粒子群算法在多机器人任务分配问题的应用

粒子群算法在多机器人任务分配问题的应用(任务书,开题报告,论文18000字)
摘  要
多机器人系统是构建机器人工作自主化、产能高效化的高新科技生态的重要组成部分，同时也是当今学术界研究的热门问题。多机器人任务分配是多机器人系统的关键技术，因此，针对多机器人系统任务分配的优化也成为该领域研究的热点之一。然而，复杂任务下的多机器人任务分配问题已被证明是一个非确定性多项式时间困难(Non-deterministic Polynomial Hard, NP)问题。该问题的这种特性给其求解带来了巨大挑战，也体现了多机器人任务分配问题对于一个高效求解方法的迫切需求。因此，找到一个同时具有时效性，稳定性的高性能算法成为提升移动机器人自主化的重中之重。为此，本文中粒子群算法(Particle Swarm Optimization, PSO)将作为基本优化工具，围绕多机器人任务分配问题的优化求解展开研究。完成的主要工作总结如下：
（1）完成了多机器人任务分配问题建模。在建立多机器人任务分配数学模型过程中，综合考虑到机器人的价值、机器人执行任务的成功率、任务的价值和任务对机器人的干扰率，并且通过引入二元决策变量，最终建立了多机器人任务分配的数学模型。
（2）应用PSO算法对建立的任务分配模型进行求解。在求解过程中，针对标准PSO算法不能很好平衡其全局和局部搜索能力的缺陷，设计了一种线性参数更新法则对PSO算法中的三个主要控制参数进行更新。
（3）PSO算法在多机器人任务分配中的仿真验证。在完成前两个基本研究内容的基础上，本文最后给出了PSO算法在多机器人任务分配问题MATLAB仿真验证。仿真结果表明该算法在求解多机器人任务分配问题上具有较好适应性。   
关键词：粒子群算法；优化算法；多机器人任务分配
 
Abstract
Multi-robot system is an important part of the construction of high and new technology ecology with autonomous robot work and high productivity. Multi-robot task assignment is the key technology of multi-robot system, so the optimization of multi-robot task assignment has become one of the research hotspots in this field. However, under the complex task of multi-robot task allocation problem has been proved to be a non-deterministic Polynomial time difficult (Non - deterministic Polynomial Hard, NP) problem. This characteristic of the problem brings great challenge to its solution and also reflects the urgent need of multi-robot task assignment problem for an efficient solution method. Therefore, finding a high performance algorithm with timeliness and stability at the same time has become the most important task to improve the autonomy of mobile robots. Therefore, this paper takes Particle Swarm Optimization (PSO) as the basic Optimization tool and focuses on the Optimization solution of multi-robot task allocation problem. The main work completed is summarized as follows:
(1) Multi-robot task assignment problem modeling. In the process of establishing the mathematical model of multi-robot task assignment, the mathematical model of multi-robot task assignment is established by introducing binary decision variables, taking into account the value of the robot, the success rate of the robot's task completion, the value of the task and the failure rate of the task to the robot.
(2) Apply PSO algorithm to solve the established task assignment model. In the solving process, aiming at the defect that the standard PSO algorithm cannot balance its global and local search ability well, a linear parameter updating rule is designed to update the three main control parameters in the PSO algorithm.
(3) Simulation verification of PSO algorithm in multi-robot task allocation. On the basis of completing the first two basic research contents, this paper finally gives the PSO algorithm in the multi-robot task allocation problem MATLAB simulation verification. Simulation results show that the algorithm has good adaptability in solving the problem of multi-robot task assignment.
Key Words： Particle swarm optimization; Optimization algorithm; Multi-robot task allocation
 
