基于改进精英势场蚁群算法的机器人三维路径规划算法研究

Robot 3D Path Planning Algorithm Based on Improved Elitist Potential Field Ant Colony Algorithm

摘要: 三维路径规划是机器人完成目标任务的重要部分，针对蚁群算法存在收敛速度慢，后期易陷入局部最优解，平滑度较低等问题，提出了一种基于改进精英势场蚁群算法的机器人三维路径规划算法。首先引入人工势场的方向性初始化信息素浓度来加快算法收敛速度，同时采用基于精英策略的信息素浓度更新方式，增强优秀个体对种群的引导作用，并采用双向搜索的策略，进一步加快算法的收敛速度，避免算法陷入局部最优解，然后引入方向扩展搜索策略对轨迹进行处理，以提高平滑度。最后通过仿真实验对精英势场蚁群算法、蚁群算法和粒子群算法进行了比较。实验结果表明，相对于其它智能优化算法，精英势场蚁群算法具有收敛速度快、搜索效率高、最终搜索的路径更优且更加平滑等特点。

Abstract: Three dimensional path planning is an important part of robot to complete the target task. Aiming at the problems of slow convergence speed, easy to fall into local optimal solution and low smoothness in the later stage of ant colony algorithm, therefore this paper proposes a robot 3D path planning algorithm based on improved elitist potential field ant colony algorithm. Firstly, the orientation of the artificial potential field is introduced to initialize the pheromone concentration to accelerate the convergence speed of the algorithm, and the pheromone concentration update method based on elite strategy is adopted to enhance the guidance of excellent individuals to the population, and the two-way search strategy is adopted to further accelerate the convergence speed of the algorithm and avoid the algorithm falling into the local optimal solution, and then the direction expansion search strategy is introduced to improve the trajectory Line processing to improve smoothness. Through simulation experiments, the elitist potential field ant colony algorithm, ant colony algorithm and particle swarm algorithm are compared. The experimental results show that compared with other intelligent optimization algorithms, the elitist potential field ant colony algorithm has fast convergence speed, high search efficiency, better and smoother final search path.