Robot path planning method in complex narrow environment

Abstract

The invention discloses a robot path planning method in a complex narrow environment. An RRT-Connect algorithm is combined with a reinforcement learning algorithm, and certain randomness of a random tree is reserved. According to a degree of obstacle density, an appropriate step size is selected so that a robot can realize rapid and random exploration in an obstacle-dense environment, can quicklypass through in a sparse obstacle environment; orientation of random tree distribution is increased, and a convergence speed is improved; and planning performance can be increased in the process of interaction with the environment so that a planned path is close to an optimal path and a local minimum can be avoided. An improved algorithm is compared with an original standard algorithm, the plannedpath is better and consumed time is less.

Description

technical field [0001] The invention relates to a path planning method, in particular to a path planning method for a robot in a complex and narrow environment. Background technique [0002] Path planning is an important direction in the field of intelligent robot research. Robot path planning is divided into global path planning and local path planning. At present, the commonly used local path planning methods are mainly based on artificial potential field method, grid method, etc., and combined with genetic algorithm, fuzzy logic algorithm, neural network and other intelligent search algorithms to improve search efficiency, system robustness and adaptability. [0003] Fuzzy method is a kind of planning method usually adopted in online planning, including modeling and local planning. But fuzzy logic method fuzzy logic rules need to rely on human experience, which restricts the wide application of this algorithm in the field of robot path planning. [0004] Genetic algorit...

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Problems solved by technology

[0004] Genetic algorithm is an algorithm that simulates the genetic mechanism of nature and the theory of biological evolution to search for the optimal solution. It is widely used in the research of robot path planning, but the ability of genetic algorithm to adapt to a wide range of environments is weak, and the speed of model evolution is difficult to control. , so the algorithm has certain limitations. In addition, the genetic algorithm is easy to fall into the local optimal solution, which leads to the premature phenomenon of the algorithm.

[0005] The neural network method uses a structure similar to the synaptic connection of the brain's neural network to express and process information, but the neural network needs to train a large number of samples in the learning process, and needs to manually set more empirical parameters, so it is applied in the robot There are certain limitations in the field of path planning

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