Research progress and challenges faced by unmanned aerial vehicles in complex underground spaces
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摘要: 分析了地下复杂空间无人机的技术发展与应用现状，指出地下复杂空间无人机面临单体性能不足、环境态势感知与自主导航能力有限、编队协同能力有限等问题，针对上述问题，展望了地下无人机关键技术发展趋势：① 小型化轻量化一体化无人机设计技术。通过改进无人机的机械结构，提高激光雷达、深度相机等信息感知传感器与控制系统的集成度，优化电源管理系统等，最终实现单体无人机巡航速度、续航时间等性能的提升；② GPS拒止环境下态势感知与自主导航技术。攻克即时定位与地图构建（SLAM）导航与实时路径规划等关键技术难题，围绕特定场景逐步突破算法的局限性，提升无人系统的感知能力、环境适应性和鲁棒性；③ 有限信息下编队协同控制技术。攻克异构/同构无人机集群协同、复杂信道环境下的无线通信等技术难题，通过优化无人机群体智能控制策略、信息交互机制及任务决策协同机制等，增强集群无人系统的鲁棒性，提高无人系统在地下复杂环境中的自适应能力，进而提升无人系统的任务执行效率与成功率。Abstract: The technological development and application status of underground complex space UAVs are analyzed. It is pointed out that underground complex space UAVs face problems such as insufficient individual performance, limited environmental situational awareness and autonomous navigation capabilities, and limited formation collaboration capabilities. In order to solve the above problems, the development trends of key technologies for underground UAVs are prospected. ① Small and lightweight integrated UAV design technology is proposed. By improving the mechanical structure of the UAV, improving the integration of information perception sensors such as LiDAR and depth camera with control systems, and optimizing power management systems, the ultimate goal is to improve the cruise speed, endurance time, and other performance of individual UAV. ② Situation awareness and autonomous navigation technology in GPS rejection environment is proposed. The key technical challenges such as simultaneous localization and mapping (SLAM) navigation and real-time path planning should be overcome. The limitations of algorithms around specific scenarios should be gradually broken through. The perception capability, environmental adaptability, and robustness of unmanned systems should be improved. ③ Formation collaboration control technology under limited information is proposed. The technical problems such as heterogeneous/isomorphic UAV cluster collaboration, and wireless communication in complex channel environments should be overcome. By optimizing UAV swarm intelligence control strategies, information interaction mechanisms, and task decision-making collaboration mechanisms, the robustness of clustered unmanned systems should be enhanced. The adaptability of unmanned systems in complex underground environments should be improved. Furthermore, the task execution efficiency and success rate of unmanned systems should be improved.
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图 1 地下无人机技术发展与应用框架
Figure 1. Development and application framework of underground unmanned aerial vehicle technology
表 1 国内外关键技术研究现状总结
Table 1. Summary of the research status of key technologies at home and abroad
研究团队/机构 研究方向及代表算法 存在的问题 浙江大学FAST实验室 研究方向主要集中在无人机运动规划。
针对微小型无人机，提出了一种无需距离场的基于梯度的轨迹规划算法，EGO−Planner偏向理论研究，示范与应用验证较少，算法工程适用性仍需验证 天津大学无人系统自主导航与控制实验室 研究方向主要集中在多无人机集群控制与路径规划。针对多无人机协同系统，提出了基于地图匹配及全局路图的多无人机协同定位及协同调度算法 实际应用场景仅限于无GPS森林、无矿洞和隧道的示范与应用，算法工程适用性仍需验证 香港科技大学空中
机器人研究组研究组与大疆创新科技有限公司建立联合实验室，倾向于使用优化的思路去解决无人机自主飞行中的工程问题，研究方向主要集中在无人机状态估计、建图、运动规划等。
针对深度视觉定位无人机，提出了一种鲁棒且通用的单目视觉惯性状态估计器，VINS Mono无人机控制算法相关的研究较少，示范与应用验证较少 香港大学MaRS实验室 研究方向主要是空中机器人设计、规划和控制，以及基于激光雷达的SLAM。
针对激光雷达定位无人机，提出了一种计算高效且鲁棒的LiDAR惯性里程计框架，FAST−LIO算法路径规划与控制算法相关的研究较少，示范与应用验证较少 卡内基梅隆大学
机器人研究所主要研究方向为导航拒止环境下自主导航和路径规划算法，提出了多种路径规划算法，如Falco、自主探索的方法、TARE、FAR Planner 等 感知定位和鲁棒控制算法相关的研究较少，示范与应用验证较少，算法工程适用性仍需验证 
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表 2 仿真环境
Table 2. Simulation environment
序号 环境 特征 1 校园环境
(340 m×340 m)包含一些上下坡及盘绕的地形 2 室内环境
(130 m×100 m)包含长且窄的走廊及许多桌子/椅子等障碍物，其中还有一个护栏，由于其中间可以穿透的特性，会对机器人的感知（perception）模块增加挑战性 3 停车场
(140 m×130 m,5层)包含多层楼且有上下坡，会给机器人3D导航任务增加难度 4 隧道(330 m×250 m) 由错综复杂的隧道构成的一个庞大的网格结构 5 森林(150 m×150 m) 包含无规律分布的树木及几栋房子
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