三维地形风场环境下无人机全覆盖路径规划

doi:10.12305/j.issn.1001-506X.2025.07.27

摘要/Abstract

摘要：

针对旋翼无人机(unmanned aerial vehicle, UAV)采用往复全覆盖路径在三维地形和风场环境中面临的能量非最优问题, 提出一种风场地形自适应全覆盖路径规划算法。首先, 通过栅格法进行环境建模, 根据栅格内的高程数据点信息构建空中航路点。接着，通过分析旋翼UAV在风场环境中的机动动作和能量函数构建UAV风中模型及能量消耗模型。然后，基于滚动优化和全局思想提出一种邻点滚动试探-全局预测融合策略作为算法核心, 规划出UAV顺应风场与起伏地形能量近似最优全覆盖路径。最后, 在不同环境下进行仿真实验。实验结果表明考虑风场和地形的必要性, 同时相较于单一往复式覆盖, 所提算法能自适应环境因素进行规划从而显著减少全覆盖过程中的能量消耗。

关键词: 旋翼无人机, 路径规划, 全覆盖, 风场, 三维地形

Abstract:

A wind field terrain adaptive full coverage path planning algorithm is proposed to address the energy non-optimal problem faced by rotor unmanned aerial vehicle(UAV) using back-and-forth full coverage paths in three-dimensional terrain and wind field environment. Firstly, the environment is modeled using the grid method, and aerial waypoints are constructed based on the elevation data point information within the grid. Next, by analyzing the maneuvering actions and energy functions of the rotary wing UAV in the wind field environment, a wind model and energy consumption model of the UAV are constructed. Then, based on rolling optimization and global thinking, a fusion strategy of neighbor rolling probing and global prediction is proposed as the algorithm core to plan the UAV approximate optimal full coverage path that adapts to the wind field and undulating terrain energy. Finally, simulation experiments is conducted in different environments. The experimental results indicate the necessity of considering wind field and terrain, and compared to single reciprocating coverage, the proposed algorithm can adapt to environmental factors for planning, significantly reducing energy consumption during the full coverage process.

Key words: unmanned aerial vehicle (UAV), path planning, full coverage, wind field, three-dimensional terrain

中图分类号:

V279

唐俊超, 胡春鹤. 三维地形风场环境下无人机全覆盖路径规划[J]. 系统工程与电子技术, 2025, 47(7): 2349-2356.

Junchao TANG, Chunhe HU. Complete coverage path planning for UAVs in 3D terrain and wind field environment[J]. Systems Engineering and Electronics, 2025, 47(7): 2349-2356.

