Soccer robot GGRRT path planning method

A football robot and path planning technology, applied in the direction of navigation calculation tools, etc., can solve the problems of prolonging the time of path search planning, the purpose of searching is not strong, and not having the strength to win, so as to shorten the planning time and improve the path. Search efficiency and avoid blindness

Active Publication Date: 2018-11-23
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

However, the actual results of the game prove that applying the traditional RRT algorithm to the robot soccer game, although it can also ensure the normal and smooth progress of the game, it does not have the strength to win at all.
What is the reason? Because the real-time, accuracy and efficiency of path planning are the core and key of the soccer robot, and the randomness of RRT sampling determines that the robot search process is blind, and the random tree will expand many nodes that are not related

Method used

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  • Soccer robot GGRRT path planning method
  • Soccer robot GGRRT path planning method
  • Soccer robot GGRRT path planning method

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Experimental program
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Embodiment

[0046] In the first step, the robot's current position coordinates are (nearestNode.posX, nearestNode.posY), and the node is used as the parent node to calculate the coordinates of its child nodes (tempNode.posX, tempNode.posY). Methods as below:

[0047] tempNode.posX=nearestNode.posX+(rrtStepSize*cosθ)+(kgoal*cosα)

[0048] tempNode.posY=nearestNode.posY+(rrtStepSize*sinθ)+(kgoal*sinα)

[0049] Among them, rrtStepSize represents the step size of the robot's movement. In the experiment, rrtStepSize=3, kgoal represents the target gravitational coefficient, in the experiment, kgoal=1, θ represents the angle between the connection line between the random node and the parent node and the x-axis, and α represents the target The angle between the connection line between the node and the parent node and the x-axis, the coordinates of the random node are (rand()%150+1, rand()%150+1).

[0050] The second step is to judge the validity of the child nodes (tempNode.posX, tempNode.posY)...

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Abstract

The invention discloses a soccer robot GGRRT path planning method, aiming to improve the search efficiency of soccer robots and allow the robots to react quickly and take the initiative in the game. Agoal orientation function is introduced on the basis of an original RRT algorithm, so that a growth function is no longer determined by a stochastic growth function alone, but is determined by the stochastic growth function and the goal orientation function together, which is equivalent to increasing the attractiveness of a target point to a research object, and effectively guiding a random treeto grow toward the target direction. Experimental results show that the route planning carried out with the GGRRT algorithm proposed by the invention consumes only 1/12 to 5/6 of the time cost by theoriginal RRT algorithm, and redundant branches are also greatly reduced, thereby effectively solving the problem of blind search of the soccer robot and greatly improving the efficiency of route planning.

Description

technical field [0001] The invention belongs to an optimization method for robot path planning, and introduces a goal-guided function on the basis of a Rapidly-exploring Random Trees (RRT) algorithm to form a Goal Guide Rapidly-exploring Random Trees (Goal Guide Rapidly-exploring Random Trees) , referred to as GGRRT) algorithm, this algorithm can effectively avoid the blind search of the robot, greatly reduce the search time, is suitable for dynamic and arbitrary complexity application scenarios, and can play an important role in robot soccer games. Background technique [0002] In recent years, with the rapid development of artificial intelligence, robots have once again received unprecedented attention. The football game represented by NAO robots has become a hot sport in which countries compete for the first place, and has also become an important indicator to measure the level of intelligence. In July 2007, NAO was selected as the standard platform by the RoboCup Organiz...

Claims

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Application Information

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IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 阮晓钢周静朱晓庆张晶晶王飞董鹏飞
Owner BEIJING UNIV OF TECH
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