Abstract
The study of mobile robots, which began in the late 1960s, is the most dramatic development in human history in the twentieth century, and the invention has undergone radical changes in just over 50 years. The robot body is developing in the direction of flexibility and miniaturization. This is because the robot application is mostly oriented to the family and service industries, and it needs to adapt to a more complex environment. This manuscript aims to improve further the ant colony optimization algorithm by using rough set theory to improve the convergence speed and accuracy of the algorithm in robot path planning on the basis of an in-depth diagnosis on the shortcomings and its causes of development of the ant colony optimization algorithm. It overcomes the drawbacks of the algorithm that easily get trapped in partial optimality solution, the search time is much slower and the search effect is not good. In this paper, the CMA-ES algorithm, the modified ant colony, and the BK method are proposed, which have high theoretical value and exploration significance. In addition, simulation experiments are conducted to obtain the stage results on the basis of artificial information. The results of the present paper indicate the GWO algorithm performs more stable in the optimization results of each experiment when there are 14 robots and the communication range is 1.6, compared with the PSO algorithm and BA algorithm.
Highlights
Path planning is the core of mobile robot research [1–3]. is research direction has a wide range of implementation prospects and has attracted great attention
Intelligent robots can be more widely used in the real environment, and appropriate design technology can be used as an opportunity to carry out a higher level of robot research
It can be found that the basic ant colony algorithm is prone to falling into local traps in the middle of the path search process. e reason for this is usually due to the influence of objective heuristic functions in the path search process by ants, which prematurely focuses most of the v (V)
Summary
People knew the word “robot” in the early 1920s and gave it a rich meaning. Since the birth of industrial robots in the 1960s, and the continuous expansion and expansion of application fields, widely used in industry, agriculture, military, medical and service industries. e requirements for intelligent robots in various fields have become higher and higher. Since the birth of industrial robots in the 1960s, and the continuous expansion and expansion of application fields, widely used in industry, agriculture, military, medical and service industries. E requirements for intelligent robots in various fields have become higher and higher. Whether autonomous mobile robots can achieve intelligence to a certain extent is one of the main research contents of this subject. Path planning is the core of mobile robot research [1–3]. After entering the new century, the practicability, ease of use, and technical performance of mobile robots have reached a higher level. Intelligent robots can be more widely used in the real environment, and appropriate design technology can be used as an opportunity to carry out a higher level of robot research
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