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A Solution for the Challenges Presented by the 2020 AUVSI SUAS Competition

机译：对2020年Auvsi Suas竞争提出的挑战的解决方案

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A suite of solutions was developed by the University of Cincinnati Aerial Vehicles (UCAV) team to address the challenges presented by the 2020 AUVSI SUAS Competition. Competition tasks are reflective of current topics in Unmanned Aerial System (UAS) research including autonomous flight, object detection classification and localization (ODLC), obstacle avoidance, coverage path planning (CPP), and aerial payload delivery. A custom designed, autonomous hexacopter Unmanned Aerial Vehicle (UAV) named Xelaya was developed, having a gross takeoff weight (GTOW) of 22kg and an endurance of more than 30 minutes, allowing for the transport of additional vehicle subsystems. A second vehicle, a custom autonomous Unmanned Ground Vehicle (UGV), was manufactured and tested to be integrated into the UAV platform for the delivery objective. A modular approach to software design was used, taking advantage of the features of Robot Operating System (ROS) for managing data flow and handling a distributed workload across multiple systems and vehicles. Both an autonomous and manual system were implemented for ODLC. The autonomous system implements a custom convolutional neural network (CNN), while the manual system is composed of two web-based graphical user interfaces (GUIs) for operator input. For obstacle avoidance, a geometry-based method is compared to a node-based A* algorithm approach in order to find the more effective way to minimize both travel distance and execution time. Several methods typically used for solving NP-hard problems, including a genetic algorithm, 2-opt heuristic, and nearest neighbor are investigated for their application to a CPP problem through the competition's search area.
机译：辛辛那提大学航空公司（UCAV）团队开发了一套解决方案，以解决2020年Auvsi Suas竞争所呈现的挑战。竞争任务是无人机航空系统（UAS）研究中当前主题的反思，包括自主飞行，对象检测分类和定位（ODLC），障碍避免，覆盖路径规划（CPP）和空中有效载荷传递。一种定制的自主六泊车无人机（UAV）被开发出来的Xelaya，具有22kg的总起飞重量（GTOW），耐久性超过30分钟，允许运输额外的车辆子系统。第二辆车是一种定制自主无人的地面车辆（UGV），并测试并测试以集成到UAV平台中进行递送目标。利用机器人操作系统（ROS）的特征来使用模块化的软件设计方法，用于管理数据流并处理跨多个系统和车辆的分布式工作负载。为ODLC实施了自主和手动系统。自主系统实现了定制卷积神经网络（CNN），而手动系统由两个基于Web的图形用户界面（GUI）组成，用于操作员输入。对于避免障碍物，将基于几何的方法与基于节点的A算法方法进行比较，以便找到更有效的方法来最小化行驶距离和执行时间。通常用于求解NP硬问题的几种方法，包括遗传算法，2-opt启发式和最近的邻居通过竞争的搜索区域将其应用于CPP问题。

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