收稿日期:2026-01-16
修回日期:2026-02-12
接受日期:2026-03-09
出版日期:2026-03-24
发布日期:2026-03-24
通讯作者:
姜斌
E-mail:binjiang@nuaa.edu.cn
基金资助:
Mou CHEN, Zihan ZHEN, Bin JIANG(
)
Received:2026-01-16
Revised:2026-02-12
Accepted:2026-03-09
Online:2026-03-24
Published:2026-03-24
Contact:
Bin JIANG
E-mail:binjiang@nuaa.edu.cn
Supported by:摘要:
低空经济作为新质生产力的代表性产业之一,不仅可以创造新的经济增长点,还能带动低空制造、低空飞行、低空保障以及综合服务等多个相关产业的发展,对推动经济增长和促进科技创新等具有多重重要意义。低空无人机智能避障决策与安全控制是保证低空无人机高效安全运输的前提条件,因此是低空经济领域近年来的研究热点之一。基于国内外现有的研究成果,综述了低空无人机智能避障决策与安全控制技术的研究现状并展望了未来发展方向。主要从低空无人机安全避障决策,安全航路规划,卫星信号拒止下的低空无人机安全控制,干扰下低空无人机安全控制,以及多约束下低空无人机安全控制几个方面,综述了目前已解决的关键技术。最后,对低空无人机智能避障决策与安全控制的未来研究方向进行了展望。
中图分类号:
陈谋, 甄子涵, 姜斌. 低空无人机智能避障决策与安全控制技术综述[J]. 航空学报, 2026, 47(9): 533388.
Mou CHEN, Zihan ZHEN, Bin JIANG. Overview of intelligent obstacle avoidance decision-making and safety control technologies for low-altitude UAV[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(9): 533388.
表1
低空无人机安全避障决策技术总结
| 避障决策技术 | 核心思想 | 存在问题 | 适用场景 | 代表性方法 |
|---|---|---|---|---|
| 几何与规则类方法 | 通过几何推理或算法预定义的规则进行避障 | 易出现振荡现象 | 障碍物明确、需强实时响应的飞行避障 | VO[ |
方法 人工势场类 | 将目标、障碍、其他机体等分别建模为不同类型的势场,通过合力方向进行避障决策 | 局部极小值、目标不可达问题 | 常用于低速飞行避障或编队队形保持 | APF及其衍生算法[ |
| 基于优化的避障决策方法 | 将避障问题建模为优化问题中的约束,嵌入优化算法进行求解 | 复杂环境中优化问题的可解性与计算量消耗问题 | 障碍物约束明确、需综合性能权衡的场景 | CBF[ |
方法 基于人工智能的避障决策 | 能够通过学习环境特征、运动模式和策略经验,进行自主避障决策 | 训练数据集难获取、算法可解释性问题 | 环境不确定性高、规则难以设计的低空避障场景 | 端到端网络[ |
表2
低空单无人机安全航路规划技术总结
| 航路规划技术 | 核心思想 | 存在问题 | 适用场景 | 代表性方法 |
|---|---|---|---|---|
| 图搜索类算法 | 将环境离散为栅格或图结构,航路规划转化为路径搜索问题 | 复杂场景计算开销大,不易适应动态环境 | 结构化、可精确建模的全局场景 | A*[ |
| 采样类算法 | 通过随机采样,构建可行路径连通图,实现航路规划 | 航迹通常需进行后处理以满足光滑性和动力学约束 | 障碍结构复杂或未知/非结构化环境 | RRT[ |
方法 基于优化的 | 将航路生成问题转化为具有明确目标函数和约束条件的数学优化问题 | 复杂优化问题的可解性与计算量消耗问题 | 满足安全约束、追求轨迹质量的场景 | 文献[ |
| 智能优化算法 | 基于自然或生物启发,通过迭代在解空间进行大量搜索,在多目标下寻优 | 计算消耗较大,实时性不强,且需要避免陷入局部极值点 | 多目标且问题非凸的全局航路规划,尤其适合离线规划 | 遗传算法、粒子群算法[ |
| 人工智能方法 | 通过学习环境特征、运动模式和策略经验,自主进行飞行决策 | 训练数据集难获取、算法可解释性问题 | 未知环境、动态威胁与强时变场景 | 强化学习[ |
表3
卫星信号拒止环境下的低空无人机安全控制
| 方法分类 | 代表性方法 | 核心思想 | 特点 | 适用场景 |
|---|---|---|---|---|
| 基于状态估计增强的方法 | 激光SLAM | 通过激光雷达扫描生成点云数据,通过点云匹配进行定位和建图 | 精度高、不受光线影响,但对点云退化场景和计算资源有要求 | 光照不足/纹理贫乏但几何结构明显场景如地下管廊、隧道等 |
| 视觉SLAM | 通过视觉传感器捕捉环境特征,分析并计算自身位置与姿态 | 硬件成本低、光照好时信息丰富,但对光线敏感 | 光照较好、有纹理特征的场景 | |
| 视觉/激光雷达与惯性测量单元(IMU)融合 | 结合视觉/激光雷达的高精度环境感知与IMU的快速运动估计 | 优势互补,稳定性与精度较高,算法设计复杂度高 | 复杂工业场景、算力与载荷资源较充足的平台 | |
| 超宽带(UWB)定位 | 通过与部署的基站之间的相对信号和飞行时间进行定位 | 定位延迟低、精度高,对基站质量要求高 | 可预先部署基站的固定场所 | |
| 视觉伺服类方法 | 基于位置的视觉伺服(PBVS) | 将图像特征通过相机模型反投影到三维空间,计算目标的位姿,生成误差信号 | 控制量物理意义明确,但需精准相机标定 | 适用于目标模型已知、标定精确且需直接控制三维位姿的任务,如精确悬停、对接与着陆 |
| 基于图像的视觉伺服(IBVS) | 直接在图像空间定义误差,通过雅克比矩阵将图像误差映射为无人机运动特征 | 对模型误差鲁棒性强,需实时计算雅克比矩阵 | 适用于特征稳定、需保持目标在视场内的近距跟踪与引导场景,对精确模型依赖较低 | |
| 混合视觉伺服(HVS) | 部分特征采用位置反馈,另一部分采用图像特征反馈 | 结合两者优点,综合性能较好,但算法设计复杂度较高 | 适用于需同时保证视场约束与位姿精度的复杂视觉引导任务 |
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