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Journal of Projectiles, Rockets, Missiles and Guidance >

2025 , Vol. 45 >Issue 5: 693 - 706

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2025.05.013

Academic article

Multi-UAV Obstacle Avoidance Method Based on Improved DWA Fusion Algorithm

  • Chang Xucheng , 1 ,
  • Wang Jingyu , 2, * ,
  • Li Kang 2 ,
  • Tang Qian 2 ,
  • Zhang Xinhui 2
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  • 1 School of Automation, Zhengzhou University of Aeronautics, Zhengzhou 450046, Henan, China
  • 2 School of Aerospace Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, Henan, China

Received date: 2024-11-14

  Online published: 2025-11-28

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Abstract

To address the issue that traditional UAV obstacle-avoidance algorithms have low efficiency in unknown and complex environments,an improved improved Dynamic Window Approach (DWA) fusion algorithm was proposed.Regarding the lack of a global perspective in the DWA algorithm,a bidirectional search strategy was introduced to enhance the global value of the planned trajectory.Confronted with the difficulty of balancing calculation speed and accuracy in the DWA algorithm,a dynamic time step adjusted according to the environment was designed to weigh the computational efficiency.Aiming at the poor environmental adaptability of the DWA algorithm,a trajectory evaluation function with variable weights was put forward to improve environmental fitness.To boost the inter-UAV obstacle-avoidance ability in the multi-UAV collaborative mode,the improved DWA algorithm was integrated with the with the ORCA (Optimal Reciprocal Collision Avoidance) method.Simulation experiments were conducted to verify the proposed improved fusion algorithm.Compared with the traditional DWA algorithm,the UAV flight trajectory has decreased by 33.10%,the mission completion time has been shortened by 31.32%,and the number of iterations has been reduced by 50.05%.The overall performance has been significantly enhanced,which holds guiding significance for the engineering application of multi-UAV autonomous obstacle-avoidance technology.

Key words： Multiple Unmanned Aerial Vehicles (UAVs); Unknown Environment; Improved DWA Algorithm; ORCA Algorithm; Autonomous Obstacle Avoidance

Cite this article

Chang Xucheng , Wang Jingyu , Li Kang , Tang Qian , Zhang Xinhui . Multi-UAV Obstacle Avoidance Method Based on Improved DWA Fusion Algorithm[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2025 , 45(5) : 693 -706 . DOI: 10.15892/j.cnki.djzdxb.2025.05.013

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References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
S.M.Yusuf, I.Abdullahi, A.Bappi,A, et al. Towards Autonomous Multi-UAVs Surveillance Mission:A Study of Nigerian Telecommunication Masts Surveillance[C]// Proceedings of 2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS).Abuja,Nigeria:IEEE, 2021:1-6.

[2]
B.Yang,H.Shi and X.Xia. Federated Imitation Learning for UAV Swarm Coordination in Urban Traffic Monitoring[J]. IEEE Transactions on Industrial Informatics, 2023, 19(4),6037-6046.

DOI

[3]
S.Hafeez, R.Cheng, L.Mohjazi, et al. A Blockchain-Enabled Framework of UAV Coordination for Post-Disaster Networks[C]// Proceedings of 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring).Singapore,Singapore:IEEE, 2024:1-5.

[4]
XiaoWei Fu, ChenYuan Zhi, Di Wu. Obstacle avoidance and collision avoidance of UAV swarm based on improved VFH algorithm and information sharing strategy[J]. Computers & Industrial Engineering, 2023,186:109761.

[5]
Chengqing Liang, Lei Liu, Chen Liu. Multi-UAV autonomous collision avoidance based on PPO-GIC algorithm with CNN-LSTM fusion network[J]. Neural Networks, 2023,162:21-33

[6]
S.Guo, X.Pan and Z.Liu. AGV Path Planning Algorithm Based on Fusion of Improved A* and DWA[C]// Proceedings of 2024 43rd Chinese Control Conference (CCC).Kunming,China:IEEE, 2024:1782-1787

[7]
CAI Z, SELEZNEVA M S, YANG M. Dijkstra algorithm-based minimum acceleration/snap quadrotor UAV trajectory planning[J]. Journal of Physics:Conference Series, 2024,2746:012028.

