Switching-Based Cooperative Avoidance Control for Multi-Agent Quadrotor Dynamic Systems in Dense Environments
Source abstract
Abstract
This paper presents a control framework for multi-unmanned aerial vehicle systems that achieves safe and cooperative navigation in complex environments through a unified collision avoidance and trajectory guidance strategy. The principal innovation lies in the incorporation of velocity information into the design of a switching function, enabling more accurate assessment of collision risk and effectively reducing system conservativeness. Building upon this, an adaptive trajectory guidance mechanism is developed using collision avoidance information to ensure safe motion coordination among the vehicles. In addition, a closed-form solution for the dynamic system is derived, and its safety and stability are rigorously established through Lyapunov-based analysis. The effectiveness of the proposed framework is validated through simulation studies conducted on the MATLAB/Simulink platform (version R2020b), confirming reliable cooperative navigation in densely cluttered environments and guaranteeing dynamic safety.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotic Path Planning Algorithms · Guidance and Control Systems · Air Traffic Management and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Coordinated Science Laboratory and the Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
References (32)
Show source-provided references
1. Forest fire, thinning, and flood in wildland-urban interface: UAV and lidar-based estimate of natural disaster impacts10.1007/s10980-024-01811-5 ↗
2. Low-cost UAV coordinated carbon observation network: Carbon dioxide measurement with multiple UAVs10.1016/j.atmosenv.2024.120609 ↗
3. Velocity Optimization for UAV-Mounted Transmitter in Population-Varying Fields10.1109/TVT.2023.3262356 ↗
4. Integrating intelligent sensors for safe UAV distribution: Design and evaluation of ranging system10.28991/HIJ-2024-05-03-04 ↗
5. Adoni, W.Y.H., Fareedh, J.S., Lorenz, S., Gloaguen, R., Madriz, Y., Singh, A., and Kühne, T.D. (2024). Intelligent Swarm: Concept, Design and Validation of Self-Organized UAVs Based on Leader–Followers Paradigm for Autonomous Mission Planning. Drones, 8.10.3390/drones8100575 ↗
6. Lee, K., and Han, X. (2023). A Study on Leveraging Unmanned Aerial Vehicle Collaborative Driving and Aerial Photography Systems to Improve the Accuracy of Crop Phenotyping. Remote Sens., 15.10.3390/rs15153903 ↗
7. Obstacle-Avoidance Trajectory Planning Based Adaptive Tracking Control for 4DOF Tower Cranes with Tracking Error Constraints10.1016/j.ymssp.2024.112109 ↗
8. Hybrid Path Planning for USV with Kinematic Constraints and COLREGS Based on Improved APF-RRT and DWA10.1016/j.oceaneng.2024.120128 ↗
9. AAPF*: A Safer Autonomous Vehicle Path Planning Algorithm Based on the Improved A* Algorithm and APF Algorithm10.1007/s10586-024-04287-9 ↗
10. A Hybrid Path Planning Method Based on Improved A* and CSA-APF Algorithms10.1109/ACCESS.2024.3372573 ↗
11. Benmachiche, A., Derdour, M., Kahil, M.S., Ghanem, M.C., and Deriche, M. (2025). Adaptive Hybrid PSO–APF Algorithm for Advanced Path Planning in Next-Generation Autonomous Robots. Sensors, 25.10.3390/s25185742 ↗
12. Tao, F., Ding, Z., Wang, Z., and Ji, B. (2025). VPS-APF-RRT*: A Variable Probability Sampling RRT* Intelligent Algorithm Combined with APF for Autonomous Vehicles Path Planning. Expert Syst. Appl., 129965.10.1016/j.eswa.2025.129965 ↗
13. DBVS-APF-RRT*: A Global Path Planning Algorithm with Ultra-High Speed Generation of Initial Paths and High Optimal Path Quality10.1016/j.eswa.2024.123571 ↗
14. APF-CPP: An Artificial Potential Field Based Multi-Robot Online Coverage Path Planning Approach10.1109/LRA.2024.3432351 ↗
15. PyRoboCOP: Python-Based Robotic Control and Optimization Package for Manipulation and Collision Avoidance10.1109/TASE.2024.3365637 ↗
16. Hierarchical Pigeon Inspiblue Optimization Based Multi-UAV Obstacle Avoidance Control10.1016/j.ast.2025.109963 ↗
17. Path Planning and Obstacle Avoidance Control of UUV Based on an Enhanced A* Algorithm and MPC in Dynamic Environment10.1016/j.oceaneng.2024.117584 ↗
18. MPC-Based Position Control for Multi-Segment Pneumatic Soft Manipulator Considering Obstacle Avoidance10.1109/LRA.2025.3532165 ↗
19. Butler, B.A., Cabrera, Z., Nguyen, A., and Paré, P.E. (2025, January 24–26). Safe Reference Tracking and Collision Avoidance for Taxiing Aircraft Using an MPC-CBF Framework. Proceedings of the 2025 American Control Conference (ACC), St. Louis, MO, USA.10.23919/ACC63710.2025.11107583 ↗
20. Force-Compliance MPC and Robot-User CBFs for Interactive Navigation and User-Robot Safety in Hexapod Guide Robots10.1109/TASE.2025.3596630 ↗
21. Deep Reinforcement Learning for Collision Avoidance in Unmanned Surface Vehicles: State-of-the-Art10.1016/j.apor.2025.104778 ↗
22. Deep Learning-Based Obstacle-Avoiding Autonomous UAVs with Fiducial Marker-Based Localization for Structural Health Monitoring10.1177/14759217231177314 ↗
23. Collision Avoidance Decision-Making Strategy for Multiple USVs Based on Deep Reinforcement Learning Algorithm10.1016/j.oceaneng.2024.118323 ↗
24. Alharthi, R., Noreen, I., Khan, A., Aljrees, T., Riaz, Z., and Innab, N. (2025). Novel Deep Reinforcement Learning Based Collision Avoidance Approach for Path Planning of Robots in Unknown Environment. PLoS ONE, 20.10.1371/journal.pone.0312559 ↗
25. A Deep Residual Reinforcement Learning Algorithm Based on Soft Actor-Critic for Autonomous Navigation10.1016/j.eswa.2024.125238 ↗
26. Multirobot Unknown Environment Exploration and Obstacle Avoidance Based on a Voronoi Diagram and Reinforcement Learning10.1016/j.eswa.2024.125900 ↗
27. Learning Vision-Based Agile Flight via Differentiable Physics10.1038/s42256-025-01048-0 ↗
28. Ahn, S., Oh, T., and Yoo, J. (2024). Collision Avoidance Path Planning for Automated Vehicles Using Pblueiction Information and Artificial Potential Field. Sensors, 24.10.3390/s24227292 ↗
29. Herrmann, B., Rieck, L., and Thielecke, F. (2024, January 11–13). Flight Testing of Real-Time Model Pblueictive Flight Control for Unmanned Flexible Aircraft. Proceedings of the EuroGNC Conference, CEAS, Bristol, UK.
30. Bu, Y., Yan, Y., and Yang, Y. (2024). Advancement Challenges in UAV Swarm Formation Control: A Comprehensive Review. Drones, 8.10.3390/drones8070320 ↗
31. Fossen, T.I. (1994). Guidance and Control of Ocean Vehicles, Wiley.
32. Clarke, F.H. (1990). Optimization and Nonsmooth Analysis, SIAM.10.1137/1.9781611971309 ↗