Multi-robot task allocation and path planning method

A task allocation and multi-robot technology, applied in the field of multi-robot path planning of immune genetic algorithm, can solve the problem of too large search space of genetic algorithm

Inactive Publication Date: 2017-09-15
SHENYANG POLYTECHNIC UNIV
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Problems solved by technology

And on the basis of previous work, it has fully proved its feasibility and its many advantages, but it still has the disadvantages of local optimum and too large search space of genetic algorithm.

Method used

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  • Multi-robot task allocation and path planning method
  • Multi-robot task allocation and path planning method

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Embodiment Construction

[0098] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0099] The invention is a multi-robot task assignment and path planning method. In a multi-robot system, tasks are first assigned to each robot according to a certain strategy, and then each robot performs path planning for the assigned tasks. Specifically include the following steps:

[0100] Step 1: Acquisition of robot data information, such as robot speed, power, maximum working time, maximum acceptable number of tasks, and the location of task points. as attached figure 2 As shown in the figure, the results of six randomly generated robots and data of 200 task points after task assignment are shown.

[0101] Step 2: Multi-robot task allocation: Calculate the comprehensive cost data between each robot and task, comprehensively consider factors such as robot power, working time, and the maximum number of tasks, and assign tasks to robots...

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Abstract

The invention relates to a multi-robot task allocation and path planning method. The method comprises: step one, information of multiple robots and tasks is initialized; to be specific, initial state parameters of robots and task information in an environment are obtained; and positions, speeds, maximum power of the robots and task locations are obtained; step two, multi-robot task allocation is carried out; to be specific, comprehensive cost data between all robots and tasks are calculated, factors including the power, working time, and maximum task numbers of the robots are considered, and tasks are allocated to the robots based on a criterion of comprehensive cost minimization; and step three, a robot path planning method is executed; to be specific, path planning of robots is carried out by using an improved immune genetic algorithm. With the method provided by the invention, multiple tasks can be distributed to multiple robots effectively; and the improved immune genetic algorithm is implemented during the robot path planning process to reduce the iteration frequency of the algorithm, thereby improving the searching efficiency of the globally optimal solution of the immune genetic algorithm and obtaining a short optimal solution.

Description

technical field [0001] The invention belongs to the technical field of multi-robots, and relates to a multi-robot task distribution and path planning method, in particular to a multi-robot path planning method based on an improved immune genetic algorithm. Background technique [0002] With the rapid advancement of robot technology, robots have been widely used in the fields of industry, military, service industry, and dangerous environment exploration; with the continuous improvement of the requirements for robots in various fields, the tasks of robots are becoming more and more complicated. It is difficult for a single robot to meet the current needs in terms of data acquisition and data processing capabilities. Compared with single robots, multi-robots have many advantages, such as multi-robot data collection information and sharing of processing results, and robots cooperate to complete complex tasks. However, the research on multi-robot environment exploration strategi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 段勇王宇喻祥尤
Owner SHENYANG POLYTECHNIC UNIV
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