基于改进人工势场法的无人机航迹规划

doi:10.12305/j.issn.1001-506X.2021.11.31

Abstract

Abstract:

Traditional artificial potential field (TAPF) method has a large trajectory swing and falls into the local minimum point potentially. In order to solve this problem, an improved artificial potential field is proposed. Firstly, on the basis of TAPF method, the angle and speed adjustment factors are introduced to simulate a more realistic flight path of the unmanned aerial vehicle. Then, the auxiliary obstacle avoidance force is introduced to achieve obstacle avoidance while smoothing path. Finally, the simulation of the improved and TAPF method shows that the improved obstacle avoidance algorithm has a significant improvement in the smoothness of the trajectory compared with the traditional algorithm, and unmanned aerial vehicle avoids the local minimum and the local minimum point successfully.

Key words: artificial potential field (APF) method, path planning, local planning, physical constraints, smooth trajectory

CLC Number:

V249

Yao HAN, Shaohua LI. UAV path planning based on improved artificial potential field[J]. Systems Engineering and Electronics, 2021, 43(11): 3305-3311.

Figures/Tables 9

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References 31

1	KERNS A J , SHEPARD D P , BHATTI J A , et al. Unmanned aircraft capture and control via GPS spoofing[J]. Journal of Field Robotics, 2014, 31 (4): 617- 636. doi: 10.1002/rob.21513
2	SAFADINHO D , RAMOS J , RIBEIRO R , et al. UAV landing using computer vision techniques for human detection[J]. Sensors, 2020, 20 (3): 613. doi: 10.3390/s20030613
3	高升, 艾剑良, 王之豪. 混合种群RRT无人机航迹规划方法[J]. 系统工程与电子技术, 2020, 42 (1): 101- 107.
	GAO S , AI J L , WANG Z H . Mixed population RRT algorithm for UAV path planning[J]. Systems Engineering and Electronics, 2020, 42 (1): 101- 107.
4	ZHANG Z , LI J X , WANG J . Sequential convex programming for nonlinear optimal control problem in UAV path planning[J]. Aerospace Science & Technology, 2018, 76 (1): 280- 290.
5	ZHENG C W , LI L , XU F J , et al. Evolutionary route planner for unmanned air vehicles[J]. IEEE Trans. on Robotics, 2005, 21 (4): 609- 620. doi: 10.1109/TRO.2005.844684
6	王琼, 刘美万, 任伟建, 等. 无人机航迹规划常用算法综述[J]. 吉林大学学报(信息科学版), 2019, 37 (1): 58- 67. doi: 10.3969/j.issn.1671-5896.2019.01.008
	WANG Q , LIU M W , REN W J , et al. Overview of common algorithms for UAV path planning[J]. Journal of Jilin University (Information Science Edition), 2019, 37 (1): 58- 67. doi: 10.3969/j.issn.1671-5896.2019.01.008
7	SARAVANAKUMAR S , ASOKAN T . Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space[J]. Intelligent Service Robotics, 2013, 6 (4): 211- 224. doi: 10.1007/s11370-013-0138-2
8	KUMAR P B , RAWAT H , PARHI D R . Path planning of humanoids based on artificial potential field method in unknown environments[J]. Expert Systems, 2019, 36 (2): 1- 12.
9	YAO Q F , ZHENG Z Y , QI L , et al. Path planning method with improved artificial potential field — a reinforcement learning perspective[J]. IEEE Access, 2020, 8, 135513- 135523. doi: 10.1109/ACCESS.2020.3011211
10	CHEN P Y , SHEN P F , ZHANG P , et al. Path planning of underwater terrain-aided navigation based on improved artificial potential field method[J]. Marine Technology Society Journal, 2019, 53 (2): 65- 74. doi: 10.4031/MTSJ.53.2.7
11	SINGH Y , SHARMA S , SUTTON R , et al. A constrained A^* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents[J]. Ocean Engineering, 2018, 169 (9): 187- 201.
12	SONG R , LIU Y C , BUCKNALL R . Smoothed A^* algorithm for practical unmanned surface vehicle path planning[J]. Applied Ocean Research, 2019, 83, 9- 20. doi: 10.1016/j.apor.2018.12.001
13	BEHNCK L P , DOERING D , PEREIRACE , et al. A modified simulated annealing algorithm for SUAVs path planning[J]. IFAC PapersOnLine, 2015, 48 (10): 63- 68. doi: 10.1016/j.ifacol.2015.08.109
14	GAO W X , TANG Q , YE B F , et al. An enhanced heuristic ant colony optimization for mobile robot path planning[J]. Soft Computing, 2020, 24 (8): 6139- 6150. doi: 10.1007/s00500-020-04749-3
15	PANDEY P , SHUKLA A , TIWARI R . Three-dimensional path planning for unmanned aerial vehicles using glowworm swarm optimization algorithm[J]. International Journal of System Assurance Engineering and Management, 2018, 9 (4): 836- 852.
16	ROBERGE V , TARBOUCHI M , LABONTE G . Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning[J]. IEEE Trans. on Industrial Informatics, 2013, 9 (1): 132- 141. doi: 10.1109/TII.2012.2198665
