摘要：

随着人工智能与机器人技术的深度融合, 多旋翼无人机在多个领域中得到了广泛应用, 展现了其灵活性和高效性。然而, 在开发和验证多旋翼无人机的飞行控制算法或解决方案时, 研究人员面临着高成本和高风险的挑战。为了降低这些风险并提高算法测试和优化的效率, 多旋翼无人机仿真平台提供了一个安全、可控的环境。首先, 介绍了多旋翼无人机的常规机型, 选取了常用的四旋翼无人机作为多旋翼无人机的代表机型, 根据不同仿真程度阐述了其动力学模型。接着, 对多旋翼无人机仿真平台的常规系统结构框架进行概述, 并探讨了其评价方式和分类方法。从功能和性能两个方面出发, 进一步细化了仿真平台的评价方式。多旋翼无人机仿真平台的分类一方面根据其是否支持交互学习环境进行划分, 另一方面依据不同侧重点, 从动力学、传感器和多机集群3个方面进行分类。然后, 回顾了现有无人机飞行任务的主要解决方案, 在传统解决方案和基于学习方式的解决方案背景下, 分析了现有的典型多旋翼无人机仿真平台。最后, 对多旋翼无人机仿真平台未来发展进行了展望。

关键词: 多旋翼无人机, 仿真平台, 传感器, 解决方案, 强化学习

Abstract:

The continuous integration of artificial intelligence and robotics technology has facilitated the widespread adoption of multi-rotor Unmanned Aerial Vehicles (UAVs) across various fields, demonstrating their flexibility and efficiency. However, when developing and validating flight control algorithms or solutions for multi-rotor UAVs, researchers face high costs and significant risks. To mitigate these risks and enhance the efficiency of algorithm testing and optimization, simulation platforms for multi-rotor UAVs provide a safe and controlled environment. In this regard, this paper first introduces conventional models of multi-rotor UAVs, selecting the commonly used quadrotor as the representative model. It then elaborates on dynamic models according to different levels of simulation. Subsequently, it provides an overview of the conventional system framework of multi-rotor UAV simulation platforms and discusses their evaluation methods and classification approaches. The evaluation of simulation platforms is detailed from the perspectives of function and performance. multi-rotor UAVs are classified based on whether they support an interactive learning environment and their focus areas: dynamics, sensors, and multi-UAV coordination. This paper also reviews the main solutions for existing UAV flight missions, analyzing typical multi-rotor UAV simulation platforms within the context of traditional and learning-based methods. Finally, the paper outlines future directions for multi-rotor UAV simulation platforms.

Key words: multi-rotor Unmanned Aerial Vehicle (UAV), simulation platform, sensor, solution, reinforcement learning