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ANALYSIS ON PATH OPTIMIZATION OF AGRICULTURAL WAREHOUSE LOGISTICS HANDLING ROBOT BASED ON POTENTIAL FIELD ANT COLONY algorithm

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INMATEH-AGRICULTURAL ENGINEERING 2024年第2期73卷 784-795页

作者： Wang, Yunyun Xie, Mingzhe Wuhan Univ Technol Sch Management Wuhan Hubei Peoples R China Ningbo Univ Technol Sch Econ & Management Ningbo Zhejiang Peoples R China

In the layout of modern agricultural warehouse logistics handling industry, it was an inevitable way to realize industrial upgrading by replacing people with mobile robots. Aiming at the problems that the existing obstacle avoidance control algorithm of agricultural handling robot was easy to fall into local optimal solution, and the operation process of agricultural warehouse logistics handling robot was prone to collision, the obstacle avoidance control of agricultural warehouse logistics handling robot was studied. In addition, a control algorithm based on improved potential field ant colony was proposed. The moving trajectory of the agricultural warehouse logistics handling robot during the handling process was studied, and the spatial kinematics equation of the robot was given. The ant colony algorithm was used to optimize the classical artificial potential field algorithm to improve the global optimization ability and balance the interaction between gravity and repulsion. In the aspect of local area obstacle avoidance of agricultural storage and handling robots, the artificial potential field was optimized twice based on the strategy gradient algorithm. By analyzing the probability of the next action command, the randomness of the travel path selection when multiple robots work at the same time was improved. After testing, the path of the proposed control algorithm was the shortest, and under the condition of complex path planning, the number of collisions between robots was also significantly less than that of the traditional obstacle avoidance control algorithm. The practical application could meet the needs of improving the efficiency of warehouse logistics management.

关键词： Artificial potential field ant colony algorithm warehouse handling robot obstacle avoidance strategy gradient algorithm

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Chinese-English Machine Translation Based on Generative Adversarial Network

IJLAI Transactions on Science and Engineering

引用

IJLAI Transactions on Science and Engineering 2025年第1期3卷 35-44页

作者： Tianxiao Wang College of International Business Shenyang Normal University Shenyang 11034 China

Although Chinese-English machine translation is a resource-rich language pair,the problem of data sparsity still *** example,for the translation of some specific domains or low-frequency words,the amount of parallel corpus is limited,which makes it difficult to improve the translation quality of the model in these *** machine translation models usually need a large amount of alignment data to train,otherwise they are prone to *** machine translation models are slow to train and decode,especially when dealing with long sentences or complex structures,which limits their efficiency in real-time application ***-English machine translation can help people overcome the language barrier and promote the communication and cooperation between people with different language backgrounds,which is of great significance for international business,academic exchanges and cultural *** paper proposes a Chinese-English neural machine translation model based on generative adversarial *** model applies the generative adversarial network to neural machine translation,and further optimizes the adversarial learning based neural machine translation model by improving the monotone decoding sequence from left to right or from right to left in the original machine translation *** the same time,unlike previous generative adversarial networks,neural machine translation models are actually a sequence of discrete symbols that map source language sentences to target language sentences,both in discontinuous *** this case,the generative adversarial network fails to transmit the gradient properly,causing the generator to lose its update *** introducing the strategy gradient algorithm in reinforcement learning,the generator optimization problem in adversarial learning is solved,and the translation performance of the model is ***,experiments on public data sets show that the proposed model can effective

关键词： Chinese-English machine translation Generative adversarial network strategy gradient algorithm Generator optimization

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