Author:
Mishra Priya,Boopal Balaji,Mishra Naveen
Abstract
In the realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.
Publisher
European Alliance for Innovation n.o.
Reference25 articles.
1. Konovalenko, I.; Kuznetsova, E.; Miller, A.; Miller, B.; Popov, A.; Shepelev, D.; Stepanyan, K. New approaches to the integration of navigation systems for autonomous unmanned vehicles (UAV). Sensors 2018, 18, 3010.
2. Wang, J.; Garrat, M.; Lambert, A.; Wang, J.J.; Han, S.; Sinclair, D. Integration of GPS/INS/vision sensors to navigate unmanned aerial vehicles. ISPRS Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci. 2008, 37, 963–970.
3. García, J.; Molina, J.M.; Trincado, J.; Sánchez, J. Analysis of sensor data and estimation output with configurable UAV platforms. In Proceedings of the 2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF), Bonn, Germany, 10–12 October 2017;pp. 1–6.
4. Abdulla, A.-K.; David, M.; Fernando, G.; Arturodela, E.; José, M.A. Survey of computer vision algorithms and applications for unmanned aerial vehicles. Expert Syst. Appl. 2018, 92, 447–463.
5. Wagoner, A.R.; Schrader, D.K.; Matson, E.T. Survey on Detection and Tracking of UAVs Using Computer Vision. In Proceedings of the First IEEE International Conference on Robotic Computing (IRC), Taichung, Taiwan, 10–12 April 2017; pp. 320–325.