摘要
针对机器人在不同类型障碍物环境下的路径规划问题,提出基于神经网络的改进粒子群优化算法。采用神经网络统一障碍物环境建模,快速实现路径与所有障碍物的碰撞检测,通过惯性权重和三次样条曲线平滑路径,以较低的粒子编码维度,在提高算法收敛速度的同时保持路径精度,避免陷入局部最优。仿真结果表明:神经网络能够统一静态和动态障碍物环境表示和碰撞检测模型,改进的粒子群优化算法可以应用于静态和动态障碍物环境,快速规划出无碰撞平滑路径,路径长度更短,算法的迭代次数更少。
In order to plan robot path in dynamic environment with different types of obstacles,an improved particle swarm optimization(PSO) algorithm based on neural network was proposed,where neural network was used to model the environment and quickly complete collision detection of dynamic obstacles.Through inertia weight and cubic spline path smoothing,the convergence speed of the algorithm was improved with low coding dimension of particles,while maintaining the path accuracy and avoid falling into local optimum in the later stage.Simulation results show that the neural network can unify the environmental representation and the collision detection model of static and dynamic obstacles,the improved PSO algorithm can quickly plan smooth collision free paths under both static and dynamic environments,with shorter path length and fewer iterations.
作者
陈秋莲
郑以君
蒋环宇
陈燕
CHEN Qiulian;ZHENG Yijun;JIANG Huanyu;CHEN Yan(School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China)
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2021年第2期51-55,共5页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(71371058)
广西自然科学基金资助项目(2020GXNSFAA159090)
广西大学资助项目(XBZ200371)。
关键词
神经网络
三次样条
改进粒子群优化
动态环境
路径规划
neural network
cubic spline
improved particle swarm optimization
dynamic environments
path planning
