Trajectory Planning and Tracking for Multi-quadcopter in Dynamic Obstacle Environments

doi:10.16182/j.issn1004731x.joss.24-0893

Abstract

Abstract:

Multi-quadrotor UAV systems face challenges when performing complex tasks in dynamic obstacle environments. Therefore, a comprehensive particle swarm optimization-based task allocation method, a trajectory planning integrating the traditional Informed-RRT* and A* algorithms, and a trajectory tracking method based on model predictive control were designed.The multi-quadrotor task allocation problem was constructed as a classical multiple traveling salesman problem, and then, the particle swarm optimization was used to assign the task to multi-quadrotor UAVs. A fusion algorithm that combined the advantages of traditional Informed-RRT* and A* algorithms for quadrotor UAV trajectory planning was designed, so as to ensure that a safe and effective trajectory was planned for each UAV of the multi-quadrotor UAV system in dynamic obstacle environments. The tracking controller was designed using the model predictive control approach to ensure that the quadcopter UAV can accurately track the online planning trajectories in real time and avoid collisions with dynamic obstacles. The simulation experiments verify the effectiveness of the proposed method.

Key words: quadcopter unmanned aerial vehicle, dynamic obstacle, task allocation, trajectory planning, tracking control

CLC Number:

TP391.9

Jiang Haosheng, Wu Fangfang, Huang Zexian, Ma Ziyue, Dong Chunyun, Ping Xubin. Trajectory Planning and Tracking for Multi-quadcopter in Dynamic Obstacle Environments[J]. Journal of System Simulation, 2025, 37(8): 2089-2102.

Figures/Tables 10

Fig. 1

Fig. 2

Fig. 3

Table 1

Parameters of Flame Wheel 450 UAV

参数	数值
质量/kg	1.4
电机响应时间常数	0.02
机身半径/m	0.022 5
速度常数	-141.4
螺旋桨拉力系数	$1.105 × 10 - 5$
螺旋桨扭矩系数	$1.779 × 10 - 7$
x轴的旋转惯量	0.021 1
y轴的旋转惯量	0.021 1
z轴的旋转惯量	0.036 6
螺旋桨的最大转速/(r/min)	960

