自主轮式移动机器人纵横向目标路径规划仿真
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摘要
对自主轮式移动机器人纵横两个方向路径进行规划,能够解决机器人单向路径规划弊端,提高执行搜索任务效率。对移动机器人纵横向目标路径的规划,需要对移动机器人基本行为进行融合,获取纵横向移动行为权值,完成机器人目标路径规划。传统方法将该问题转换为曲线有限个点生成位置优化问题,对问题进行求解,但忽略了对移动行为权值的获取,导致规划精度偏低。提出一种基于粒子群优化的自主轮式移动机器人纵横向目标路径规划方法。构建自主轮式移动机器人运动模型和障碍物拓展模型,利用二维笛卡尔栅格描述当前机器人所处环境,对移动机器人纵横向目标路径规划过程中的基本行为进行融合,依据传感器采集的实时环境信息获取移动行为的权值。实验结果表明,所提方法可以快速解决障碍物环境下自主轮式移动机器人横纵两个方向目标路径规划问题,且有效缩短了目标路径规划时间。
The traditional method ignores the acquisition of the weight of moving behavior, which leads to the low planning accuracy. Therefore, a method of path planning for vertical and horizontal target of autonomous wheeled mobile robot based on particle swarm optimization was proposed. Firstly, the motion model of autonomous wheeled mobile robot and the model of obstacle expansion were constructed. Secondly, two-dimensional Cartesian grid was used to describe the current environment of robot. Then, the basic behavior during path planning for vertical and horizontal target of mobile robot was fused. Finally, the weight of mobile behavior was obtained based on the real-time environmental information collected by the sensor. Simulation results show that the proposed method can quickly solve the path planning problem for vertical and horizontal target of autonomous wheeled mobile robot under the obstacle environment. Meanwhile, it can effectively shorten the planning time of target path.
The traditional method ignores the acquisition of the weight of moving behavior, which leads to the low planning accuracy. Therefore, a method of path planning for vertical and horizontal target of autonomous wheeled mobile robot based on particle swarm optimization was proposed. Firstly, the motion model of autonomous wheeled mobile robot and the model of obstacle expansion were constructed. Secondly, two-dimensional Cartesian grid was used to describe the current environment of robot. Then, the basic behavior during path planning for vertical and horizontal target of mobile robot was fused. Finally, the weight of mobile behavior was obtained based on the real-time environmental information collected by the sensor. Simulation results show that the proposed method can quickly solve the path planning problem for vertical and horizontal target of autonomous wheeled mobile robot under the obstacle environment. Meanwhile, it can effectively shorten the planning time of target path.
引文
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[2] 徐雪松,等.复杂环境移动群机器人最优路径规划方法[J].电子测量与仪器学报,2016,30(2):274-282.
[3] 史久根,李凯业.基于分层改进D～*算法的室内路径规划[J].计算机应用研究,2015,32(12):3609-3612.
[4] 陈薇,蒋旭云.关于轮式机器人路径规划控制仿真[J].计算机仿真,2016,33(5):367-371.
[5] 于乃功,默凡凡.基于深度自动编码器与Q学习的移动机器人路径规划方法[J].北京工业大学学报,2016,42(5):668-673.
[6] 王海泉,等.基于改进人工蜂群算法的机器人路径规划[J].控制工程,2016,23(9):1407-1411.
[7] 莫宏伟,马靖雯.一种生物地理学移动机器人路径规划算法[J].智能系统学报,2015,10(5):705-711.
[8] 朱佩华,等.基于GPL模型的仿生爬壁机器人路径规划[J].中国机械工程,2016,27(24):3273-3278.
[9] 陈彦杰,等.局部环境增量采样的服务机器人路径规划[J].仪器仪表学报,2017,38(5):1093-1100.
[10] 黄宜庆,彭凯,袁梦茹.基于多策略混合人工鱼群算法的移动机器人路径规划[J].信息与控制,2017,46(3):283-288.