A least-squares estimation approach to improving the precision of inverse dynamics computations
A least-squares estimation approach to improving the precision of inverse dynamics computations
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DOI：
10.1115/1.2834295
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发表时间：
1998-02-01
影响因子：
1.7
通讯作者：
Kuo, AD
中科院分区：
文献类型：
--
作者：
Kuo, AD
提出了一种计算逆动力学的最小二乘方法。该方法利用包含地面反力和力矩项的多节段物体的运动方程。所得到的系统在每个时间点都被过度确定，因为运动学和力测量的数量多于未知扭矩，并且可以使用加权最小二乘法来求解，以得到与测量数据最匹配的关节扭矩和关节角加速度的估计值。通过误差分析，可以预测BASH常规方法和最小二乘法的误差大小。对该方法的修改还可以抑制恒定偏差，例如由于力盘和运动测量参照系不对准而引起的偏差。给出了一个基准算例，与传统的牛顿-欧拉法相比，在测量数据的大范围噪声水平下，关节扭矩误差减少了30%左右。与牛顿-欧拉法相比，牛顿-欧拉法的优势包括：充分利用所有可用的测量，在测量到的地面反作用力不足时仍能正常工作，抑制作用于最大身体部分的残余扭矩，以及排除数据中的恒定偏差。
A least-squares approach to computing inverse dynamics is proposed. The method utilizes equations of motion for a multi-segment body incorporating terms for ground reaction forces and torques. The resulting system is overdetermined at each point in time, because kinematic and force measurements outnumber unknown torques, and may be solved using weighted least squares to yield estimates of the joint torques and joint angular accelerations that best match measured data. An error analysis makes it possible to predict error magnitudes for bath conventional and least-squares methods. A modification of the method also makes it possible to reject constant biases such as those arising from misalignment of force plate and kinematic measurement reference frames. A benchmark case is presented, which demonstrates reductions in joint torque errors on the order of 30 percent compared to the conventional Newton-Euler method for a wide range of noise levels on measured data. The advantages over the Newton-Euler method include making best Else of all available measurements, ability to function when less than a full complement of ground reaction forces is measured, suppression of residual torques acting on the rep-most body segment, and the rejection of constant biases in data.