基于势场法和遗传算法的机器人路径规划技术研究
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摘要
移动机器人是迅速发展起来的一门综合学科,集成了计算机、电子、自动控制以及人工智能等多学科的最新研究成果,代表了机电一体化的最高成就。近年来,移动机器人路径规划已经成为自动控制、计算机和人工智能等领域的一个研究热点,其发展对国防、社会、经济和科学技术具有重大的影响力,已成为各国高科技领域的战略性研究目标。
本文研究了基于势场法和遗传算法的移动机器人路径规划技术,在传统方法的基础上,提出了一些改进算法及新的解决方案,以提高算法的计算效率,扩展其使用范围。具体的研究内容包括以下几个方面：
(1)提出了一种改进的机器人路径规划方法。为了对机器人的复杂工作空间进行预处理,采用二值图像的数学形态学的方法,利用膨胀运算和腐蚀运算两种对偶的基本变换,将离散的障碍物个体融合为完整的障碍物体,使用改进的势场法进行机器人导航,以改善其运动轨迹；另外,通过设置子目标点使陷入局部极小的机器人“逃离”极小状态。
(2)考虑到动态环境下移动机器人路径规划的各个量都可能在变化,在人工势场算法中引入了有关的位置信息、速度信息和加速度信息。在路径规划过程中当机器人到达或者追上了目标点时,相对加速度值为零。否则,调整参数使相对加速度值为零。又利用人工势场法结合遗传算法进行路径规划,在人工势场算法中引用一种“逃脱力”,当机器人陷入局部最小状态时,使用“逃脱力”来逃脱局部最小的限制。利用遗传算法进行全局搜索和个体优化,保证了最优个体传递到下一代。
(3)提出了一种改进的遗传算法,在该算法中,设计了一种新的适应性函数,该函数考虑了路径长度信息、碰撞惩罚因素、路径间隙因素；并且设计了一套合理的遗传算子及路径修复机制来优化路径；给出了理想的路径优化参数。该方法能够在起始点和目标点之间搜索一条优化路径。
(4)以人工势场法和栅格法为基础,考虑到遗传算法的“收敛速度慢”和“早熟收敛”问题,提出了一种基于量子遗传算法的机器人路径规划方法。该方法引入量子遗传算法和势场栅格法进行融合,来求解移动机器人路径规划问题。采用栅格法进行全局路径规划、人工势场法对移动机器人进行控制、量子遗传算法对最优或次优个体进行选择,并且引入双适应度评价函数对进化个体进行评价,为最优或次优个体进入下一代提供了保障。
(5)由于遗传算法的过早收敛而使一些优秀个体过早地被排除掉,从而导致搜索范围缩小及产生局部最优的缺陷。提出了一种基于改进染色体编码的自适应遗传算法,采用方向和距离对来编码染色体,使用自适应控制交叉概率函数(P(?))和突变概率函数(只,)进行遗传操作。该算法使得过早收敛问题得以缓解,同时又提高了搜索的范围和效率。
(6)提出了一种基于量子染色体变异的融合算法。首先,对人工势场的斥力场进行改进,然后利用融合的人工势场法和栅格法对路径进行规划,产生初始化种群,最后利用量子比特对染色体编码、利用量子染色体变异对种群个体进行更新,完成最佳路径搜索。提高了种群质量和收敛速度,有效地避开障碍物,稳定地产生最佳规划路径,适合于求解复杂优化问题。
本文在最后对全文进行了总结,并且对今后进一步的研究方向进行了展望。
Mobile robot system, as an integrative subject with updated research results in mechanical, electronic, computer, automatic control and artificial intelligence, represents is tremendous success of mechatronics. In recent years, mobile robot path planning has become an important research area in automatic control, computer and artificial intelligence. The development of mobile robot path planning has imposing on the defense, society, economy and academy, and becomes the tactic research object of high technology of all countries.
Mobile robot path planning based on artificial potential field and genetic algorithm has been studied in this paper. Several improved methods and novel solutions are presented in order to improve computational efficiency, and additionally extend application domains. The main content of this dissertation include the following aspects:
(1) An improved method for moblie robot path planning is proposed for the innate limitations of potential field. In order to pretreat and optimize the workspace of robot, expansion and erosion mathematical operations based on mathematical morphology of binary image are used to integrate discrete obstacles in to complete obstacles, and improved potential field is used to navigate robot to improve trajectory of robot. Furthermore, sub-goal point is set for robot to get rid of local minimization rapidly.
