TY - JOUR AB - Purpose The purpose of this paper is to propose cuckoo optimization algorithm (COA)-based back propagation neural network (BPNN) to reduce the effect of the nonlinearities presented in laser triangulation displacement sensors. The 3D positioning and posture sensor allows access to the third dimension through depth measurement; the performance of the sensor varies according to the level of nonlinearities presented in the system, which leads to inaccuracies in measurement.Design/methodology/approach While applying optimization approach, the mathematical model and the relationship between the key parameters in the laser triangulation ranging and the indexes of the measuring system were analyzed.Findings Based on the performance of the parametric optimization method, the measurement repeatability reached 0.5 µm with an STD value within 0.17 µm, an expanded uncertainty of measurement was within 5 µm, the angle error variation of the object’s rotational plane was within 0.031 degrees and nonlinearity was recorded within 0.006 per cent in a full scale. The proposed approach reduced the effect of the nonlinearity presented in the sensor. Thus, the accuracy and speed of the sensor were greatly increased. The specifications of the optimized sensor meet the requirements for high-accuracy devices and allow wide range of industrial application.Originality/value In this paper, COA-based BPNN is proposed for laser triangulation displacement sensor optimization, on the basis of the mathematical model, clarifying the working space and working angle on the measurement system. VL - 40 IS - 1 SN - 0260-2288 DO - 10.1108/SR-03-2019-0085 UR - https://doi.org/10.1108/SR-03-2019-0085 AU - Selami Yassine AU - Lv Na AU - Tao Wei AU - Yang Hongwei AU - Zhao Hui PY - 2020 Y1 - 2020/01/01 TI - Optimizing laser triangulation displacement sensor of 3D positioning and posture using COA Based BPNN T2 - Sensor Review PB - Emerald Publishing Limited SP - 112 EP - 120 Y2 - 2024/03/28 ER -