Table 1

Table 2

Table 3

Fig. 4

Table 4

Table 5

Fig. 5

References 22

[1]	贾永楠, 田似营, 李擎. 无人机集群研究进展综述[J]. 航空学报, 2020, 41(增1): 1-11.
	Jia Yongnan, Tian Siying, Li Qing. Recent Development of Unmanned Aerial Vehicle Swarms[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(S1): 1-11.
[2]	Cheikhrouhou Omar, Khoufi Ines. A Comprehensive Survey on the Multiple Traveling Salesman Problem: Applications, Approaches and Taxonomy[J]. Computer Science Review, 2021, 40: 100369.
[3]	Chen Xinye, Zhang Ping, Du Guanglong, et al. Ant Colony Optimization Based Memetic Algorithm to Solve Bi-objective Multiple Traveling Salesmen Problem for Multi-robot Systems[J]. IEEE Access, 2018, 6: 21745-21757.
[4]	Al-Taani Ahmad T, Majdalawi Hala S, Bsoul Abedalraoof K. Solving the Multiple Travelling Salesman Problem using Gaussian Mixture Model and Artificial Neural Network[C]//2023 24th International Arab Conference on Information Technology (ACIT). Piscataway: IEEE, 2023: 1-5.
[5]	Zhou Shaolei, Yin Gaoyang, Wu Qingpo. UAV Cooperative Multiple Task Assignment Based on Discrete Particle Swarm Optimization[C]//2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics. Piscataway: IEEE, 2015: 81-86.
[6]	赵乃刚, 邓景顺. 粒子群优化算法综述[J]. 科技创新导报, 2015, 12(26): 216-217.
[7]	樊娇, 雷涛, 韩伟, 等. 无人机航迹规划技术研究综述[J]. 郑州大学学报(工学版), 2021, 42(3): 39-46.
	Fan Jiao, Lei Tao, Han Wei, et al. A Survey of UAV Path Planning[J]. Journal of Zhengzhou University(Engineering Science), 2021, 42(3): 39-46.
[8]	姜月秋, 李紫嫣, 关启学, 等. 基于改进A^*算法的无人机路径规划研究[J]. 兵器装备工程学报, 2020, 41(9): 160-164.
	Jiang Yueqiu, Li Ziyan, Guan Qixue, et al. Research on UAV Path Planning Based on Improved A^* Algorithms[J]. Journal of Ordnance Equipment Engineering, 2020, 41(9): 160-164.
[9]	马云红, 张恒, 齐乐融, 等. 基于改进A^*算法的三维无人机路径规划[J]. 电光与控制, 2019, 26(10): 22-25.
	Ma Yunhong, Zhang Heng, Qi Lerong, et al. A 3D UAV Path Planning Method Based on Improved A^* Algorithm[J]. Electronics Optics & Control, 2019, 26(10): 22-25.
[10]	任鹏博, 董泽华. 基于RRT算法的无人机路径规划应用研究[J]. 现代导航, 2022, 13(1): 62-66.
	Ren Pengbo, Dong Zehua. Research on UAV Path Planning Application Based on RRT Algorithm[J]. Modern Navigation, 2022, 13(1): 62-66.
[11]	李克玉, 陆永耕, 鲍世通, 等. 基于改进RRT算法的无人机三维避障规划[J]. 计算机仿真, 2021, 38(8): 59-63, 96.
	Li Keyu, Lu Yonggeng, Bao Shitong, et al. Three-dimensional Obstacle Avoidance Planning of UAV Based on Pre-planning Path Optimizing RRT Algorithms[J]. Computer Simulation, 2021, 38(8): 59-63, 96.
[12]	刘文倩, 单梁, 张伟龙, 等. 复杂环境下基于改进Informed RRT^*的无人机路径规划算法[J]. 上海交通大学学报, 2024, 58(4): 511-524.
	Liu Wenqian, Shan Liang, Zhang Weilong, et al. Unmanned Aerial Vehicle Path Planning Algorithm Based on Improved Informed RRT^* in Complex Environment[J]. Journal of Shanghai Jiao Tong University, 2024, 58(4): 511-524.
[13]	盛春红, 范珈铭. 改进APF-informed-RRT^∗融合算法的无人机航迹规划[J]. 电光与控制, 2023, 30(6): 1-7.
	Sheng Chunhong, Fan Jiaming. Improved APF-informed-RRT^* Fusion Algorithm for UAV Trajectory Planning[J]. Electronics Optics & Control, 2023, 30(6): 1-7.
[14]	毛晨悦, 吴鹏勇. 基于人工势场法的无人机路径规划避障算法[J]. 电子科技, 2019, 32(7): 65-70.
	Mao Chenyue, Wu Pengyong. UAV Path Planning Obstacle Avoidance Algorithm Based on Artificial Potential Field Method[J]. Electronic Science and Technology, 2019, 32(7): 65-70.
[15]	危怡然, 吴碧, 邓宏彬, 等. 基于改进人工势场法和自适应神经网络的共轴双旋翼无人机避障飞行控制[J]. 信息与控制, 2023, 52(2): 154-165.
	Wei Yiran, Wu Bi, Deng Hongbin, et al. Obstacle Avoidance and Flight Control of Coaxial Rotor UAV Based on Improved Artificial Potential Field Method and Adaptive Neural Network[J]. Information and Control, 2023, 52(2): 154-165.
[16]	黄志锋, 刘媛华. 基于改进狮群算法的城市无人机低空路径规划[J]. 信息与控制, 2023, 52(6): 747-757, 772.
	Huang Zhifeng, Liu Yuanhua. Low Altitude Path Planning of Urban UAV Based on Improved Lion Swarm Optimization[J]. Information and Control, 2023, 52(6): 747-757, 772.
[17]	Li Yibing, Zhang Sitong, Ye Fang, et al. A UAV Path Planning Method Based on Deep Reinforcement Learning[C]//2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium). Piscataway: IEEE, 2020: 93-94.
[18]	He Yu, Chen Shengyong. Advances in Sensing and Processing Methods for Three-dimensional Robot Vision[J]. International Journal of Advanced Robotic Systems, 2018, 2018(3): 1729881418760623.
[19]	郭婕, 金海, 沈昕格. 基于神经网络PID算法的四旋翼无人机优化控制[J]. 电子科技, 2021, 34(10): 51-55.
	Guo Jie, Jin Hai, Shen Xinge. Optimization Control of Quadrotor UAVs Based on Neural Network PID Algorithm[J]. Electronic Science and Technology, 2021, 34(10): 51-55.
[20]	Jiang Min. Application of Fuzzy PID Control in UAV Control System[C]//2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). Piscataway: IEEE, 2021: 197-200.
[21]	Huo Da, Dai Li, Chai Runqi, et al. Collision-free Model Predictive Trajectory Tracking Control for UAVs in Obstacle Environment[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(3): 2920-2932.
[22]	Zhu Dandan, Sun Junqing. A New Algorithm Based on Dijkstra for Vehicle Path Planning Considering Intersection Attribute[J]. IEEE Access, 2021, 9: 19761-19775.

