A new optimization particle filtering navigation location method for aquatic plants cleaning workboat in crab farming. (16th July 2018)

• Record Type: Journal Article
• Authors: Ruan, Chengzhi; Zhao, Dean; Ding, Shihong; Sun, Yueping; Rao, Jinhui; Liu, Xiaoyang; Jia, Weikuan
• Is Part Of: International journal of advanced robotic systems. Volume 15:Number 4(2018:Jul./Aug.)
• Publication Date: 2018-07-16
• Subjects: Crab farming -- aquatic plants cleaning workboat -- visual navigation -- particle filter -- generalized regression neural network -- fruit fly optimization algorithm; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418787911
• Languages: English
• ISSNs: 1729-8806

Abstract

Chinese river crabs are important aquatic products in China, and the accurate operation of aquatic plants cleaning workboat is an urgent need for solving various problems in the aquaculture process. In order to achieve the accurate navigation positioning, this article introduces the visual-aided navigation system and combines the advantages of particle filter in nonlinear and non-Gaussian systems. Meanwhile, the generalized regression neural network is used to adjust the particle weights so that the samples are closer to the posterior density, thus avoiding the phenomenon of particle degradation and keeping the diversity of particles. In order to improve the network performance, the fruit fly optimization algorithm is introduced to adjust the smoothing factor of transfer function for the generalized regression neural network model layer. On this basis, the location filtering navigation method based on fruit fly optimization algorithm-generalized regression neural network-particle filter is proposed. According to the simulation results, the meanR of root-mean-square error of the proposed fruit fly optimization algorithm-generalized regression neural network- particle filter method decreases by 12.39% and 6.87%, respectively, compared with those of particle filter and generalized regression neural network methods, and the meanT of running time decreases by 16.04% and 9.14%, respectively. From the repeated experiments on the aquatic plants cleaning workboat in crab ponds, the latitude error of the proposed method decreases by 23.45% and 12.68%, respectively, and that the longitude error decreases by 29.11% and 17.65%, respectively, compared with those of particle filter and generalized regression neural network methods. It is proved that our proposed method can effectively improve the navigation positioning accuracy of aquatic plants cleaning workboat.