Motion Capture Data

Liang Feng¹, Zhang Zhili¹, Li Xiangyang¹, Tang Zhibo¹, Ma Chao²

1. State Key Discipline Laboratory of Armament Launch Theory and Technology, The Second Artillery Engineering University, Xi'an 710025, China;
2. The Second Artillery Presentation Office in No 519 Factory, Changzhi 047100, China

Received:2014-01-24 Revised:2014-06-28 Published:2020-09-02

摘要/Abstract

摘要： 由于被动式光学运动捕捉设备只能捕捉人体关键关节的运动信息,介绍了一种简化的虚拟人体骨架结构模型;在此基础上根据光学运动捕捉数据的获取过程,分析了虚拟人体运动数据误差的来源。重点针对由于运动捕捉数据连续性不足造成的虚拟人下肢运动连续性差等问题,为加强对虚拟人运动过程的控制,提出一种对关键关节点运动数据平滑处理,再基于逆向运动学计算下肢运动链其它关节点运动信息的运动控制方法。通过实验和分动箱虚拟维修训练平台证明了该方法的有效性和实用性。

关键词: 运动捕捉设备, 运动控制, 数据平滑处理, 逆向运动学

Abstract: As passive-optical motion capture equipment could only capture the motion information of human’s key joints, a simplified skeleton structure model of virtual human was introduced; and then the error sources of motion data of virtual human were analyzed according to the acquisition process of optical motion capture data based on the skeleton structure model. Focusing on the problem of poor continuity of motion process of virtual human’s lower limb caused by continuity deficiency of motion capture data, a motion control method was proposed to strengthen the control of motion process of virtual human, which included data smoothing of motion process of key joints and the calculation of motion information of other joints in the lower limb kinematic chain based on inverse kinematics. An experiment and a virtual maintenance training platform of a transfer case have proved the validity and practicability of the proposed method.

Key words: motion capture equipment, motion control, data smoothing, inverse kinematics

中图分类号:

TP391.9

梁丰, 张志利, 李向阳, 汤志波, 马超. 基于光学运动捕捉数据的虚拟人下肢运动控制方法[J]. 系统仿真学报, 2015, 27(2): 327-335.

Liang Feng, Zhang Zhili, Li Xiangyang, Tang Zhibo, Ma Chao. Research on Motion Control Technology of Virtual Human’s Lower Limb Based on Optical Motion Capture Data[J]. Journal of System Simulation, 2015, 27(2): 327-335.

参考文献

[1] 姚旭东. 基于运动捕捉数据的虚拟人建模及运动仿真[J]. 计算机仿真, 2010, 27(6): 225-229.
[2] 魏小鹏, 张强, 肖伯祥, 等. 基于模板匹配的人体运动捕捉数据处理方法[J]. 系统仿真学报, 2010, 22(10): 2368-2372.
[3] Andreas Aristidou, Joan Lasenby.Real-time marker prediction and CoR estimation in optical motion capture[J]. The Visual Computer (S0178-2789), 2013, 29(1): 7-26.
[4] Lou H, Chai J.Example-based human motion denoising[J]. IEEE Transactions on Visualization and Computer Graphics (S1077-2626), 2010, 16(5): 870-879.
[5] Alessandra Martins Coelho, Vania Vieira Estrela.Data-driven motion estimation with spatial adaptation[J]. International Journal of Image Processing (S1985-2304), 2012, 6(1): 54-67.
[6] Wu Sheng, Zhang Qiang, Xiao Boxiang, et al. An effective method of capture data processing for optical motion[J]. Journal of Basic Science and Engineering (S1005-0930), 2009, 17(5): 790-798.
[7] 邱世广, 周德吉, 范秀敏, 等. 虚拟操作仿真环境中基于运动捕捉的虚拟人实时控制技术[J]. 计算机集成制造系统, 2013, 19(3): 523-528.
[8] F J Alonso, J M Del Castillo, P Pintado. Motion data processing and wobbling mass modeling in the inverse dynamics of skeletal models[J]. Mechanism and Machine Theory (S0094-114X), 2007, 42: 1153-1169.
[9] Chen Shanmin, Ning Tao, Wang Ke.Motion control of virtual human based on optical motion capture in immersive virtual maintenance system[C]// Proceeding of 2011 International Conference on Virtual Reality and Visualization. Washington, DC, USA: IEEE, 2011: 52-56.
[10] Wang Songshan, Qi Yanqing.A virtual human’s driving method based on motion capture data[J]. Advanced Materials Research (S1022-6680), 2013, 711: 500-505.
[11] Li Xiangyang, Gao Qinhe, Zhang Zhili, et al. Collaborative virtual maintenance training system of complex equipment based on immersive virtual reality environment[J]. Assembly Automation (S0144-5154), 2012, 32(1): 72-85.
[12] 侯永隆, 宁涛, 王可. 基于光学运动捕捉的虚拟人体标定技术[J]. 图学学报, 2013, 34(9): 126-132.
[13] 肖伯祥, 魏小鹏, 张强, 等. 光学运动捕捉散乱数据处理的一种方法[J]. 系统仿真学报, 2008, 20(2): 382-385.
[14] 王军, 胡永刚, 韩崇昭. 被动式光学运动捕捉系统丢点检测与补偿[J]. 系统工程与电子技术, 2012, 34(11): 2374-2378.
[15] 张毅, 张烁, 罗元, 等. 基于Kinect深度图像信息的手势轨迹识别及应用[J]. 计算机应用研究, 2012, 29(9): 3547-3550.
[16] Zong Dan, Li Chunpeng, Xia Shihong, et al. Instruction level motion control of virtual human for maintenance process simulation[C]// Proceedings of 2010 International Conference on Audio Language and Image Processing. Washington, DC, USA: IEEE, 2011: 1289-1294.
[17] 容志能. 惯性运动捕捉系统中传感数据的传输与处理[D]. 杭州: 浙江大学, 2012.
[18] Lin Hongwei.Adaptive data fitting by the progressive-iterative approximation[J]. Computer Aided Geometric Design (S0167-8396), 2012, 29(7): 463-473.
[19] Naturalpoint Corporation. Delivering tracking technology since 1996 [EB/OL]. (2013-12-15) [2015-01-12]. http:// www. naturalpoint. com/ optitrack/ about/, 2013.