Tentacle algorithm of obstacle avoidence and autonomous driving for intelligent vehicle
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摘要: 从Velodyne雷达构建障碍物地图入手, 分析了触须构建方法、自主驾驶和避障策略。考虑车辆质心侧偏角对触须重构的影响, 运用卡尔曼滤波对惯导数据进行处理, 得出车辆纵向和侧向实时速度, 从而对质心侧偏角实时辨识, 并利用质心侧偏角对不同分组的触须进行修正。计算结果表明: 在中低速时, 轨迹误差由0.40 m减小到0.20 m; 在高速时, 通过采用性能优良的控制器和合理的融合参数使轨迹误差由1.00 m减小到0.75 m。可见, 采用修正的触须算法可以较好实现车辆自主驾驶和合理避障。Abstract: Obstacle map was established by using velodyne radar. Tentacle regeneration, autonomous driving and avoiding obstacle strategy were analyzed. The influence of sideslip angle on tentacle regeneration was considered. INS (inertial navigation system) sensors with GPS were integrated by Kalman filter, and the longitudinal velocity and lateral velocity of vehicle were obtained. Therefore, tentacle regeneration method corresponding to specific "speed sets" based on sideslip angle identification was promoted. At low-medium speed, trajectory error reduces from 0.40 m to 0.20 m. At high speed, because of using good-performance controller and appropriate fusion parameters, trajectory error reduces from 1.00 m to 0.75 m. Analysis result indicates that the promoted method effectively ensures obstacle avoidence and autonomous driving.
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