A hybrid improved moth-flame optimization with differential evolution with global and local neighborhoods algorithm for pose optimization on a space manipulator. (19th September 2019)

• Record Type: Journal Article
• Authors: Li, Yaru; Wang, Zhongyu; Cheng, Yinbao; Tang, Yingqi; Shang, Zhendong
• Is Part Of: Measurement science & technology. Volume 30:Number 12(2019:Dec.)
• Publication Date: 2019-09-19
• Subjects: space manipulator -- pose optimization -- heuristic algorithm -- moth-flame optimization -- differential evolution algorithm; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ab2fa6
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: To improve the location accuracy of the vision system of a space manipulator, a new hybrid improved moth-flame optimization based on a differential evolution with global and local neighborhoods algorithm (HIMD) is proposed to optimize the pose of a target relative to a camera. Firstly, the non-linear optimization model is established according to the imaging rule and space geometry transformation principle of the vision system. Secondly, the initial population of pose parameters is generated by the moth-flame optimization (MFO) algorithm, and the population is updated by the improved MFO (IMFO). Finally, the new population is crossed, mutated and selected by the differential evolution with global and local neighborhoods (DEGL) algorithm, the population is iterated and updated continuously and the optimum pose can be obtained. The proposed algorithm is applied to the precision test in the measurement system of a space manipulator. The experimental results show that the average synthetic errors are 2.67mm for chaotic harmony search algorithm (CHS), 1.80mm for differential evolution with particle swarm optimization (DEPSO), 2.94 mm for the particle swarm optimization and gravitational search algorithm (PSOGSA), 2.13 mm for the DEGL algorithm, 2.56 mm for the MFO algorithm and 0.53 mm for the HIMD algorithm. This means that the accuracy of the HIMD algorithm is about four times higher than that of the MFO, PSOGSA and CHS algorithm and about three times higher than that of the DEGL and DEPSO algorithms. Therefore, the HIMD algorithm is superior to the other five algorithms for the non-linear optimization model of the pose.