摘要
针对当前基本蚁群算法应用于水下机器人全局路径规划时存在路径搜索速度慢、容易陷入局部最优等问题,对其进行优化,提出一种改进蚁群算法。首先,改进算法引入A*算法作为新的初始路径搜索策略提高初始解的质量,加快算法收敛速度;针对特殊环境下算法容易陷入局部最优的问题做出优化,引入狼群分配策略进行蚂蚁回退。此外,对距离启发函数做出改进,综合考虑当前节点和下一节点以及下一节点和目标节点之间的距离,提高了算法搜索效率;提出一种信息素动态自适应更新策略,加快了算法前期搜寻效率,同时又扩大了算法后期搜寻范围。最后,以三次B样条法为基础引入路径平滑操作,去除规划路径结果中的冗余节点,减少了水下机器人移动过程中的能耗。仿真结果表明,和基本蚁群算法相比,改进算法不仅能取得更短、能耗更低的最优路径,收敛速度也更快。
Aiming at the problems that the current basic ant colony algorithm is applied to the global path planning of underwater robots, there are problems such as slow path search speed and easy to fall into local optimum. To optimize it, an improved ant colony algorithm is proposed. First, the improved algorithm introduces the A* algorithm as a new initial path search strategy to improve the quality of the initial solution and speed up the convergence speed of the algorithm;Secondly,to optimize the problem that the algorithm is prone to fall into local optimality under special circumstances, the wolf allocation strategy is introduced;In addition, the distance heuristic function is improved, and the distance between the current node and the next node and the distance between the next node and the target node is comprehensively considered to improve the search efficiency of the algorithm. A pheromone dynamic adaptive update strategy is proposed, which speeds up the search efficiency in the early stage of the algorithm, and at the same time expands the search range in the later stage of the algorithm;Finally, based on the cubic B-spline method, the path smoothing operation is introduced to remove the redundant nodes in the planning path result, reducing the underwater robot moving process energy consumption. The simulation results show that, compared with the basic ant colony algorithm, the improved algorithm can not only obtain a shorter optimal path with lower energy consumption, but also converge faster.
作者
刘兴盛
王俊雄
LIU Xing-sheng;WANG Jun-xiong(School ofNaval Architecture,Ocean and Civil Engineering,Shanghai Jiaotong University,Shanghai 200240,China)
出处
《舰船科学技术》
北大核心
2022年第21期80-87,共8页
Ship Science and Technology
关键词
水下机器人
路径规划
蚁群算法
蚂蚁回退
启发函数
underwater robots
path planning
ant colony algorithm
ants back
heuristic function
