Cooperative Multi-UAV Collision Avoidance Based on Distributed Dynamic Optimization and Causal Analysis
Source abstract
Abstract
A critical requirement for unmanned aerial vehicles (UAV) is the collision avoidance (CA) capability to meet safety and flexibility issues in an environment of increasing air traffic densities. This paper proposes two efficient algorithms: conflict detection (CD) algorithm and conflict resolution (CR) algorithm. These two algorithms are the key components of the cooperative multi-UAV CA system. The CD sub-module analyzes the spatial-temporal information of four dimensional (4D) trajectory to detect potential collisions. The CR sub-module calculates the minimum deviation of the planned trajectory by an objective function integrated with track adjustment, distance, and time costs, taking into account the vehicle performance, state and separation constraints. Additionally, we extend the CR sub-module with causal analysis to generate all possible solution states in order to select the optimal strategy for a multi-threat scenario, considering the potential interactions among neighboring UAVs with a global scope of a cluster. Quantitative simulation experiments are conducted to validate the feasibility and scalability of the proposed CA system, as well as to test its efficiency with variable parameters.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Air Traffic Management and Optimization · UAV Applications and Optimization · Robotic Path Planning Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
The School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Department of Telecommunication and System Engineering, Universitat Autònoma de Barcelona, Sabadell 08201, Spain
References (24)
Show source-provided references
1. Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping10.3390/s150819688 ↗
2. A medium term conflict detection and resolution system for terminal maneuvering area based on spatial data structures and 4D trajectories10.1016/j.trc.2012.10.005 ↗
3. Development, validation and application of stochastically and dynamically coloured Petri net model of ACAS operations for safety assessment purposes10.1016/j.trc.2012.04.018 ↗
4. Airborne Separation Assurance Systems: Towards a work programme to prove safety10.1016/j.ssci.2003.12.002 ↗
5. Lai, C.P., Ren, Y.J., and Lin, C. (2009, January 7–9). ADS-B based collision avoidance radar for unmanned aerial vehicles. Proceedings of the IEEE MTT-S International Microwave Symposium Digest, Boston, MA, USA.10.1109/MWSYM.2009.5165638 ↗
6. Lin, Y., and Saripalli, S. (2015, January 26–30). Sense and avoid for Unmanned Aerial Vehicles using ADS-B. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.
7. BaraldiSesso, D., Vismari, L.F., and Camargo, J.B. (2014, January 27–30). An approach to assess the safety of ADS-B based unmanned aerial systems. Proceedings of the 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, FL, USA.10.1109/ICUAS.2014.6842311 ↗
8. Aircraft collision avoidance using Monte Carlo real-time belief space search10.1007/s10846-010-9532-6 ↗
9. Collision Avoidance for Multi-UAV Based on Geometric Optimization Model in 3D Airspace10.1007/s13369-014-1368-0 ↗
10. A symbiotic simulation architecture for evaluating UAVs collision avoidance techniques10.1057/jos.2013.5 ↗
11. A Probabilistic Approach to Aircraft Conflict Detection10.1109/6979.898224 ↗
12. Rendezvous of multiple UAVs with collision avoidance using consensus10.1061/(ASCE)AS.1943-5525.0000145 ↗
13. Reactive Collision Avoidance of Using Nonlinear Geometric and Differential Geometric Guidance10.2514/1.50923 ↗
14. Evolutionary algorithm based offline/online path planner for UAV navigation10.1109/TSMCB.2002.804370 ↗
15. UAV position estimation and collision avoidance using the extended Kalman filter10.1109/TVT.2013.2243480 ↗
16. UAV path planning in a dynamic environment via partially observable markov decision process10.1109/TAES.2013.6621824 ↗
17. Lin, Y., and Saripalli, S. (2014, January 27–30). Path planning using 3D Dubins Curve for Unmanned Aerial Vehicles. Proceedings of the 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, FL, USA.10.1109/ICUAS.2014.6842268 ↗
18. Shim, D.H., and Sastry, S. (2007, January 11–13). An Evasive Maneuvering Algorithm for UAVs in See-and-Avoid Situations. Proceedings of the 2007 American Control Conference, New York, NY, USA.10.1109/ACC.2007.4283147 ↗
19. Lin, Y., and Saripalli, S. (2015, January 9–12). Collision avoidance for UAVs using reachable sets. Proceedings of the 2015 International Conference on Unmanned Aircraft Systems (ICUAS), Denver, CO, USA.10.1109/ICUAS.2015.7152295 ↗
20. Netjasov, F., Vidosavljevic, A., Tosic, V., Everdij, M.H.C., and Blom, H.A.P. (2010, January 1–4). Stochastically and dynamically coloured Petri net model of ACAS operations. Proceedings of the 4th International Conference on Research in Air Transportation (ICRAT), Budapest, Hungary.
21. Next-generation airborne collision avoidance system
22. Strategic de-confliction in the presence of a large number of 4D trajectories using a causal modeling approach10.1016/j.trc.2013.12.002 ↗
23. Defining changeability: Reconciling flexibility, adaptability, scalability, modifiability, and robustness for maintaining system lifecycle value10.1002/sys.20098 ↗
24. George, J., and Ghose, D. (2009, January 10–12). A reactive inverse PN algorithm for collision avoidance among multiple unmanned aerial vehicles. Proceedings of the 2009 American Control Conference, St. Louis, MO, USA.10.1109/ACC.2009.5160241 ↗