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Robot sensor calibration via neural network and particle swarm optimization enhanced with crossover and mutation

机译：通过神经网络和粒子群优化对机器人传感器进行标定，并具有交叉和变异功能

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摘要

In order to determine the position and orientation of an object in the wrist frame for robot, transform relation of hand-eye system should be estimated, which is described as rotational matrix and translational vector. A new approach integrating neural network and particle swarm optimization algorithm with crossover and mutation operation for robot sense calibration is proposed. First the neural network with rotational weight matrix is structured, where the weights are the elements of rotational part of homogeneous transform of the hand-eye system. Then the particle swarm optimization algorithm is integrated into the solving program, where the inertia weight factor and mutation probability are tuned self-adaptively according to the motion trajectory of particles in longitudinal direction and lateral direction. When the termination criterion is satisfied, the rotational matrix is obtained from the neural network’s stable weights. Then the translational vector is solved, so the position and orientation of camera frame with respect to wrist frame is achieved. The proposed approach provides a new scheme for robot sense calibration with self-adaptive technique, which guarantees the orthogonality of solved rotational components of the homogeneous transform.
机译：为了确定机器人手腕框架中对象的位置和方向，应估计手眼系统的变换关系，将其描述为旋转矩阵和平移矢量。提出了一种结合神经网络和粒子群优化算法与交叉变异操作相结合的机器人感知标定的新方法。首先，构造具有旋转权重矩阵的神经网络，其中权重是手眼系统的均匀变换的旋转部分的元素。然后将粒子群优化算法集成到求解程序中，根据粒子在纵向和横向的运动轨迹对惯性权重因子和突变概率进行自适应调整。满足终止条件后，将从神经网络的稳定权重中获得旋转矩阵。然后求解平移向量，从而获得相机框架相对于腕部框架的位置和方向。所提出的方法为采用自适应技术的机器人感应校准提供了一种新方案，该方案保证了均匀变换的已求解旋转分量的正交性。

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