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Multi-source and multi-objective path planning based on genetic optimized long short-term memory neural network model

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Abstract

Robot technology includes many fields such as machinery manufacturing, sensor application and recognition, electronic technology, automation, and artificial intelligence. In recent years, with the development of automation technology and artificial intelligence, robotic technology has evolved significantly. According to various applications, robots are divided into industrial robots, agricultural robots, home robots, and medical robots. With the continuous development and diffusion of robotic technology, there is a growing demand for innovative robotic road design. Compared with multi-object road design, multi-object road design can have an overview of various factors such as distance, safety, and quietness. This is why the design of multi-lane roads is more in line with the current situation. The traditional local path planning method has some shortcomings, such as local optimum, poor real-time performance, and high requirements for the moving speed of obstacles. This paper proposes corresponding solutions for the problems that cannot be solved well in common path planning algorithms. For algorithm-based research on local path planning method, the paper introduces the establishment of local path planning environment model and puts forward the basic framework of path planning. By optimizing the genetic algorithm, this paper obtains the optimal topology and optimal weight of the network and combines the genetic algorithm with the long short-term memory neural network. The experimental results show that the time required for multi-objective path planning by the method in this paper is 12.5 s, 11.7 s, 12.2 s and 14.9 s, respectively. Compared with the existing methods, the time required is significantly reduced, and the method in this paper improves the efficiency of path planning.

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Authors and Affiliations

  1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 43000, Hubei, China

    Junlong Su & Congshi Jiang

  2. School of Computer Science and Software, Zhaoqing University, Zhaoqing, 526000, Guangdong, China

    Yihong Li

Authors
  1. Junlong Su
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  2. Congshi Jiang
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  3. Yihong Li
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All authors contribute equally.

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Correspondence to Junlong Su.

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Su, J., Jiang, C. & Li, Y. Multi-source and multi-objective path planning based on genetic optimized long short-term memory neural network model. Int J Adv Manuf Technol (2022). https://doi.org/10.1007/s00170-022-10046-0

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