Modified Vector Field Histogram with a Neural Network Learning Model for Mobile Robot Path Planning and Obstacle Avoidance
Modified Vector Field Histogram with a Neural Network Learning Model for Mobile Robot Path Planning and Obstacle Avoidance
复制标题
DOI：
10.4156/ijact.vol2.issue5.18
复制
发表时间：
2010-12
期刊：
影响因子：
--
通讯作者：
B. Kazem;Ali H. Hamad;Mustafa M. Mozael
中科院分区：
文献类型：
--
作者：
B. Kazem;Ali H. Hamad;Mustafa M. Mozael
在这项工作中，一个修正的矢量场直方图（MVFH）已被开发，以改善路径规划和避障的轮式驱动的移动的机器人。它允许检测未知的障碍物，以避免碰撞的同时转向的移动的机器人对目标的规则网格地图表示的工作空间环境caurried了。一个神经网络（NN）模型被用来学习许多关键情况下的环境中的机器人导航障碍物之间使用MVFH。此外，数字滤波器已被用于提高的鲁棒性的避障轨迹的移动的机器人。所提出的MVFH神经网络已实现和测试，使用MobotSim程序仿真和MATLAB。该算法具有良好的导航性能，可应用于复杂的真实的世界（迷宫般的环境），并能克服传统VFH算法的局限性（如宽候选谷、窄走廊和目标距离限制）。
In this work, a Modified Vector Field Histogram (MVFH) has been developed to improve path planning and obstacle avoidance for a wheeled driven mobile robot. It permits the detection of unknown obstacle to avoid collisions by simultaneously a steering the mobile robot toward the target; a regular grid map representation for a work space environment is caurried out. A Neural Network (NN) model is used to learn many critical situations of environment during robot navigation among obstacles using MVFH. Also, digital filter has been utilized for improving the robustness of obstacle avoidance trajectory of mobile robot. The proposed MVFH-NN has been implemented and tested by using MobotSim program simulation and MATLAB . The developed algorithm showed good navigation properties and can be used in complex real world (maze-like environment), it also shows good ability to overcome limitations of the traditional VFH algorithm (like wide candidate valley, narrow hallway and target distance limitation).