图/表 7

图1

图2

图3

图4

表1

图5

图6

参考文献 34

1	XIANG H Y , HAN Y H , PAN N , et al. Study on multi-UAV cooperative path planning for complex patrol tasks in large cities[J]. Drones, 2023, 7 (6): 367.
2	SUI H G , ZHANG H , GOU G H , et al. Multi-UAV cooperative and continuous path planning for high-resolution 3D scene reconstruction[J]. Drones, 2023, 7 (9): 544.
3	PRATAP T , JOSHUA V H , DAVID M , et al. Sensor planning for a symbiotic UAV and UGV system for precision agriculture[J]. IEEE Trans.on Robotics, 2016, 32 (6): 1498- 1511.
4	FRANCESCO N , DIOGO D , ANNE S , et al. Towards real-time building damage mapping with low-cost UAV solutions[J]. Remote Sensing, 2019, 11 (3): 287.
5	CABREIRA M T , BRISOLARA B L , FERREIRA-JR P R . Survey on coverage path planning with unmanned aerial vehicles[J]. Drones, 2019, 3 (1): 4.
6	张世勇, 张雪波, 苑晶, 等. 旋翼无人机环境覆盖与探索规划方法综述[J]. 控制与决策, 2022, 37 (3): 513- 529.
	ZHANG S Y , ZHANG X B , YUAN J , et al. A survey on cove-rage and exploration path planning with multi-rotor micro aerial vehicles[J]. Control and Decision, 2022, 37 (3): 513- 529.
7	FEVGAS G , LAGKAS T , ARGYRIOU V , et al. Coverage path planning methods focusing on energy efficient and cooperative strategies for unmanned aerial vehicles[J]. Sensors, 2022, 22 (3): 1235.
8	AMNA K , IRAM N , HYEJEONG R , et al. Online complete coverage path planning using two-way proximity search[J]. Intelligent Service Robotics, 2017, 10 (3): 229- 240.
9	VASQUEZ-GOMEZ J I , MARCIANO-MELCHOR M , VAL ENTIN L , et al. Coverage path planning for 2D convex regions[J]. Journal of Intelligent & Robotic Systems, 2020, 97 (99): 81- 94.
10	TAUA M C , CARMELO D F , PAULO R F J , et al. Energy-aware spiral coverage path planning for UAV photogrammetric applications[J]. Computer Science, 2018, 3 (4): 3662- 3668.
11	YAN L , HAI C , MENG J E , et al. Coverage path planning for UAVs based on enhanced exact cellular decomposition method[J]. Mechatronics, 2011, 21 (5): 876- 885.
12	MARINA T , DAVID A P , JOSÉ L V , et al. Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction[J]. Expert Systems with Applications, 2016, 55 (C): 441- 451.
13	王红星, 马学娇, 张长森. 一种凹多边形区域的无人机覆盖路径规划算法[J]. 航空兵器, 2021, 28 (6): 46- 52.
	WANG H X , MA X J , ZHANG C S . An algorithm of coverage path planning for UAV in concave polygon area[J]. Aero Weaponry, 2021, 28 (6): 46- 52.
14	GHADDAR A , MEREI A , NATALIZIO E . PPS: energy-aware grid-based coverage path planning for UAVs using area partitioning in the presence of NFZs[J]. Sensors, 2020, 20 (13): 3742.
15	吴靖宇, 朱世强, 宋伟, 等. 基于改进单元分解法的全覆盖路径规划[J]. 系统工程与电子技术, 2023, 45 (12): 3949- 3957. doi: 10.12305/j.issn.1001-506X.2023.12.25
	WU J Y , ZHU S Q , SONG W , et al. Coverage path planning based on improved cellular decomposition[J]. System Engineering and Electronics, 2023, 45 (12): 3949- 3957. doi: 10.12305/j.issn.1001-506X.2023.12.25
16	DAI R , FOTEDAR S , RADMANESH M , et al. Quality-aware UAV coverage and path planning in geometrically complex environments[J]. Ad Hoc Networks, 2018, 73, 95- 105.
17	GONG Y G , CHEN K , NIU T Y , et al. Grid-based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT[J]. Journal of Cloud Computing, 2022, 11, 29. doi: 10.1186/s13677-022-00298-2
18	THEILE M, BAYERLEIN H, NAI R, et al. UAV coverage path planning under varying power constraints using deep reinforcement learning[C]//Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020: 1444-1449.
19	THEILE M, BAYERLEIN H, NAI R, et al. UAV path planning using global and local map information with deep reinforcement learning[C]//Proc. of the 20th International Conference on Advanced Robotics, 2021: 539-546.
20	THEILE M, BAYERLEIN H, CACCAMO M, et al. Learning to recharge: UAV coverage path planning through deep reinforcement learning[EB/OL]. [2024-02-05]. https://arxiv.org/abs/2309.03157.
21	王宇, 陈海涛, 李海川. 基于引力搜索算法的植保无人机三维路径规划方法[J]. 农业机械学报, 2018, 49 (2): 28-33, 21.
	WANG Y , CHEN H T , LI H C . 3D path planning approach based on gravitational search algorithm for sprayer UAV[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49 (2): 28-33, 21.
22	WANG H P, LI H Z, ZHANG C, et al. A 3D coverage path planning approach for flying cameras in nature environment under photogrammetric constraints[C]//Proc. of the 36th Chinese Control Conference, 2017: 6761-6766.
23	WANG H P , ZHANG S Y , ZHANG X Y , et al. Near-optimal 3-D visual coverage for quadrotor unmanned aerial vehicles under photogrammetric constraints[J]. IEEE Trans.on Industrial Electronics, 2021, 69 (2): 1694- 1704.
24	BIALAS J, DOLLER M. Coverage path planning for unmanned aerial vehicles in complex 3D environments with deep reinforcement learning[C]//Proc. of the IEEE International Conference on Robotics and Biomimetics, 2022: 1080-1085.
25	BIALAS J, DOELLER M, KATHREIN R. Robust multi- agent coverage path planning for unmanned airial vehicles (UAVs) in complex 3D environments with deep reinforcement learning[C]// Proc. of the IEEE International Conference on Robotics and Biomimetics, 2023.
26	COOMBES M, CHEN W H, LIU C. Boustrophedon coverage path planning for UAV aerial surveys in wind[C]//Proc. of the IEEE International Conference on Unmanned Aircraft Systems, 2017: 1563-1571.
27	COOMBES M , FLETCHER T , CHEN W , et al. Optimal polygon decomposition for UAV survey coverage path planning in wind[J]. Sensors, 2018, 18 (7): 2132.
28	VASQUEZ J I, GOMEZ C, DE-COTE E M, et al. Multirotor UAV coverage planning under wind conditions[C]//Proc. of the International Conference on Mechatronics, Electronics and Automotive Engineering, 2016: 32-37.
29	QIAN L , LIU H H T . Path-following control of a quadrotor UAV with a cable-suspended payload under wind disturbances[J]. IEEE Trans.on Industrial Electronics, 2019, 67 (3): 2021- 2029.
30	PU O , YUAN B Q , LI Z N , et al. Research on wind field visualization based on UAV wind measurement method[J]. Measurement Science and Technology, 2023, 35 (2): 025801.
31	DI F C , BUTTAZZO G . Coverage path planning for UAVs photogrammetry with energy and resolution constraints[J]. Journal of Intelligent & Robotic Systems, 2016, 83 (3/4): 445- 462.
32	齐立哲, 华中伟, 苏昊, 等. 面向荒漠复杂地形的机器人在线全覆盖路径规划方法[J]. 控制与决策, 2024, 39 (4): 1095- 1103.
	QI L Z , HUA Z W , SU H , et al. Robot online fully coverage path planning algorithm for desert complex terrain[J]. Control and Decision, 2024, 39 (4): 1095- 1103.
33	阮贵航, 陈教料, 胥芳. 基于滚动优化和分散捕食者猎物模型的全覆盖路径规划算法[J]. 控制与决策, 2023, 38 (9): 2545- 2553.
	RUAN G H , CHEN J L , XU F . Complete coverage path planning algorithm based on rolling optimization and decentralized predator-prey model[J]. Control and Decision, 2023, 38 (9): 2545- 2553.
34	CABREIRA T M, FERREIRA P R, DI F C, et al. Grid-based coverage path planning with minimum energy over irregular-shaped areas with UAVs[C]//Proc. of the IEEE International Conference on Unmanned Aircraft Systems, 2019: 758-767.

