基于改进粒子群算法的路径规划

传统粒子群算法存在收敛精度低、搜索停滞等缺点，导致机器人路径规划精度低。为了提高路径规划的精度，对传统的粒子群算法进行改进。首先在算法运行的各阶段对惯性权重因子和加速因子同时使用三角函数的变化方式自适应调整，使算法中的参数在算法运行各阶段的配合达到最佳，提高了算法的搜索能力；其次在算法中引入鸡群算法中的母鸡更新方程和小鸡更新方程对搜索停滞的粒子进行扰动，并在引进的方程中使用全局最优解使扰动后的粒子向全局最优解靠近；最后通过函数优化和路径规划两组对比实验，验证了改进算法在问题优化时具有寻优精度高、鲁棒性好的优点。

Path Planning Based on Improved Particle Swarm Optimization Algorithm

The traditional particle swarm optimization (PSO) algorithm has some shortcomings such as low convergence precision, stagnant search and so on, which lead to the low precision of robot path planning. In order to improve the precision of path planning, the traditional particle swarm optimization algorithm was improved. Firstly, the inertia weight factor and acceleration factor were adjusted adaptively by the trigonometric function in each stage of the algorithm operation, so that the parameters in the algorithm were optimized in each stage of the algorithm operation, and the search ability of the algorithm was improved. Secondly, the hen equation and chick equation of chicken swarm algorithm were introduced to perturb the search stagnation particles, and the global optimal solution was used in the introduced equation to make the disturbed particle approach the global optimal solution. Finally, through two sets of comparative experiments of function optimization and path planning, it was proved that the improved algorithm had the advantages of high searching precision and good robustness.