移动机器人路径规划是一个很复杂的问题，不仅要寻求一条无碰撞的最短路径，而且还要求该路径尽可能平滑并满足一定的安全性。本文在分析了目前各种路径规划方法优缺点的基础上，选择混沌遗传算法来解决静态环境下移动机器人的路径规划问题。 本文首先分析了路径规划技术的发展现状以及应用方法；其次通过对遗传算法和混沌算法的深入研究，剖析各自的优缺点，将其结合构成了兼备较强的全局和局部搜索能力的混沌遗传算法；接着针对路径规划问题的特点，对混沌遗传算法具体应用的各个环节进行了细致的研究，包括环境建模、染色体的表示和编码、适应度函数的设计、遗传算子的设计、混沌遗传算法参数的分析和选取等；最后在三种复杂程度不同的静态环境下进行仿真实验，并借鉴工程优化上的定量分析评价思想，引入截止代数与截止代数分布熵两个独立的指标反映优化效率，重点讨论了不同的参数和适应度系数对路径规划结果的影响，同时与其它算法进行了对比分析。 实验结果表明，混沌遗传算法能够成功地在不同的静态环境下规划出一条近似最优的路径，验证了算法的有效性。

Path planning for mobile robots is a complex problem that not only guarantees a collision-free path with minimum traveling distance but also requires smoothness and security. This dissertation presents a chaos genetic algorithm for solving the path planning problem in static mobile robot environments. Firstly, the paper summarizes and analyzes the current development and research method of path planning in several active areas. Secondly, the paper develops a chaos genetic algorithm, a hybrid of genetic and chaos algorithm, by analyzing and comparing the advantages and disadvantages of them. The new algorithm has better capability of searching globally and locally. Thirdly, according to the characteristic of path planning problem, every component of the algorithm is researched carefully, including environment representation, chromosome coding, fitness function design, genetic operators design and chaos genetic algorithm parameters selection etc. Finally, simulate experiment are finished in three static environment. drawing on the concept of quantitative analysis and evaluation in engineering optimization, the paper introduces two separate indicators, i.e., truncated generation and its distribution entropy, to evaluate the efficiency of optimization. In addition, the paper emphasizes that different parameters and the fitness function coefficients have a great effect on path planning, chaos genetic algorithm is compared and analyzed with other algorithms as well. It's proved that the algorithm can plan an approximate optimal path successfully in different static environment, and validate the effectiveness of the proposed approach.