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PIE: a Tool for Data-Driven Autonomous UAV Flight Testing
Journal of Intelligent & Robotic Systems ( IF 3.3 ) Pub Date : 2019-09-04 , DOI: 10.1007/s10846-019-01078-y
Mrinmoy Sarkar , Abdollah Homaifar , Berat A. Erol , Mohammadreza Behniapoor , Edward Tunstel

In this paper, a novel technique is presented to test the flight of an unmanned aerial vehicle autonomously in a real-world scenario using a data-driven technique without intervening with its onboard software. With the growing applications of such vehicles, testing of autonomous flight is a very important task for rapid deployment. There are different tools for modeling and simulating unmanned vehicles in virtual worlds such as Gazebo, MATLAB, Simulink, and Webots to name a few. None of these simulation tools are able to model all possible physical parameters of a real-world environment. Hence, the flight controller or mission planning software has to be tested in the physical world in the presence of an expert before deployment for a specific task. A Perception Inference Engine evaluation tool is presented that can infer internal states of the autonomous system from external observations only. The Gazebo simulation platform is used to collect data to develop the perception model. For real-time data collection, a VICON motion capture system is used to observe the autonomous flight of a small unmanned aerial vehicle. A state-of-the-art decision tree algorithm is used to implement the data-driven approach. The technique was tested using simulation data and verified with real-time data from Intel Aero Ready to Fly and Parrot AR. 2.0 drones. Moreover, we analyzed the robustness of the proposed system by introducing noise in sensor measurement and ambiguity in the testing scenario. We compared the performance of the decision tree classifier with Naïve bayes and support vector machine classifiers. It is shown that the developed system can be used for the performance evaluation of a UAV operating in the physical world by significantly reducing uncertainty in mission failure due to environmental parameters.



中文翻译:

PIE:用于数据驱动的自主无人机飞行测试的工具

在本文中,提出了一种新颖的技术,可以在不涉及其机载软件的情况下,使用数据驱动技术在现实世界中自动测试无人机的飞行情况。随着此类车辆的日益增长的应用,自动飞行测试对于快速部署是非常重要的任务。在虚拟世界中,有多种用于对无人驾驶车辆进行建模和仿真的工具,例如Gazebo,MATLAB,Simulink和Webots等。这些仿真工具均无法对现实环境中所有可能的物理参数进行建模。因此,在部署特定任务之前,必须在专家在场的情况下对飞行控制器或任务计划软件进行物理测试。提出了一种感知推理引擎评估工具,该工具只能从外部观察来推断自治系统的内部状态。凉亭模拟平台用于收集数据以开发感知模型。对于实时数据收集,VICON运动捕获系统用于观察小型无人机的自主飞行。最新的决策树算法用于实现数据驱动的方法。使用模拟数据对该技术进行了测试,并使用了Intel Aero Ready to Fly和Parrot AR的实时数据进行了验证。2.0无人机。此外,我们通过在传感器测量中引入噪声和测试场景中的歧义性来分析所提出系统的鲁棒性。我们将决策树分类器与朴素贝叶斯和支持向量机分类器的性能进行了比较。

更新日期:2020-04-21
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