Robot path planning method based on improved ant colony algorithm

Abstract

The invention discloses a robot path planning method based on an improved ant colony algorithm. The method comprises the following steps: introducing a pre-planned path; adding inflection point parameters; and building a deadlock prohibition list. The method improves searching efficiency and effectively avoids the deadlock problem.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a robot path planning method based on an improved ant colony algorithm. Background technique [0002] With the continuous development of human society and the continuous expansion of living space, mobile robots are more and more widely used in national defense, earthquake relief, disaster prevention and relief, anti-terrorism, modern military weapons, manufacturing and daily life. Dynamic routing for more efficient planning. With the rapid development of mobile robot technology and the continuous expansion of the application range, people put forward higher requirements for the performance of robots in all aspects. Through unremitting efforts, scientists have proposed a variety of path planning algorithms for mobile robots, such as the traditional grid method and artificial potential field method. The grid method is generally used for global planning, but when t...

AI Technical Summary

Problems solved by technology

The grid method is generally used for global planning, but when the complexity of the environment increases, the required storage space also increases, resulting in low search efficiency; the artificial potential field method is the most commonly used method in robot path planning. It is suitable for local path planning, but the problem of local minimum points and unreachable targets has always been the weakness of this algorithm

With the application of robots in more and more fields, the traditional path planning algorithm based on mathematical models has been difficult to adapt to complex and changeable environments, and the optimization effect is not satisfactory

With the development of artificial intelligence, intelligent algorithms such as genetic algorithm, neural network algorithm, and simulated annealing algorithm are more and more used in robot path planning, although these intelligent algorithms have achieved some satisfactory results in the field of robot path planning. However, problems such as low search efficiency and easy to fall into local optimum have not been fundamentally solved.

[0007] The existing technology only improves the traditional ant colony algorithm through the path planning method, which is not enough to fully improve the search efficiency and effectively avoid the "deadlock" problem

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