Solution space path planning for supporting en-route air traffic control
A new conflict-free path planning algorithm for en-route air traffic control is proposed, leveraging solution-space displays for interpretability and flexibility. It integrates three intent-based conflict detection methods and two search variants (SSPPV and SSPPE). Empirical results using MUAC data show SSPPV with zone-based detection achieves optimal performance with average computation time of 3.69 ms.
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[Submitted on 30 Jun 2026]
Title:Solution space path planning for supporting en-route air traffic control
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Abstract:As technology advances, many path-planning algorithms have been proposed for Air Traffic Management, yet their operational adoption in tactical control remains limited, revealing a misalignment between algorithmic design priorities and air traffic controllers' needs. This underscores the need for decision-support solutions that are inherently interpretable, computationally efficient, and explicitly designed for human use. Focusing on this design challenge, this study develops a conflict-free path-planning algorithm for en-route Air Traffic Control (ATC) designed to be compatible with two guiding considerations: (1) the interpretability and flexibility offered by solution-space displays, which motivate constructing an algorithm that exposes all feasible safe actions and accommodates shifting optimization goals; and (2) the decision logic controllers naturally apply when enforcing operational constraints, such as separation standards, maneuverability limits, waypoint minimization, and routing practicality. Centered on these principles, the algorithm integrates three intent-based conflict detection methods -- distance-based, time-interval-based, and zone-based -- within a solution-space framework to identify conflict-free paths in computationally efficient ways. Additionally, vertex-based and edge-based search nodes are proposed for solution space path planning (SSPP), resulting in two variants -- SSPPV and SSPPE, respectively, which are evaluated in terms of computational speed and solution quality. Empirical results show that SSPPV paired with zone-based conflict detection achieves the best performance, computing paths in 3.69 ms on average in operational-relevant scenarios based on the Delta sector of the Maastricht Upper Area Control Centre (MUAC) using a 5 nmi grid.
Comments: 37 pages, 16 figures
Subjects:
Artificial Intelligence (cs.AI); Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2607.00064 [cs.AI]
(or arXiv:2607.00064v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.00064
arXiv-issued DOI via DataCite
Submission history
From: Yiyuan Zou [view email] [v1] Tue, 30 Jun 2026 12:33:42 UTC (6,398 KB)
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