基于改进蚁群算法的水下机器人路径规划  

Path Planning of Underwater Robot Based on Improved Ant Colony Algorithm

作　　者：付乐乐 陈宏[1] 巩伟杰 FU Le-le;CHEN Hong;GONG Wei-jie(College of Mechatronics and Control Engineering,Shenzhen University,Shenzhen 518061,China)

机构地区：[1]深圳大学机电与控制工程学院,深圳518061

出　　处：《自动化与仪表》2022年第4期46-50,共5页Automation & Instrumentation

摘　　要：针对水下机器人路径规划局部最优、迭代次数长的问题,该文提出静态、动态环境下的改进蚁群算法。静态环境下,自动调整信息素挥发因子,并对启发函数、蚁周模型以及信息素更新规则进行修改,并采用三次B样条曲线平滑策略,使最终路径更加平滑;动态环境下采用改进蚁群算法和人工势场法融合的策略,使其能以最优路径成功避开动态障碍物。仿真实验表明,改进的算法在动、静态环境下收敛速度更快、拐点更少、时间更短,缩短了寻径距离,在水下机器人路径规划方面有很好的应用性。Aiming at the problem of local optimal path planning and long iteration times about underwater robot,writer proposes an improved ant colony algorithm under static and dynamic environments. In the static environment,the pheromone volatile factor is adjusted automatically,with appropriate modifications on heuristic function,ant cycle model and pheromone update rules. And the cubic B-spline smoothing strategy is adopted to make the final path more smoothly. The fusion strategy of improved ant colony algorithm and artificial potential field method is adopted in dynamic environment,so that it can successfully avoid dynamic obstacles by the optimal path. Simulation results verify that the improved algorithm has faster convergence speed,fewer inflection points,shorter time and shorter path finding distance under dynamic and static environments,which has a good application in the path planning for underwater robots.

关 键 词：蚁群算法 路径规划 B样条曲线 人工势场法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]