(2) Each object is probably moving in a dynamic environment for mobile robot path planning, so the position, the velocity and the acceleration are taken into account, the relative factor of the distance and the acceleration is introduced in the repulsive potential function. In path planning when the robot reaches or catches up the goal, the relative acceleration is zero. Otherwise, the parameters are adjusted to reduce the relative acceleration to zero. In addition the potential field method is combined with the genetic algorithm to plan the path for mobile robot, an escape force function is taken into account in potential function, so the local minimas is solved by the escape force function. Genetic algorithm is used for global search to get the optimal path, and guarantees that the best individual is passed to the next generation.
(3) An improved genetic algorithm is presented for mobile robot path planning in static environment. In this mothod the fitness function is introduced by tacking the path length, the penalty factor of collision and the path clearance into account, a set of suitable genetic operators are designed, the path repair mechanisms are used for a local genetic search and the optimization parameters are given for the desired path. The proposed mothod can accomplish search and find a optimal path from the start to the goal.
(4) Based on artificial potential field and grid method, in order to solve the prematurity and lower convergence speed in genetic algorithm for robotic path planning, a novel mobile robot path planning method based on quantum genetic algorithm is proposed. In the method the quantum genetic algorithm is combined with artificial potential field and grid method to plan parh. it uses grid method to establish mobile robot work environment model, artificial potential field to control mobile robot, quantum genetic algorithm to select the optimal or sub-optimal path, and double fitness evaluation function to evaluate the path to protect the optimal or sub-optimal path in to the next generation.
(5) As premature convergence of genetic algorithms can make some outstanding individuals to be excluded prematurely, and lead to narrowing the search range and causing the local optimization. In order to overcome this defect, an improved chromosome encoding based adaptive genetic algorithm is proposed. In this algorithm, pairs of direction and distance used for chromosome encoding is combined with adaptive adjusting probability functions(P and Pm) for genetic operator, the method alleviates the problem of premature convergence and improves the efficiency and range of searching.
(6) A fusion algorithm based on quantum chromosome mutation is proposed. Firstly, the new repulsive potential function are reformed. Then, the fusion method of artificial potential field and grid is used to establish work environment model for mobile robot and produce initialize population. Finally, quantum bit is used to code chromosome, and quantum chromosome mutation is used to update individual of population for getting the best path. This method increases population quality and convergence rate, avoids safely the obstacles by optimal path, it is fit for the solution of complex optimization problems.
A summary of the research conclusions and a discussion on the most promising paths of future research work are presented in the last chapter of this dissertation.
本文研究了基于势场法和遗传算法的移动机器人路径规划技术,在传统方法的基础上,提出了一些改进算法及新的解决方案,以提高算法的计算效率,扩展其使用范围。具体的研究内容包括以下几个方面：
(1)提出了一种改进的机器人路径规划方法。为了对机器人的复杂工作空间进行预处理,采用二值图像的数学形态学的方法,利用膨胀运算和腐蚀运算两种对偶的基本变换,将离散的障碍物个体融合为完整的障碍物体,使用改进的势场法进行机器人导航,以改善其运动轨迹；另外,通过设置子目标点使陷入局部极小的机器人“逃离”极小状态。
(2)考虑到动态环境下移动机器人路径规划的各个量都可能在变化,在人工势场算法中引入了有关的位置信息、速度信息和加速度信息。在路径规划过程中当机器人到达或者追上了目标点时,相对加速度值为零。否则,调整参数使相对加速度值为零。又利用人工势场法结合遗传算法进行路径规划,在人工势场算法中引用一种“逃脱力”,当机器人陷入局部最小状态时,使用“逃脱力”来逃脱局部最小的限制。利用遗传算法进行全局搜索和个体优化,保证了最优个体传递到下一代。
(3)提出了一种改进的遗传算法,在该算法中,设计了一种新的适应性函数,该函数考虑了路径长度信息、碰撞惩罚因素、路径间隙因素；并且设计了一套合理的遗传算子及路径修复机制来优化路径；给出了理想的路径优化参数。该方法能够在起始点和目标点之间搜索一条优化路径。
(4)以人工势场法和栅格法为基础,考虑到遗传算法的“收敛速度慢”和“早熟收敛”问题,提出了一种基于量子遗传算法的机器人路径规划方法。该方法引入量子遗传算法和势场栅格法进行融合,来求解移动机器人路径规划问题。采用栅格法进行全局路径规划、人工势场法对移动机器人进行控制、量子遗传算法对最优或次优个体进行选择,并且引入双适应度评价函数对进化个体进行评价,为最优或次优个体进入下一代提供了保障。
(5)由于遗传算法的过早收敛而使一些优秀个体过早地被排除掉,从而导致搜索范围缩小及产生局部最优的缺陷。提出了一种基于改进染色体编码的自适应遗传算法,采用方向和距离对来编码染色体,使用自适应控制交叉概率函数(P(?))和突变概率函数(只,)进行遗传操作。该算法使得过早收敛问题得以缓解,同时又提高了搜索的范围和效率。
(6)提出了一种基于量子染色体变异的融合算法。首先,对人工势场的斥力场进行改进,然后利用融合的人工势场法和栅格法对路径进行规划,产生初始化种群,最后利用量子比特对染色体编码、利用量子染色体变异对种群个体进行更新,完成最佳路径搜索。提高了种群质量和收敛速度,有效地避开障碍物,稳定地产生最佳规划路径,适合于求解复杂优化问题。
本文在最后对全文进行了总结,并且对今后进一步的研究方向进行了展望。
