Multi robot path planning method based on multi-target artificial bee colony algorithm

An artificial bee colony algorithm and path planning technology, applied in the direction of two-dimensional position/channel control, etc., can solve the problems that cannot meet the needs of robot path planning, path length, path safety and smoothness conflict, and it is difficult to achieve simultaneous optimization And other issues

Inactive Publication Date: 2015-07-29
SHANGHAI UNIV
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Problems solved by technology

However, in actual path planning problems, the path length, path security and smoothness are generally in conflict with each other, and it is difficult to achieve simultaneo

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  • Multi robot path planning method based on multi-target artificial bee colony algorithm
  • Multi robot path planning method based on multi-target artificial bee colony algorithm
  • Multi robot path planning method based on multi-target artificial bee colony algorithm

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Embodiment Construction

[0050] The present invention will be further described below in conjunction with the accompanying drawings.

[0051] A kind of multi-robot path planning method based on the multi-objective artificial bee colony algorithm proposed by the present invention, such as figure 1 As shown, it specifically includes the following optimization steps:

[0052] Step 1: Environment Modeling

[0053] 1. Environmental modeling for path planning problems:

[0054] Such as figure 2 As shown, the path planning environment is set to a two-dimensional plane, and the global coordinate system O-XY of the environment map is established; Start is the starting point of the robot, and Target is the target point of the robot. The path of the robot can be expressed in the environment map as a set of starting point, target point and n path points passed in the middle: Path={Start,Step 1 ,Step 2 ,...,Step n ,Target}. Among them, set P={Step 1 ,Step 2 ,...,Step n} is the optimization goal of path ...

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Abstract

The invention provides a multi robot path planning method based on a multi-target artificial bee colony algorithm and belongs to the technical field of path planning. The method includes path planning problem environment modeling, multi-target artificial bee colony algorithm parameter initialization, three-variety bee iteration optimization path and non-inferior solution determination, good path reservation by sequencing and optimum path set outputting. By means of the method, the standard artificial bee colony algorithm is improved based on the concept of non-domination sequence of Pareto domination and crowd distance, and the multi-target artificial bee colony algorithm applicable to solving the multi-target optimization problem is provided. In the path planning process, multiple performance indexes of path length, smoothness and safety are considered in the algorithm, and a group of Pareto optimum paths can be acquired through one-step path planning. The path planning method belongs to meta-heuristic intelligent optimization methods, is different from the traditional single-target path planning method, and can well adapt to path planning tasks in complex environment.

Description

technical field [0001] The invention relates to the technical field of path planning, in particular to a multi-robot path planning method based on a multi-objective artificial bee colony algorithm. Background technique [0002] Since the birth of robots, great changes have taken place in the way of life and production in human society. With the development of the demand for robots, people realize that when completing a complex task, compared with designing a single robot with complex functions, designing multiple robots with simple functions has many advantages in cost, efficiency, and robustness. Compared with a single robot, a multi-robot system has the advantages of higher efficiency, lower cost, high flexibility, and strong robustness. In multi-robot systems, mobile multi-robot systems are an important research direction. Designing mobile multi-robot systems with autonomous navigation is one of the key research issues. As the core of autonomous navigation technology, ...

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Application Information

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IPC IPC(8): G05D1/02
Inventor 李敏窦连航李洋
Owner SHANGHAI UNIV
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