Task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm

A multi-robot and task distribution technology, applied in the direction of instruments, motor vehicles, non-electric variable control, etc., can solve problems such as the difficulty of optimal distribution of multi-robot systems

Inactive Publication Date: 2016-08-10
SHANGHAI UNIV
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AI Technical Summary

Problems solved by technology

The prerequisite for maximizing the performance of a multi-robot system is optimal task allocation. However, in practice, the computational complexity of the optimal allocation algorithm increases exponentially with the problem size. W

Method used

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  • Task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm
  • Task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm
  • Task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm

Examples

Experimental program
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Effect test

Embodiment 1

[0045] Embodiment one: see figure 1 , a multi-robot cooperative round-up task assignment method based on dynamic adjustment of task intensity, which specifically includes the following steps: 1) group search and round-up modeling, 2) task assignment strategy.

Embodiment 2

[0046] Embodiment two: this example is basically the same as embodiment one, and the special features are as follows:

[0047] Step 1) Group search and round-up modeling is:

[0048] (1) Group search method:

[0049] The group search strategy adopted is the multi-robot roaming method, that is, the round-up robot walks randomly in the multi-robot collaborative system environment; the multi-robots using the roaming method do not communicate before finding the target robot to reduce the communication burden. ;When a hunting robot finds a target robot, the hunting robot switches to the role of auction robot according to the algorithm, evaluates the hunting task of the target robot, creates a temporary auction market, releases the task of rounding up the target robot and hunts down the target Robot; the siege robot that receives the information becomes a bidding robot and evaluates the task to decide whether to bid for the siege task;

[0050] (2) Roundup modeling:

[0051] Such...

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Abstract

The invention provides a task intensity dynamic adjustment based multiple robots cooperating task hunting allocation algorithm, which belongs to the technical field of task allocation. The method comprises group searching, hunting model building and task allocation strategies. According to the invention, the conception of hunting experience obtained from an intensified learning method is introduced into a task allocation algorithm; the method can make dynamic adjustments to an initial task allocation scheme obtained from an auction algorithm so that the method can better adapt to a dynamic changing hunting environment and reduce the communication quantity and calculation quantity among systems. In the task allocation auction algorithm, the solutions for the cost function for a bidding robot are optimized, and the conception of task intensity is raised. As a result, task allocation efficiency for a multiple robots cooperating system is improved while cost is reduced.

Description

technical field [0001] The invention relates to the field of task allocation, in particular to a multi-robot cooperative capture task allocation method based on dynamic adjustment of task intensity. Background technique [0002] Since the birth of robots, great changes have taken place in the way of life and production in human society. As a very important branch of distributed artificial intelligence, multi-robot system has the characteristics of fault tolerance, strong robustness, distributed coordination and so on. Task allocation is an important basis for effectively utilizing the resources of a multi-robot system to give full play to the advantages of system performance. The prerequisite for maximizing the performance of a multi-robot system is optimal task allocation. However, in practice, the computational complexity of the optimal allocation algorithm increases exponentially with the problem size. When there is significant dynamic uncertainty in task allocation, the...

Claims

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0221G05D2201/0217
Inventor 李敏王忠亚李杰
Owner SHANGHAI UNIV
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