引用本文: 张岳星, 王轶群, 李硕, 王晓辉. 基于海图和改进粒子群优化算法的AUV全局路径规划[J]. 机器人, 2020, 42(1): 120-128.
ZHANG Yuexing, WANG Yiqun, LI Shuo, WANG Xiaohui. Global Path Planning for AUV Based on Charts and the Improved Particle Swarm Optimization Algorithm[J]. ROBOT, 2020, 42(1): 120-128.
 Citation: ZHANG Yuexing, WANG Yiqun, LI Shuo, WANG Xiaohui. Global Path Planning for AUV Based on Charts and the Improved Particle Swarm Optimization Algorithm[J]. ROBOT, 2020, 42(1): 120-128.

## Global Path Planning for AUV Based on Charts and the Improved Particle Swarm Optimization Algorithm

• 摘要: 针对AUV（自主水下机器人）在复杂条件海域做全局路径规划时面临的环境信息缺少，环境建模困难和常规算法复杂、求解能力弱等问题，提出一种基于海图和改进粒子群优化算法的全局路径规划方法．首先利用电子海图的先验知识建立3维静态环境模型，并构造路径航程、危险度和平滑函数；在粒子群优化算法中引入搜索因子和同性因子自适应地调整参数，并结合鱼群算法的“跳跃”过程提升算法的求解能力．同时建立安全违背度和选优规则以提高所规划路径的安全性．仿真实验结果表明，本文方法与传统粒子群算法和蚁群算法相比，规划出短航程、安全性高的全局路径的能力更强，可满足AUV在复杂海域航行时的全局路径规划需求．

Abstract: When the AUV (autonomous underwater vehicle) plans a global path in sea area with complex conditions, there exist various problems, such as the lack of environmental information, the difficulty in environmental modeling, and the high complexity and the weak solution ability of conventional algorithms. To solve the problems, a global path planning method based on charts and the improved PSO (particle swarm optimization) algorithm is proposed. Firstly, a 3-dimensional static environment model based on the prior knowledge of electronic charts, and functions of the path range, the hazard degree and the smoothness are constructed. In order to improve the solution ability of the algorithm, the search factor and homology factor are introduced into the PSO algorithm to adjust the parameters adaptively, and the jump process of the fish swarm algorithm is also combined. Meanwhile, the security violation degree and the optimization rules are established to improve the security of the planned path. Finally, the simulation results show that the proposed method is more capable of planning short-range and high-security global paths compared with the traditional PSO algorithms and the ant colony algorithm (ACA), and can meet the requirements of AUV global path planning in complex sea conditions.

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