17	XIN J F , ZHONG J B , YANG F R , et al. An improved genetic algorithm for path-planning of unmanned surface vehicle[J]. Sensors, 2019, 19 (11): 2640. doi: 10.3390/s19112640
18	LAMINI C , BENHLIMA S , ELBEKRI A . Genetic algorithm based approach for autonomous mobile robot path planning[J]. Procedia Computer Science, 2018, 127 (1): 180- 189.
19	KHATIB O . Real-time obstacle avoidance for manipulators and mobile robots[J]. International Journal of Robotics Research, 1986, 5 (1): 90- 98. doi: 10.1177/027836498600500106
20	MABROUK M H , MCINNES C R . Solving the potential field local minimum problem using internal agent states[J]. Robotics and Autonomous Systems, 2018, 56 (12): 1050- 1060.
21	ZHU Y , ZHANG T , SONG J Y . Study on the local minima problem of path planning using potential field method in unknown environments[J]. Acta Automatica Sinica, 2010, 36 (8): 1122- 1130. doi: 10.3724/SP.J.1004.2010.01122
22	韩知玖, 吴文江, 李孝伟, 等. 一种改进的动力学约束人工势场法[J]. 上海大学学报(自然科学版), 2019, 25 (6): 879- 887.
	HAN Z J , WU W J , LI X W , et al. An improved artificial potential field method constrained by a dynamic model[J]. Journal of Shanghai University (Natural Science), 2019, 25 (6): 879- 887.
23	梁献霞, 刘朝英, 宋雪玲, 等. 改进人工势场法的移动机器人路径规划研究[J]. 计算机仿真, 2018, 35 (4): 291- 294, 361. doi: 10.3969/j.issn.1006-9348.2018.04.063
	LIANG X X , LIU C Y , SONG X L , et al. Research on improved artificial potential field approach in local path planning for mobile robot[J]. Computer Simulation, 2018, 35 (4): 291- 294, 361. doi: 10.3969/j.issn.1006-9348.2018.04.063
24	LADDHA A, KOCAMAZ M K, NAVARRO L E, et al. Map-supervised road detection[C]//Proc. of the IEEE Intelligent Vehicles Symposium, 2016: 118-123.
25	CHEN Y B , LUO G C , MEI Y S , et al. UAV path planning using artificial potential field method updated by optimal control theory[J]. International Journal of Systems Science, 2016, 47 (6): 1407- 1420. doi: 10.1080/00207721.2014.929191
26	王强, 张安, 吴忠杰. 改进人工势场法与模拟退火算法的无人机航路规划[J]. 火力与指挥控制, 2014, (8): 70- 73. doi: 10.3969/j.issn.1002-0640.2014.08.017
	WANG Q , ZHANG A , WU Z J . UAV route planning based on improved artificial potential field method and simulated annealing algorithm[J]. Fire Control and Command Control, 2014, (8): 70- 73. doi: 10.3969/j.issn.1002-0640.2014.08.017
27	XU J , PARK K S . A real-time path planning algorithm for cable-driven parallel robots in dynamic environment based on artificial potential guided RRT[J]. Microsystem Technologies, 2020, 26 (11): 3533- 3546. doi: 10.1007/s00542-020-04948-w
28	PARK S O , LEE M C , KIM J . Trajectory planning with collision avoidance for redundant robots using jacobian and artificial potential field-based real-time inverse kinematics[J]. International Journal of Control, Automation and Systems, 2020, 18 (8): 2095- 2107. doi: 10.1007/s12555-019-0076-7
29	ZHA M , WANG Z W , FENG J , et al. Unmanned vehicle route planning based on improved artificial potential field method[J]. Journal of physics: Conference series, 2020, 1453 (1): 012059. doi: 10.1088/1742-6596/1453/1/012059/pdf
30	SONG J , HAO C , SU J C . Path planning for unmanned surface vehicle based on predictive artificial potential field[J]. International Journal of Advanced Robotic Systems, 2020, 17 (2): 1- 13.
31	程志, 张志安, 李金芝, 等. 改进人工势场法的移动机器人路径规划[J]. 计算机工程与应用, 2019, 55 (23): 29- 34. doi: 10.3778/j.issn.1002-8331.1904-0472
	CHENG Z , ZHANG Z A , LI J Z , et al. Mobile robots path planning based on improved artificial potential field[J]. Computer Engineering and Applications, 2019, 55 (23): 29- 34. doi: 10.3778/j.issn.1002-8331.1904-0472

参数	值
起始点	(36, 36)
目标点	(2, 2)
引力系数K_a	5
斥力系数K_r	200
障碍物斥力范围d_o/m	2r_o
最小速度V_min/(m/s)	0.5
常规速度V_c/(m/s)	0.8
最大速度V_max/(m/s)	1
最大转向角Δθ_max/(°)	30
角度阈值θ₁/(°)	10
角度阈值θ₂/(°)	20

序号	最大转向角/(°)/轨迹长度/m/飞行时间/s
序号	TAPF	A-APF	IAPF
1	-356.18/124/155	-347.41/115.2/128	30/109.7/125
2	147.63/112.8/141	98.54/107.1/119	30/105.7/113
3	303.97/125.6/157	61.35/111.6/124	30/108.7/125
4	153.1/123.2/154	89.57/115.2/128	30/111.4/125
5	-297.73/114.4/143	-343.32/107.1/119	20.53/105.8/113
6	327.7/121.6/152	336.58/112.5/125	30/109.4/130
7	142.64/116/145	48.8/108/120	30/106.2/116

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UAV path planning based on improved artificial potential field

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