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    Hybrid Ant Colony Optimization Method for the Traveling Salesman Problem
    (2024-01-01)
    Janjarassuk, Udom
    The traveling salesman problem (TSP) is a classic problem in computer science and operations research which involves finding the shortest possible route that visits a given set of cities. In this paper, we propose a hybrid algorithm for solving such problem. The algorithm combines the ant colony optimization (ACO) method with the 2-opt heuristic to improve the efficiency for solving the TSP. Instances from the TSPLIB were used to test the algorithm. The results showed that the hybrid ACO algorithm was more effective in solving the TSP as compared to the traditional ACO or the 2-opt heuristic methods.
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    Obstacle Avoidance Path Planning for Apple Picking Robotic Arm Incorporating Artificial Potential Field and A∗Algorithm
    (2023-01-01)
    Zhuang, Min
    ;
    Li, Ge
    ;
    Ding, Kexin
    With the development and maturity of the automated robotics industry, more and more apple plantations are introducing automated picking robotic arms for fruit picking. However, the complex environment in which apple fruit is picked has made it an urgent problem to optimise the robot's picking performance through obstacle avoidance path planning. The experiment selects the Six degrees of freedom manipulator as the research object, and on the basis of its Kinematics analysis, introduces the introduction of artificial potential field (APF) to carry out the path planning of the manipulator. At the same time, it integrates it with A∗ algorithm to jointly achieve the optimization of the parameters of the obstacle avoidance path of the manipulator. In addition, in order to avoid parameter optimization falling into local extremum during the path planning process, the IRRT algorithm is incorporated to re plan the path, improve the smoothness of the path, and finally verify its obstacle avoidance effect through simulation experiments. The results showed that in the convergence comparison, the research method had the minimum loss function value and the stable fitness value as soon as the iteration proceeded to the 50th and 20th generation, respectively. A On the dataset, the research method had the minimum MAPE value when the iteration proceeded to the 45th generation, with a value close to 0. At the same moment, the MAPE values of the IAPF algorithm, the IRRT algorithm and the literature were 0.052%, 0.108% and 0.218%, respectively. In the practical application analysis, when the robot arm starts running in three different starting positions a, b and c, the IRRT algorithm's obstacle avoidance path has a larger arc and tends to reach the target location through a longer path, while the research method tends to find a relatively closer obstacle avoidance path that can be passed smoothly. The above results show that the research method is highly adaptable to robotic arm path avoidance planning and can complete obstacle avoidance path planning faster and more reasonably, providing new technical support for optimising the path planning system of apple picking robots.
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    Performance Evaluation of Pre-Processing and Pre-Treatment Algorithm for Near-Infrared Spectroscopy Signals: Case Study pH of Intact Mango “Arumanis”
    (2022-08-01)
    Agustina, Sri
    ;
    Devianti
    ;
    Bulan, Ramayanty
    ;
    Muslih, Muhamad
    ;
    Sitorus, Agustami
    pH is one of the important physical parameters to characterize mango damage because it can indicate changes in the structure and chemical content of the fruit. Thus, the present work evaluated the possibility of NIRs as a rapid and non-destructive tool for measuring the pH properties of intact mango from the cultivar "Arumanis" (Mangifera indica L.) using several algorithms for pre-processing, pre-treatment, and prediction. Three different algorithm predictions, namely principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR), were used and compared to predict the pH of mangos. A total of 16 pre-processing and pretreatment algorithms are used to support algorithm prediction, and the results are also compared with the raw data spectra. The NIR spectral data used range from 1000 to 2500 nm. Algorithm performance will be evaluated using RMSE, error differences and concluded using RPD. The results show that the prediction of the PLSR algorithm can be performed with an RPD of 8.17, which is more significant than the PCR and SVMR algorithms, which are 1.04, and 1.90, respectively. To support this, pre-processing and pretreatment of the second derivative Savitzky–Golay is the best algorithm that can be used to predict the pH of the whole mango cultivar "Arumanis".