An autonomous drone flying near obstacles needs to be able to detect and avoid the obstacles or it will collide with them. In prior work, drones can detect and avoid walls using data from camera, ultrasonic or laser sensors mounted either on the drone or in the environment. It is not always possible to instrument the environment, and sensors added to the drone consume payload and power - both of which are constrained for drones. This paper studies how data mining classification techniques can be used to predict where an obstacle is in relation to the drone based only on monitoring air-disturbance. We modeled the airflow of the rotors physically to deduce higher level features for classification. Data was collected from the drone's IMU while it was flying with a wall to its direct left, front and right, as well as with no walls present. In total 18 higher level features were produced from the raw data. We used an 80%, 20% train-test scheme with the RandomForest (RF), K-Nearest Neighbor (KNN) and GradientBoosting (GB) classifiers. Our results show that with the RF classifier and with 90% accuracy it can predict which direction a wall is in relation to the drone.
翻译:在先前的工作中,无人驾驶飞机可以使用在无人驾驶飞机或环境中安装的相机、超声波或激光传感器的数据探测和避免墙壁。并不总是能够对环境进行仪器测量,无人驾驶飞机的传感器增加的传感器消耗有效载荷和功率,两者都限制无人驾驶飞机使用。本文研究如何使用数据采矿分类技术来预测无人驾驶飞机与仅以监测空中扰动为基础的无人驾驶飞机之间的障碍。我们用物理模型模拟转子的空气流以得出更高等级的分类特征。数据是在无人驾驶飞机的IMU上收集的,当时它正用墙向左、右、右和右飞,没有墙。总共从原始数据中产生了18个更高水平的特征。我们使用了80%、20%的火车测试计划,与随机Forest(RF)、K-Nearest Neighbor(KNNNN)和Gradent Boosting(GB)一起进行。我们的结果显示,与RF分类和90 %的无人驾驶飞机的路径是预测。