增强目标启发信息蚁群算法的移动机器人路径规划

针对传统蚁群算法在前期搜索盲目性大、拐点多等问题，对蚁群算法进行以下改进。首先，为了增强目标位置的启发信息，引入距离增益系数，将目标位置对下一个待选栅格节点的影响进行放大；然后引入带有权重的距离启发因子，在状态转移概率中加入距离启发转移概率，使蚂蚁大概率向目标栅格搜索；其次，采用正弦自适应动态调整信息素挥发因子，增强算法的全局搜索能力；最后通过修改路径减少路径冗余，进行路径安全性检查并重新调整路径，减少转弯的次数，从而提高路线质量。通过MATLAB仿真实验表明，改进蚁群算法转弯次数少，规划路径短且安全，搜索时间较快，提高了算法的收敛速度和寻优能力。

Mobile robot path planning based on enhanced goal heuristic information ant colony algorithm

Aiming at the problems of large blindness and many inflection points in the early search of traditional ant colony algorithm, the ant colony algorithm is improved as follows. Firstly, in order to enhance the heuristic information of the target position, the distance gain coefficient is introduced to amplify the influence of the target position on the next grid node to be selected. Then, the distance heuristic factor with weight is introduced, and the distance heuristic transition probability is added to the state transition probability to make the ant search to the target grid with high probability. Secondly, the sinusoidal adaptive dynamic adjustment of pheromone volatilization factor is used to enhance the global search ability of the algorithm. Finally, the path redundancy is reduced by modifying the path, the path safety check is performed and the path is re-adjusted to reduce the number of turns, thereby improving the route quality. MATLAB simulation experiments show that the improved ant colony algorithm has fewer turns, shorter and safer planning path, faster search time, and improves the convergence speed and optimization ability of the algorithm.