Ant colony-clustering algorithm-based self-adaptive dynamic path planning method of robot

A dynamic path, clustering algorithm technology, applied in the direction of instruments, non-electric variable control, two-dimensional position/channel control, etc.

Active Publication Date: 2019-01-08
KUNMING UNIV OF SCI & TECH +1
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the traditional ant colony algorithm does not comprehensively consider the problem of dynamic path planning in the case of limited search range, high real-time requirements and limited computing power of mobile robots.

Method used

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  • Ant colony-clustering algorithm-based self-adaptive dynamic path planning method of robot
  • Ant colony-clustering algorithm-based self-adaptive dynamic path planning method of robot
  • Ant colony-clustering algorithm-based self-adaptive dynamic path planning method of robot

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Embodiment 1

[0065] Embodiment 1: as figure 1 As shown, a robot adaptive dynamic path planning method based on ant colony-clustering algorithm, the specific steps are:

[0066] (1) Using the grid method to model the working environment of the robot: the movement area of ​​the mobile robot in the two-dimensional plane is denoted as G, the lower left corner of the movement area G is the coordinate origin, the horizontal axis is the X axis, and the vertical axis is Y The axis establishes a rectangular coordinate system. Suppose there are several obstacle grids in the motion area G. The obstacle grids are represented by black grids, and the free grids are represented by white grids. The grids are coded by serial number representation. The grid is marked as 0, the obstacle grid is marked as 1, the side length of each grid is marked as a, the maximum grid value in the abscissa and ordinate is MM, and the total number of grids is e=MM·MM. From left to right and from bottom to top, the grids are ...

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Abstract

The invention relates to an ant colony-clustering algorithm-based self-adaptive dynamic path planning method of a robot, and belongs to the technical field of an intelligent algorithm of the robot. The ant colony-clustering algorithm-based self-adaptive dynamic path planning method comprises the steps of performing environment modeling by a grid method; determining a searching radius upper bound of local dynamic path planning according to planning real-time requirement; determining a searching radius value of local dynamic path planning by employing a selection rule of radius searching and bytaking a current position of a mobile robot as a current position; calling a random roulette method to determine an optimal local target point of local dynamic path planning; calling an ant colony algorithm to plan the local optimization path; calculating two norm of the optimal local target point and a preset terminal, and taking the optimal local target point as a global target point if the twonorm is zero; and repeating if the two norm is not zero. By the ant colony-clustering algorithm-based self-adaptive dynamic path planning method, the appropriate searching radius can be automaticallyselected according to the obstacle distribution condition, the path dynamic planning is completed, and favorable environment adaptability and relatively good comprehensive path optimization performance are achieved.

Description

technical field [0001] The invention relates to a robot adaptive dynamic path planning method based on an ant colony-clustering algorithm, and belongs to the technical field of robot intelligent algorithms. Background technique [0002] Path planning refers to searching for an optimal or suboptimal path from the starting position to the target position by the mobile robot according to certain performance indicators (such as distance, time, etc.) in a known or positional environment with obstacles. Considering that the autonomous navigation of mobile robots in dynamic and complex environments has great application value in the field of unmanned driving, it is imperative to study path planning problems in dynamic and complex environments. [0003] The ant colony algorithm converges on the optimal path through the accumulation and update of pheromones, and has distributed global search capabilities. However, due to the lack of pheromones in the early stage of the solution, the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/02
CPCG05D1/0221G05D1/0276
Inventor 杨春曦刘新宇赵峰谭力铭陈飞朱强
Owner KUNMING UNIV OF SCI & TECH
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