基于蚁群算法的机器人路径规划研究
发布时间：2018-07-29 09:09
【摘要】： 移动机器人路径规划是机器人研究领域的核心内容之一,具有复杂性、约束性和非线性等特点。蚁群算法(ACA)是最近十几年发展起来的仿生优化算法,该算法在解决许多复杂问题方面已经展现出其优异的性能和巨大的发展潜力。 本文主要研究静态环境下基于蚁群算法的移动机器人全局路径规划。 首先,采用栅格法建立环境模型,并利用做过改进的基本蚁群算法在栅格环境模型中进行路径规划,这些改进有:利用伪随机比例规则代替随机比例规则进行路径转移;限制了蚂蚁行至当前栅格时下一步允许选择的栅格范围;对启发函数进行了重新定义;让蚂蚁根据转移概率利用“轮盘赌”方法选择下一个栅格。 其次,针对基本蚁群算法在某些方面的不足和缺陷提出了三种改进算法:针对蚂蚁在搜索路径过程中落入障碍物陷阱而导致的算法停滞现象,提出了带夭折策略的蚁群算法;针对蚁群在路径搜索初始阶段建立的非最优路径上的信息素对以后蚁群的信息误导作用,提出了带奖罚机制的蚁群算法;针对机器人在实际工作中的安全避碰问题,提出了基于保守蚂蚁的蚁群算法。 最后,在蚁群算法的基础上结合遗传算法(GA)提出了两种改进算法:GA-ACA算法和ACA-GA算法,并将其应用于机器人路径规划。 为了验证本文所提各种算法的有效性,基于MATLAB 7.5软件开发环境设计了基于蚁群算法的移动机器人路径规划仿真系统。仿真结果验证了所提算法的有效性。
[Abstract]:The path planning of mobile robot is one of the core contents in the field of robot research, which has the characteristics of complexity, constraint and nonlinearity. Ant colony algorithm (ACA) is a bionic optimization algorithm developed in recent years. It has shown excellent performance and great potential in solving many complex problems. This paper mainly studies the global path planning of mobile robot based on ant colony algorithm in static environment. Firstly, the environmental model is built by grid method, and the improved basic ant colony algorithm is used to plan the path in the grid environment model. These improvements are as follows: the pseudo-random proportional rule is used to replace the random proportional rule for path transfer; It limits the range of grid that ants can select next when they go to the current grid; redefines the heuristic function; and allows ants to select the next grid according to the transfer probability using the method of "roulette". Secondly, three improved algorithms are put forward in view of the shortcomings and defects of basic ant colony algorithm in some aspects: aiming at the stagnation of the algorithm caused by ants falling into obstacle trap in the course of searching path, the ant colony algorithm with abortive strategy is put forward; Aiming at the misguided effect of pheromone on the non-optimal path set up by ant colony in the initial stage of path search, an ant colony algorithm with reward and penalty mechanism is proposed to solve the problem of safe collision avoidance in actual work. Ant colony algorithm based on conserved ants is proposed. Finally, based on ant colony algorithm (ACA) and genetic algorithm (GA), two improved algorithms, namely: GA-ACA algorithm and ACA-GA algorithm, are proposed and applied to robot path planning. In order to verify the validity of the algorithms proposed in this paper, a path planning simulation system for mobile robots based on ant colony algorithm is designed based on MATLAB 7.5 software development environment. Simulation results verify the effectiveness of the proposed algorithm.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2009
【分类号】：TP301.6
本文编号：2152195
[Abstract]:The path planning of mobile robot is one of the core contents in the field of robot research, which has the characteristics of complexity, constraint and nonlinearity. Ant colony algorithm (ACA) is a bionic optimization algorithm developed in recent years. It has shown excellent performance and great potential in solving many complex problems. This paper mainly studies the global path planning of mobile robot based on ant colony algorithm in static environment. Firstly, the environmental model is built by grid method, and the improved basic ant colony algorithm is used to plan the path in the grid environment model. These improvements are as follows: the pseudo-random proportional rule is used to replace the random proportional rule for path transfer; It limits the range of grid that ants can select next when they go to the current grid; redefines the heuristic function; and allows ants to select the next grid according to the transfer probability using the method of "roulette". Secondly, three improved algorithms are put forward in view of the shortcomings and defects of basic ant colony algorithm in some aspects: aiming at the stagnation of the algorithm caused by ants falling into obstacle trap in the course of searching path, the ant colony algorithm with abortive strategy is put forward; Aiming at the misguided effect of pheromone on the non-optimal path set up by ant colony in the initial stage of path search, an ant colony algorithm with reward and penalty mechanism is proposed to solve the problem of safe collision avoidance in actual work. Ant colony algorithm based on conserved ants is proposed. Finally, based on ant colony algorithm (ACA) and genetic algorithm (GA), two improved algorithms, namely: GA-ACA algorithm and ACA-GA algorithm, are proposed and applied to robot path planning. In order to verify the validity of the algorithms proposed in this paper, a path planning simulation system for mobile robots based on ant colony algorithm is designed based on MATLAB 7.5 software development environment. Simulation results verify the effectiveness of the proposed algorithm.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2009
【分类号】：TP301.6
【引证文献】
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