目  录
第1章 绪论    1
1.1 研究背景    1
1.2 国内外研究现状    2
1.3 研究目标与文章结构    3
第2章 多机器人任务分配问题数学建模    6
2.1 多机器人任务分配的问题描述    6
2.2 多机器人任务分配的模型描述    6
2.3 多机器人任务分配的目标函数确立    8
2.4 多机器人任务分配的约束条件确立    10
第3章 粒子群算法在多机器人任务分配中的求解    12
3.1 PSO算法描述    12
3.2 基本PSO的数学模型及优化的一般步骤    13
3.3 PSO算法参数自适应法则设计    15
3.4 PSO算法对多机器人任务分配的求解    16
第4章 实验仿真结果和分析    21
4.1数值仿真1    21
4.2数值仿真2    23
4.3数值仿真3    26
第5章 总结和展望    30
参考文献    31
致  谢    33粒子群算法在多机器人任务分配问题的应用(任务书,开题报告,论文18000字)
摘  要
多机器人系统是构建机器人工作自主化、产能高效化的高新科技生态的重要组成部分，同时也是当今学术界研究的热门问题。多机器人任务分配是多机器人系统的关键技术，因此，针对多机器人系统任务分配的优化也成为该领域研究的热点之一。然而，复杂任务下的多机器人任务分配问题已被证明是一个非确定性多项式时间困难(Non-deterministic Polynomial Hard, NP)问题。该问题的这种特性给其求解带来了巨大挑战，也体现了多机器人任务分配问题对于一个高效求解方法的迫切需求。因此，找到一个同时具有时效性，稳定性的高性能算法成为提升移动机器人自主化的重中之重。为此，本文中粒子群算法(Particle Swarm Optimization, PSO)将作为基本优化工具，围绕多机器人任务分配问题的优化求解展开研究。完成的主要工作总结如下：
（1）完成了多机器人任务分配问题建模。在建立多机器人任务分配数学模型过程中，综合考虑到机器人的价值、机器人执行任务的成功率、任务的价值和任务对机器人的干扰率，并且通过引入二元决策变量，最终建立了多机器人任务分配的数学模型。
（2）应用PSO算法对建立的任务分配模型进行求解。在求解过程中，针对标准PSO算法不能很好平衡其全局和局部搜索能力的缺陷，设计了一种线性参数更新法则对PSO算法中的三个主要控制参数进行更新。
（3）PSO算法在多机器人任务分配中的仿真验证。在完成前两个基本研究内容的基础上，本文最后给出了PSO算法在多机器人任务分配问题MATLAB仿真验证。仿真结果表明该算法在求解多机器人任务分配问题上具有较好适应性。   
关键词：粒子群算法；优化算法；多机器人任务分配
 
Abstract
Multi-robot system is an important part of the construction of high and new technology ecology with autonomous robot work and high productivity. Multi-robot task assignment is the key technology of multi-robot system, so the optimization of multi-robot task assignment has become one of the research hotspots in this field. However, under the complex task of multi-robot task allocation problem has been proved to be a non-deterministic Polynomial time difficult (Non - deterministic Polynomial Hard, NP) problem. This characteristic of the problem brings great challenge to its solution and also reflects the urgent need of multi-robot task assignment problem for an efficient solution method. Therefore, finding a high performance algorithm with timeliness and stability at the same time has become the most important task to improve the autonomy of mobile robots. Therefore, this paper takes Particle Swarm Optimization (PSO) as the basic Optimization tool and focuses on the Optimization solution of multi-robot task allocation problem. The main work completed is summarized as follows:
(1) Multi-robot task assignment problem modeling. In the process of establishing the mathematical model of multi-robot task assignment, the mathematical model of multi-robot task assignment is established by introducing binary decision variables, taking into account the value of the robot, the success rate of the robot's task completion, the value of the task and the failure rate of the task to the robot.
(2) Apply PSO algorithm to solve the established task assignment model. In the solving process, aiming at the defect that the standard PSO algorithm cannot balance its global and local search ability well, a linear parameter updating rule is designed to update the three main control parameters in the PSO algorithm.
(3) Simulation verification of PSO algorithm in multi-robot task allocation. On the basis of completing the first two basic research contents, this paper finally gives the PSO algorithm in the multi-robot task allocation problem MATLAB simulation verification. Simulation results show that the algorithm has good adaptability in solving the problem of multi-robot task assignment.
Key Words： Particle swarm optimization; Optimization algorithm; Multi-robot task allocation
 
目  录
第1章 绪论    1
1.1 研究背景    1
1.2 国内外研究现状    2
1.3 研究目标与文章结构    3
第2章 多机器人任务分配问题数学建模    6
2.1 多机器人任务分配的问题描述    6
2.2 多机器人任务分配的模型描述    6
2.3 多机器人任务分配的目标函数确立    8
2.4 多机器人任务分配的约束条件确立    10
第3章 粒子群算法在多机器人任务分配中的求解    12
3.1 PSO算法描述    12
3.2 基本PSO的数学模型及优化的一般步骤    13
3.3 PSO算法参数自适应法则设计    15
3.4 PSO算法对多机器人任务分配的求解    16
第4章 实验仿真结果和分析    21
4.1数值仿真1    21
4.2数值仿真2    23
4.3数值仿真3    26
第5章 总结和展望    30
参考文献    31
致  谢    33