[8]
VENKATASIVARAMBABU P, AGRAWAL R. Enhancing UAV navigation with dynamic programming and hybrid probabilistic route mapping:an improved dynamic window approach[J]. Int.j.inf.tecnol. 2024,16:1023-1032.

[9]
WANG N, DAI J, YING J. UAV Formation Obstacle Avoidance Control Algorithm Based on Improved Artificial Potential Field and Consensus[J]. Int.J.Aeronaut. Space Sci. 2021,22:1413-1427.

[10]
WANG J, ZHAO Z, QU J, et al. APPA-3D:an autonomous 3D path planning algorithm for UAVs in unknown complex environments[J]. Sci Rep 2024,14:1231.

[11]
Y.Xue, W.Chen. Multi-Agent Deep Reinforcement Learning for UAVs Navigation in Unknown Complex Environment[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(1):2290-2303.

DOI

[12]
Wu H., Wang F., Mei X. et al. A novel fuzzy control path planning algorithm for intelligent ship based on scale factors. J Supercomput 80,202-225(2024).

[13]
WANG X, CHENG M, ZHANG S, GONG H. Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach[J]. Drones 2023,7:504.

[14]
Xiong B, Haikun J, Junjie C, et al. UAV Path Planning Based on Improved A and DWA Algorithms[J]. International Journal of Aerospace Engineering, 2021.

[15]
Wang S.; Li G.; Liu B. Research on Unmanned Vehicle Path Planning Based on the Fusion of an Improved Rapidly Exploring Random Tree Algorithm and an Improved Dynamic Window Approach Algorithm.World Electr. Veh. J. 2024,15,292.

[16]
Pamarthi V, Richa A. Enhancing UAV navigation with dynamic programming and hybrid probabilistic route mapping:an improved dynamic window approach[J]. International Journal of Information Technology, 2023, 16(2):1023-1032.

DOI

[17]
宋飞飞. 基于ORCA算法的多智能船协同避碰决策方法研究[D]. 大连: 大连海事大学, 2023.

SONG F F. Research on Collaborative Collision Avoidance Decision-Making Method for Multi-Intelligent Ships Based on ORCA Algorithm[D]. Dalian: Dalian Maritime University, 2023.

[18]
Gong X.; Gao Y.; Wang F.; Zhu D, et al. A Local Path Planning Algorithm for Robots Based on Improved DWA[J]. Electronics 2024,13:2965.

[19]
D.Fox,W. Burgard and S,Thruns.The dynamic window approach to collision avoidance[J]. IEEE Robotics & Automation Magazine, 1997, 4(1):23-33.

[20]
Wu B.; Chi X.; Zhao C; et al. Dynamic Path Planning for Forklift AGV Based on Smoothing A* and Improved DWA Hybrid Algorithm[J].

[21]
H.Liu and Y.Zhang, ASL-DWA:An Improved A-Star Algorithm for Indoor Cleaning Robots[J]. IEEE Access, 2022,10:99498-99515.

[22]
Sang W.; Yue Y.; Zhai K.; Lin M. Research on AGV Path Planning Integrating an Improved A* Algorithm and DWA Algorithm[J]. Appl.Sci.2024,14:7551.

[23]
Jain M.; Saihjpal V.; Singh N. et al. An Overview of Variants and Advancements of PSO Algorithm[J]. Appl.Sci. 2022,12:8392..

[24]
van den Berg,J.,Lin,M.C.,& Manocha. Optimal reciprocal collision avoidance for real-time multi-agent navigation[C]// IEEE.Proceedings of 2011 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2011:388-395.

[25]
李昱东. 基于RRT-DWA算法的无人机三维动态航迹规划方法研究[D]. 电子科技大学, 2024.

LI Y D. Research on Three-Dimensional Dynamic Path Planning Method for UAVs Based on RRT-DWA Algorithm[D]. University of Electronic Science and Technology of China, 2024.DOI:10.27005/d.cnki.gdzku.2024.005024.

[26]
R.Shakeri et al. , “Design Challenges of Multi-UAV Systems in Cyber-Physical Applications:A Comprehensive Survey and Future Directions,” in IEEE Communications Surveys & Tutorials,vol. 21,no.4,pp.3340-3385.

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