Robot path planning method in complex narrow environment

A path planning and robotics technology, applied in the direction of instruments, vehicle position/route/height control, non-electric variable control, etc., can solve the problems of genetic algorithm's weak ability to adapt to a wide range of environments, difficulty in controlling model evolution speed, and multiple empirical parameters

Active Publication Date: 2019-08-02
DALIAN UNIV
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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

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  • Robot path planning method in complex narrow environment
  • Robot path planning method in complex narrow environment
  • Robot path planning method in complex narrow environment

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[0045] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments: taking this as an example to further describe and illustrate the present application.

[0046] This application uses the RRT-RL-C algorithm and uses the SARSA(λ) method to select nodes with high rewards when expanding nodes. The new node is always in a position close to the target point and has a good performance evaluation, so that the random search tree in each cycle The role is maximized, and the number of branch leaf nodes of the extended tree is small, the number of iterations is small, and the planning path is short, and the optimal or near-optimal solution can be found faster in a complex configuration space. The RRT-Connect tree is randomly expanded in space, so there is randomness in the relative position of the parent node and the child node. However, considering the stability of the robot during the forward process, the RL-RRT...

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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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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0214G05D1/0221G05D1/0276
Inventor 邹启杰刘世慧张跃侯英鹂熊康
Owner DALIAN UNIV
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