基于IHDR的自主学习巡检技术研究

Research on Autonomous Learning Inspection Technology Based on IHDR

针对无人机路径规划存在只适用于静态场景的问题,提出一种自主飞行巡检方法。利用增量分层判别回归(IHDR)树存储无人机飞行经验,通过当前位置矢量搜索IHDR树,得到飞行控制量。根据当前位置与期望位置的偏差调整输出控制量,实现人造目标的巡检。实验结果表明,与IHDR方法相比,该方法学习时间缩短12.2%,且具有较高的准确率,适用于无人机巡检。

Aiming at the problem that unmanned aerial vehicle path planning only applies to static scenes,an autonomous flight inspection method is proposed.The drone stores the flight experience in the structure of the Incremental Hierarchical Discriminant Regression(IHDR) tree.The current position vector is input to search the IHDR tree to obtain the flight control amount.The output control amount is adjusted according to the deviation between the current position and the desired position,and the inspection of the artificial target is realized.Experimental results show that compared with the IDHR method,the learning time of the method is shortened by about 12.2 %,and the method has high accuracy,and is suitable for drone inspection.