Mobile robot system, as an integrative subject with updated research results in mechanical, electronic, computer, automatic control and artificial intelligence, represents is tremendous success of mechatronics. In recent years, mobile robot path planning has become an important research area in automatic control, computer and artificial intelligence. The development of mobile robot path planning has imposing on the defense, society, economy and academy, and becomes the tactic research object of high technology of all countries.
Mobile robot path planning based on artificial potential field and genetic algorithm has been studied in this paper. Several improved methods and novel solutions are presented in order to improve computational efficiency, and additionally extend application domains. The main content of this dissertation include the following aspects:
(1) An improved method for moblie robot path planning is proposed for the innate limitations of potential field. In order to pretreat and optimize the workspace of robot, expansion and erosion mathematical operations based on mathematical morphology of binary image are used to integrate discrete obstacles in to complete obstacles, and improved potential field is used to navigate robot to improve trajectory of robot. Furthermore, sub-goal point is set for robot to get rid of local minimization rapidly.
(2) Each object is probably moving in a dynamic environment for mobile robot path planning, so the position, the velocity and the acceleration are taken into account, the relative factor of the distance and the acceleration is introduced in the repulsive potential function. In path planning when the robot reaches or catches up the goal, the relative acceleration is zero. Otherwise, the parameters are adjusted to reduce the relative acceleration to zero. In addition the potential field method is combined with the genetic algorithm to plan the path for mobile robot, an escape force function is taken into account in potential function, so the local minimas is solved by the escape force function. Genetic algorithm is used for global search to get the optimal path, and guarantees that the best individual is passed to the next generation.
(3) An improved genetic algorithm is presented for mobile robot path planning in static environment. In this mothod the fitness function is introduced by tacking the path length, the penalty factor of collision and the path clearance into account, a set of suitable genetic operators are designed, the path repair mechanisms are used for a local genetic search and the optimization parameters are given for the desired path. The proposed mothod can accomplish search and find a optimal path from the start to the goal.
(4) Based on artificial potential field and grid method, in order to solve the prematurity and lower convergence speed in genetic algorithm for robotic path planning, a novel mobile robot path planning method based on quantum genetic algorithm is proposed. In the method the quantum genetic algorithm is combined with artificial potential field and grid method to plan parh. it uses grid method to establish mobile robot work environment model, artificial potential field to control mobile robot, quantum genetic algorithm to select the optimal or sub-optimal path, and double fitness evaluation function to evaluate the path to protect the optimal or sub-optimal path in to the next generation.
(5) As premature convergence of genetic algorithms can make some outstanding individuals to be excluded prematurely, and lead to narrowing the search range and causing the local optimization. In order to overcome this defect, an improved chromosome encoding based adaptive genetic algorithm is proposed. In this algorithm, pairs of direction and distance used for chromosome encoding is combined with adaptive adjusting probability functions(P and Pm) for genetic operator, the method alleviates the problem of premature convergence and improves the efficiency and range of searching.
(6) A fusion algorithm based on quantum chromosome mutation is proposed. Firstly, the new repulsive potential function are reformed. Then, the fusion method of artificial potential field and grid is used to establish work environment model for mobile robot and produce initialize population. Finally, quantum bit is used to code chromosome, and quantum chromosome mutation is used to update individual of population for getting the best path. This method increases population quality and convergence rate, avoids safely the obstacles by optimal path, it is fit for the solution of complex optimization problems.
A summary of the research conclusions and a discussion on the most promising paths of future research work are presented in the last chapter of this dissertation.
引文
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