Solution Space Path Planning: A Real-Time Human-Centered Path Planning Algorithm for En-Route Air Traffic Control

Abstract:As technology advances, various algorithms have been proposed for air traffic management, yet their operational adoption in tactical control remains limited. This gap motivates a human-centered design emphasizing algorithmic interpretability, controller-relevant operational constraints, and real-time computation. Inspired by the interpretability and flexibility of solution-space displays, as well as by the decision logic controllers naturally apply when enforcing operational constraints, this study extends the solution-space concept to path planning and develops a fast conflict-free path-planning algorithm for en-route Air Traffic Control (ATC), termed Solution Space Path Planning (SSPP). The algorithm integrates three intent-based conflict detection methods---distance-based, time-interval-based, and zone-based---within the solution-space framework to identify conflict-free paths in computationally efficient ways. SSPP is developed using both vertex-based and edge-based search nodes, resulting in two variants---SSPPV and SSPPE, respectively. Empirical results show that SSPPV paired with zone-based conflict detection performs best, computing paths in 3.69 ms on average in the Dutch Delta sector using a 5 nmi grid. SSPPV remains approximately 3.77 times faster than SSPPE while offering competitive effectiveness, making it suitable for time-critical operations and interactive 'what-if' probing in real time. An extension to SSPPV and SSPPE further examines the trade-off between delay minimization and separation requirements, demonstrating the flexibility of SSPP in revising optimization objectives. This study not only proposes a novel path-planning algorithm but also shows how such algorithms can be designed to align with human use and operational requirements, supporting their integration into future ATC systems.

Artificial Intelligence (cs.AI); Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2607.00064 [cs.AI]

(or arXiv:2607.00064v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.00064 Focus to learn more arXiv-issued DOI via DataCite

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