利用机器学习对无人驾驶飞行器进行实时 3D 路由优化

Priya Mishra, Balaji Boopal, Naveen Mishra
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引用次数: 0

摘要

在民用无人飞行器(UAV)领域,需求的激增凸显了对尖端技术的需求。无人机系统(UAS)与人工智能(AI)的整合已成为应对城市环境挑战的关键,尤其是那些涉及障碍物碰撞风险的挑战。这些无人机配备了先进的传感器阵列,结合了激光雷达和计算机视觉技术。人工智能算法在嵌入式机器上进行全面训练,促进了稳健的空间感知模型的发展。该模型使无人机能够像人类一样理解周围环境,在错综复杂的城市景观中进行解读和导航。在任务执行过程中,人工智能驱动的感知系统会检测和定位物体,确保实时感知。本研究提出了一种创新的实时三维(3D)路径规划器,旨在优化无人机穿越障碍物环境的轨迹。该路径规划器采用了启发式 A* 算法,这是一种广受认可的人工智能搜索算法。与传统的 A* 算法不同的是,它无需在内存中存储前沿节点即可运行。相反,它依赖于从感知系统中获得的相对物体位置,采用了先进的同步定位和映射(SLAM)技术。这种方法可确保生成无碰撞路径,从而提高无人机的导航效率。此外,利用高保真无人机动力学模型,通过在受限环境中进行软件在环仿真(SITL),对所提出的路径规划器进行了严格验证。考虑到风干扰和动态障碍物等因素,还进行了初步实际飞行测试,以评估系统在现实世界中的适用性。测试结果表明,路径规划器能有效提供快速、准确的制导,从而确定了其在复杂城市场景中执行实时无人机任务的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning
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.
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