环境风	算法	路径长度/m	平均功耗/W	能量消耗(×10⁴)/J	覆盖率/%
西风	单一往复式^[21]	1 130	232.7	4.381	100
	EG-CPP^[34]	1 126	292.7	4.864	100
	本文算法	1 130	232.7	4.381	100
西南风	单一往复式	1 130	423.2	7.119	100
	EG-CPP	1 331	299.3	6.366	100
	本文算法	1 324	297.8	6.320	100
南风	单一往复式	1 130	381.3	5.737	100
	EG-CPP	1 126	278.7	4.740	100
	本文算法	1 127	238.2	4.418	100
西北风	单一往复式	1 130	424.1	6.726	100
	EG-CPP	1 278	322.4	6.263	100
	本文算法	1 351	281.9	5.899	100

[1]	焉晓贞, 周新悦, 罗清华. 改进A-star算法的无人船动态路径规划[J]. 系统工程与电子技术, 2025, 47(7): 2314-2328.
[2]	刘伊婕, 姜斌, 马亚杰, 李文博, 刘成瑞. 无人艇编队避碰路径规划与重规划[J]. 系统工程与电子技术, 2025, 47(6): 1964-1974.
[3]	陈威, 王从庆, 曾强, 李战. 面向飞机表面视觉检查的无人机覆盖路径规划[J]. 系统工程与电子技术, 2025, 47(4): 1206-1213.
[4]	耿泽, 黄炎焱, 张寒. 基于火炮转移路径预测的无人机集群反炮兵搜索路径规划[J]. 系统工程与电子技术, 2025, 47(4): 1222-1234.
[5]	夏雨奇, 黄炎焱, 陈恰. 基于深度Q网络的无人车侦察路径规划[J]. 系统工程与电子技术, 2024, 46(9): 3070-3081.
[6]	费博雯, 包卫东, 刘大千, 朱晓敏. 面向动态目标搜索与打击的空地协同自主任务分配方法[J]. 系统工程与电子技术, 2024, 46(7): 2346-2358.
[7]	李杰, 谭跃进. 基于集成改进蚁群算法的作战环推荐方法[J]. 系统工程与电子技术, 2024, 46(6): 2002-2012.
[8]	孙家玮, 余明晖, 杨大鹏, 汤皓泉, 卞大鹏. 基于CL-RRT与MPC的舰载机牵引系统路径规划[J]. 系统工程与电子技术, 2024, 46(5): 1745-1755.
[9]	隋东, 杨振宇, 丁松滨, 周婷婷. 基于EMSDBO算法的无人机三维航迹规划[J]. 系统工程与电子技术, 2024, 46(5): 1756-1766.
[10]	刘钢, 安志镖, 张茂军, 刘煜, 李武. 基于连续路网环境的实体化主体路径规划算法[J]. 系统工程与电子技术, 2024, 46(4): 1346-1356.
[11]	赵贵祥, 周健, 李云淼, 王晨旭. 改进双向快速搜索随机树的无人艇路径规划[J]. 系统工程与电子技术, 2024, 46(4): 1364-1371.
[12]	桂洋, 郑柏超, 高鹏. 基于NESO-LFDC的四旋翼无人机滑模姿态控制[J]. 系统工程与电子技术, 2024, 46(3): 1075-1083.
[13]	杨平, 肖兵, 陈新, 唐璐琪. 多约束条件下战斗机三维路径规划问题[J]. 系统工程与电子技术, 2024, 46(12): 4213-4221.
[14]	吴尹菲, 李新凯, 张宏立, 陈颖颖, 龚丰金. 基于弹性面域特性的虚拟管道优化与设计[J]. 系统工程与电子技术, 2024, 46(11): 3862-3873.
[15]	唐恒, 孙伟, 吕磊, 贺若飞, 吴建军, 孙昌浩, 孙田野. 融合动态奖励策略的无人机编队路径规划方法[J]. 系统工程与电子技术, 2024, 46(10): 3506-3518.