Data glove based on hand motion model for mine equipment training
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摘要: 矿山设备实训采用虚拟现实技术互动操作真实性较差,而现有微惯性传感器式数据手套通过优化数据处理算法以减少传感器布置数量导致准确性不高。针对上述问题,在介绍人类手部结构和手部运动力学模型的基础上,分析了矿山设备实训基本动作及运动特点,提出了二指六连杆七自由度矿山设备实训手部运动模型,从而可减少微惯性传感器布置数量;基于四元数法推导了指骨位置及姿态解算算法;开发了基于矿山设备实训手部运动模型的数据手套。在手套腕部固定Tracker追踪器来获得前臂绝对坐标,在手套大拇指和食指处布置微惯性传感器以提供指骨载体坐标,并通过指骨位置及姿态解算获得指骨在导航坐标系的位置及姿态,从而实现虚拟场景手部运动姿态实时重生成。测试结果表明,矿山设备实训基本动作的最大误差为9 mm,小于控件最小直径,满足矿山设备实训操作需要。Abstract: Authenticity of interactive operation is poor when virtual reality technology adopted in mine equipment training. Data processing algorithm is optimized to reduce the number of micro-inertial sensors for data glove but results in low accuracy. According to the above problems, on the basis of introducing human hand structure and hand motion mechanics model, basic movement and motion characteristics of mine equipment training were analyzed, and hand motion model for mine equipment training of two-finger, six-link and seven-degree of freedom was put forward, so as to reduce the number of micro-inertial sensors. Position and attitude calculation algorithm of phalanges was deduced based on quaternion method. A data glove based on the hand motion model for mine equipment training was developed. Tracker is fixed at wrist of the glove to obtain absolute coordinates of forearm, micro-inertial sensors are arranged at thumb and forefinger of the glove to provide carrier coordinates of phalanges, and position and attitude of phalanges in navigation coordinate system are calculated through calculation of position and attitude of phalanges, so as to realize real-time generation of hand motion attitude in virtual scene. The test results show that the maximum error of basic movements of mine equipment training is 9 mm, which is smaller than the minimum diameter of control, and meets needs of mine equipment training operation.
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