scholarly journals Object Pose Estimation for Robot Loading in Accommodation Space Using Alpha-Shape Algorithm

2021 ◽  
Author(s):  
Qingda Guo ◽  
Lulu Tang ◽  
Jianchi Zhang
Keyword(s):  
Pose Estimation ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Alpha Shape ◽  
Accommodation Space ◽  
Bin Picking ◽  
Object Pose Estimation ◽  
The Common ◽  
Setting Parameters

Abstract Robots with visual sensors have been used in various goods logistics, such as bin picking or uploading. However, there are more and more demands for the automatic blanking and loading, it is necessary to solve the problem of object pose estimation in changing accommodation space. This paper proposes a method for pose estimation in the accommodation space using alpha-shape algorithm and improved Fruit fly Optimization Algorithm (FOA). The alpha-shape volume variety of object and measured space is set to the objective function and the pose variety of object is set to six variables of improved FOA. The experiments were performed by setting parameters of improved FOA and considering the four space types represented the common accommodation shapes. Compared with previous work using convex hull, the new study using alpha-shape algorithm not only keeps the object in the accommodation space, but also maintains the object pose is at the bottom of the space and can meet the practical requirement of object placement by robot arms.

Research on Enhancement Technology of Conveyor Belt Fault Images Based on Fruit Fly Optimization Algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 685-689
Author(s):  
Chunqing Ye ◽  
Changyun Miao ◽  
Xianguo Li ◽  
Yanli Yang
Keyword(s):  
Optimization Algorithm ◽  
Detection System ◽  
Fruit Fly ◽  
Conveyor Belt ◽  
Recognition Algorithm ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Wire Ropes ◽  
Fault Recognition ◽  
Steel Cord

In this research, we studied the fault recognition algorithm of steel cord conveyor belt, and obtained the wire ropes image by adopting the detection system of steel cord conveyor belt, so that the fault recognition algorithm of steel cord conveyor belt was proposed based on Fruit fly optimization algorithm. As we know that the fruit fly optimization algorithm is used for fault detection of the processing steel cord conveyor belt image and for obtaining the fault image. In the MATLAB environment, the algorithm process was designed and verified in terms of the effectiveness and accuracy. The experimental results show that with fast speed and high accuracy in detecting the fault image of steel cord conveyor belt rapidly and accurately, and in classifying scratch from fracture the proposed algorithm is suitable for the fault recognition of steel cord conveyor belt automatically.

scholarly journals A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Mina Salehi ◽  
Siamak Farhadi ◽  
Ahmad Moieni ◽  
Naser Safaie ◽  
Mohsen Hesami
Keyword(s):  
Neural Network ◽  
Fruit Fly ◽  
Corylus Avellana ◽  
General Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Harvesting Time ◽  
Fruit Fly Optimization ◽  
General Regression ◽  
Concentration Levels

Abstract Background Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. Results GRNN-FOA models (0.88–0.97) showed the superior prediction performances as compared to regression models (0.57–0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l−1). Conclusions Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

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