场景类别	障碍物类型	球心 (底面中心)坐标	半径/m	长/m	宽/m	高/m
Ⅰ	圆柱体	(4, 13, 0)	2			23
	圆柱体	(15, 13, 0)	3			23
	长方体	(8.5, 5.5, 0)		7	3	23
	长方体	(10, 19.5, 0)		10	3	23
Ⅱ	圆柱体	(16, 16, 0)	2			23
	圆柱体	(9, 13, 0)	2			23
	长方体	(5, 4, 0)		7	3	23
	球体	(2, 4, 3)	2
	球体	(14, 20, 13)	2
Ⅲ	圆柱体	(4, 13, 0)	2			23
	长方体	(8.5, 17, 0)		8	3	23
	长方体	(17.5, 13, 0)		7	2	23

场景类别	障碍物类型	初始球心 (底面中心)坐标	半径/m	(长，宽，高)/m	运动范围/m	移动形式	移动速度/(m/s)
Ⅰ	球体	(3, 3, 3)	2		[2, 18]	垂直往返	1.5
	球体	(16, 6, 10)	2.5		[0, 16]	垂直往返	1.5
	圆柱体	(8, 13, 0)	1.5	(-, -, 23)
Ⅱ	球体	(10, 17, 5)	2		[13, 18]	垂直往返	1.5
Ⅱ	球体	(4, 9, 7)	2		[3, 8]	沿x轴往返	1.5
Ⅲ	球体	(16, 8, 8)	2.5		[8, 15]	垂直往返	1.5
	球体	(15, 17, 18)	1.5		[15, 22]	沿x轴往返	1.5
	长方体	(6.5, 5, 0)		(5, 2, 23)

场景类别	障碍物类型	初始球心 (底面中心)坐标	半径/m	高/m
I	球体	(5, 1, 2)	1
I	圆柱体	(3, 10, 0)	1.2	15
II	球体	(6, 1, 2)	1
II	球体	(18, 22, 16)	2

场景类别	坐标类型	坐标值
I	起飞点	(0, 0, 0), (0, 10, 0), (0, 20, 0)
I	任务点	(5, 10, 8), (10, 2, 4), (10, 14, 13), (20, 8, 15), (20, 20, 20), (15, 23, 9), (10, 10, 18)
II	起飞点	(0, 0, 0), (0, 20, 0), (20, 10, 0)
II	任务点	(5, 10, 8), (10, 2, 4), (10, 17, 13), (20, 8, 15), (20, 20, 20), (15, 23, 9), (15, 13, 18)