改进IACA-GM算法在移动机器人路径规划中的研究

Reaserch of Improved IACA-GM Algorithm of Path Planning for Mobile Robot

针对传统蚁群算法应用在机器人路径规划当中,容易出现搜索空间过大,容易陷入局部最优,搜索效率过低等缺陷,提出一种在传统蚁群算法基础上,加入伪随机因子,利用下一节点到目标节点的距离来修改期望启发函数,改变差路径信息素更新方式的算法(Improved ant colony algorithm based on grid model,IACA-GM),建立栅格环境模型进行模拟仿真,实验结果表明,IACA-GM算法有效地缩小了最优路径的搜索范围,减少了循环次数,加快了算法收敛速度,提高了机器人对最优路径的搜索效率。

Aiming at the shortcomings of traditional ant colony algorithm in robot path planning,such as too large search space,easily falling into local optimum and too low search efficiency,it proposes a new algorithm based on traditional ant colony algorithm,which adds pseudo-random factor and uses the distance from the next node to the target node to modify the expected heuristic function and change the difference path.The improved ant colony algorithm based on grid model(IACAGM)is used to build a grid environment model for simulation.The experimental results show that IACA-GM algorithm effectively reduces the search range of the optimal path,reduces the number of cycles,accelerates the convergence speed of the algorithm,and improves the robot pairing.Search Efficiency of Optimal Path.