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Multi-robot task allocation method based on multi-objective optimization

A multi-objective optimization and multi-robot technology, applied in the field of multi-robot task assignment based on multi-objective optimization, can solve problems such as the difficulty of taking into account both time consumption and energy consumption, and increased energy consumption costs

Active Publication Date: 2015-11-18
ZHAOQING UNIV
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Problems solved by technology

[0004] However, time consumption cost and energy consumption cost are usually two incomparable and conflicting (conflicting) aspects in the process of multi-robot performing tasks, that is, it is difficult to take into account time consumption and energy consumption at the same time, that is, multi-robots complete If the time cost of a certain task is relatively small, it may cause an increase in energy consumption

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  • Multi-robot task allocation method based on multi-objective optimization
  • Multi-robot task allocation method based on multi-objective optimization
  • Multi-robot task allocation method based on multi-objective optimization

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Embodiment 1

[0073] In this embodiment, the cooperation between fire-fighting robots in the rescue system under fixed weight is taken as an example to describe the following.

[0074] In this embodiment, RoboCup Rescue Simulation System V0.50 (RoboCupRescueSimulationSystem, RCRSSV0.50) is selected as the simulation experiment platform, and the program code of the GDUT_Tiji team of Guangdong University of Technology in the RoboCup Rescue Simulation Group of the RoboCup2010 China Open is used as the basic framework. The task allocation strategy adopts the multi-robot task allocation method based on multi-objective optimization proposed by the present invention respectively. In this embodiment, the kMC-RCRSTD method is used to decompose the total system tasks, and the following assumptions are made for the subsequent fire-fighting task assignment operations:

[0075] (1) If the work task requires, each fire-fighting robot can move from its initial position along a straight line to any sub-fir...

Embodiment 2

[0098] In this embodiment, the cooperation between fire-fighting robots in the rescue system under fixed weights and setting ω 1 =0.75, ω 2 =0.25 as an example to explain the following.

[0099] Usually, in the robot rescue simulation system, the performance of the fire-fighting robot to complete the task can be evaluated by formula (4). Among them, P represents the number of surviving citizen Agents, S represents the sum of life values ​​of surviving citizen Agents, and S int Represents the sum of the health values ​​of all citizen Agents at the beginning, B represents the total area of ​​buildings that have not been burned (remaining buildings), B int Represents the total area of ​​the building at the beginning.

[0100] V = ( P + S S int ) × B B ...

Embodiment 3

[0114] In this embodiment, the cooperation between fire-fighting robots in the rescue system under variable weights is taken as an example to describe the following.

[0115] In this embodiment, under the premise of obtaining the same robot time utility value matrix U and robot energy utility value matrix V as in Embodiment 1, a multi-objective optimization-based multi-robot task assignment method (MOO-MRTA method) is further used to adjust variable weights The following multi-robot task allocation scheme is solved, and the optimization of the task allocation scheme also uses the improved particle swarm optimization algorithm (MPSO). In this example, ω 1 , ω 2 The change step size of these two utility weights is set to 0.02, that is, ω 1 Decrease from 1 to 0 in steps, ω 2 Increment from 0 to 1 synchronously in steps.

[0116] Such as figure 1 As shown, the multi-robot task assignment method based on multi-objective optimization in the present invention is to use weighted ...

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Abstract

The present invention provides a multi-robot task allocation method based on multi-objective optimization. A weighted summation mode is adopted to carry out modeling on a time utility objective and an energy utility objective and the time utility objective and the energy utility objective are optimized so as to implement multi-robot task allocation; energy utility is used for evaluating energy consumption of robots in the task completing process; and time utility is used for evaluating time consumption of the robots in the task completing process. According to the method, an evaluation mechanism of two factors, i.e. the time consumption and the energy consumption, can be effectively shown in the multi-robot task completing process, and thus, computing time is short and the optimal multi-robot task allocation scheme set can be rapidly obtained, so that task allocation time is greatly reduced and task completing efficiency is improved. According to the present invention, the problem of task allocation in a multi-robot system is more comprehensively and more systematically solved, a task allocation result quality quantitative evaluation index based on the mechanism is increased and improvement on scientificity and reasonability of a task allocation result is realized.

Description

technical field [0001] The invention relates to the technical field of multi-robot collaborative control, and more specifically, relates to a multi-robot task assignment method based on multi-objective optimization. Background technique [0002] Using multi-robot cooperation to complete various application problems such as environmental exploration, rescue and handling has many advantages compared with single robots: the fault tolerance of multi-robot systems can effectively prevent task failure caused by the failure of a single robot; the parallel processing capabilities of multi-robot systems can Complete specific tasks in a short time; the functions between multiple robots can complement each other to achieve functions that cannot be achieved by a single robot, etc. Therefore, at this stage, multi-robot coordination and cooperation to complete predetermined tasks has become a research hotspot in this field, and task allocation is one of the core issues in the research of ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06
CPCY02P90/82
Inventor 陈建平欧阳思洁李坚肖奇军
Owner ZHAOQING UNIV
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