Kalman Prediction Based VFH of Dynamic Obstacle Avoidance for Intelligent Vehicles

摘要：

A mixed obstacle avoidance algorithm for intelligent vehicles to avoid dynamic obstacles is presented in uncertain environments. Traditional Vector Field Histogram method is combined with kalman prediction algorithm in this algorithm. The kalman predictor forecasts the optimal position estimation of dynamic obstacles at the next moment, then the intelligent vehicle calls VFH algorithm to avoid obstacles according the current position and the next position predicted by the predictor.This method solves the problem that intelligent vehicle can not choose optimal path for the reason that the intelligent vehicle has no priori knowledge about the local environment.It is more suitable for dynamic obstacles avoidance of the intelligent vehicle. The simulation results show that the method has a good real-time performance, and the intelligent vehicle can avoid the dynamic obstacles accurately and reach the target point.

关键词： intelligent vehicles kalman prediction VFH algorithm dynamic obstacles

作者: GUO Jianming ZHANG Shouping XU Jia ZHOU Shenghui

作者单位: School of Automation Wuhan University of Technology Wuhan, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 6-10

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）