基于蚁群算法与人工势场法的移动机器人路径规划

针对复杂环境下移动机器人路径规划困难的问题，提出了一种将全局路径规划蚁群算法与局部路径规划人工势场法相融合的混合型算法。首先，采用多因素启发函数和新的蚂蚁行进机制来解决传统蚁群算法路径质量差且易陷入对角障碍的问题；其次，针对传统蚁群算法收敛速度慢的情况，设计了自适应挥发系数和动态权重系数；接着，通过引入虚拟目标点、相对距离和安全距离的概念，解决了传统人工势场法易陷入局部极小值、目标不可达以及过度避障的问题；最后，将改进蚁群算法规划路径的转折点作为局部子目标点来调用改进的人工势场法进行二次规划。仿真表明改进蚁群算法较传统算法以及其他算法在路径长度方面优化了9.9%和2.0%，在路径转折次数方面优化了81.8%和63.6%，在收敛速度方面优化了94.2%和63.6%；改进人工势场法有效解决了自身问题；而以二者为基础的混合型算法则充分地结合了二者的优势，在复杂的静态和动态环境中具有极高的环境适应性和路径规划效率。

Path Planning of Mobile Robots Based on Ant Colony Algorithm and Artificial Potential Field Algorithm

Aiming at the difficulty of path planning for mobile robots in complex environment, a hybrid algorithm combining ant colony algorithm and artificial potential field method for local path planning was proposed. Firstly, multi-factor heuristic function and ant travel mechanism were used to solve the problem that the path quality of traditional ant colony algorithm was poor and it was easy to fall into diagonal obstacles. Secondly, in view of the slow convergence of traditional ant colony algorithm, the adaptive volatilization coefficient and dynamic weight coefficient were designed. Then, the concepts of virtual target point, relative distance and safe distance were introduced to solve the problems of local minimum, unreachable target and excessive obstacle avoidance in traditional artificial potential field method. Finally, the turning point of the path planned by the improved ant colony algorithm was used as the local subentry point to invoke the improved artificial potential field method for secondary planning. The simulation results showed that the improved ant colony algorithm optimized the path length by 9.9% and 2.0%, the path turning times by 81.8% and 63.6%, and the convergence speed by 94.2% and 63.6% compared with that of the traditional algorithm and other literature algorithms. The improved artificial potential field method effectively solved the shortcomings of unreachable target, easy to fall into local minimum and excessive obstacle avoidance. The hybrid algorithm based on the two methods effectively combined the advantages of the two methods, and had high environmental adaptability and path planning efficiency in complex static and dynamic environments.