Robot path planning method based on local optimal convolution evaluation

A path planning, local optimization technology, applied in the field of robotics and navigation, can solve the problems of difficult practical application, poor robustness, low operation efficiency, etc., to achieve good engineering value and significance, improve efficiency, and low sensor requirements. Effect

Pending Publication Date: 2021-06-11
NANJING NORMAL UNIVERSITY
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Problems solved by technology

[0005] Purpose of the invention: In order to overcome the deficiencies in the existing technology, provide a robot path planning method based on local optimal convolution evaluation to solve the problem of low efficiency and robustness of the existing method for path planning of mobile robots in unknown environments Poor performance and difficult to put into practical application

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  • Robot path planning method based on local optimal convolution evaluation
  • Robot path planning method based on local optimal convolution evaluation
  • Robot path planning method based on local optimal convolution evaluation

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[0147] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these embodiments are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention Modifications in equivalent forms all fall within the scope defined by the appended claims of this application.

[0148] The present invention provides a robot path planning method based on local optimal convolution evaluation, such as figure 1 shown, including the following steps:

[0149] Step 1: Establish a two-dimensional grid map of the working environment of the mobile robot, and determine the location of the starting point and the target point.

[0150] Step 2: The mobile robot perceives the environmental information of the adjacent nodes, and screens out feasible moving dir...

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Abstract

The invention discloses a robot path planning method based on local optimal convolution evaluation. The method comprises the steps of building a two-dimensional grid map of a working environment through a mobile robot, and determining the positions of a starting point and a target point; enabling the mobile robot to sense environment information around the current position, and screening out a feasible moving direction from the candidate moving directions; calculating the projection of a unit vector in each feasible moving direction in the negative gradient direction of the target point distance function, and obtaining a local optimal moving direction; checking the rationality of the local optimal moving direction by using a convolution evaluation index; and when the feasible path cannot be found in the primary path planning, performing secondary path planning, and canceling a link of checking the rationality of the local optimal moving direction. According to the method, the path planning of the mobile robot in an unknown environment can be realized, the decision-making thought conforms to the actual operation characteristics of the mobile robot, and the method has the advantages of small operand, high ergodicity and high operation efficiency.

Description

technical field [0001] The invention belongs to the technical field of robotics and navigation, and in particular relates to a robot path planning method based on local optimal convolution evaluation. Background technique [0002] The problem of path planning for mobile robots is a difficult point that must be overcome when robotics is applied in many fields such as firefighting and disaster relief, medical services, and industrial inspections. At present, the methods widely used in path planning of mobile robots mainly include A* algorithm, ant colony algorithm, artificial potential field method and so on. The A* algorithm is based on the fact that the robot has a global environment map. If it is directly applied to an unknown environment, the planning efficiency will be extremely low due to the jump of the path in the planning process; the ant colony algorithm needs to generate a large number of virtual individuals to map the map. Exploration, to a certain extent, is only...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/02
CPCG05D1/0221G05D1/0223G05D1/0276
Inventor 谢非吴奕之吴启宇杨继全刘宗熙李群召刘丛昊张瑞凌旭
Owner NANJING NORMAL UNIVERSITY
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