A Solution for the Challenges Presented by the 2020 AUVSI SUAS Competition
Nicholas S. DeGroote, Evan Barnes, Jared Burton, Matthew V. Terry, Justin N. Ouwerkerk, Kelly De Oliveira Cohen · AIAA Scitech 2021 Forum · 2021
View Video Presentation: https://doi.org/10.2514/6.2021-0522.vid 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.