Fair-CoPlan: Negotiated Flight Planning with Fair Deconfliction for Urban Air Mobility

2025-08-19

AI Summary

In urban air mobility (UAM), unfair path allocation among multiple operators sharing constrained airspace leads to certain unmanned aircraft systems (UAS) obtaining excessively short paths at the expense of others’ operational equity and safety. Method: This paper proposes a semi-distributed flight planning framework wherein service providers and operators jointly compute solutions, integrating capacity-constrained takeoff/landing point planning, distributed path generation, and centralized conflict resolution. A novel path-length fairness optimization mechanism is introduced, employing a phased negotiation strategy to equitably distribute deconfliction costs across operators. Contribution/Results: Simulation results demonstrate that, with only marginal increases in mission delay, the framework significantly improves path allocation fairness while concurrently enhancing operator participation willingness, system adaptability, and both airspace and ground safety.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Multiagent Planning
• Search and Optimization: Distributed Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Urban Air Mobility (UAM) is an emerging transportation paradigm in which Uncrewed Aerial Systems (UAS) autonomously transport passengers and goods in cities. The UAS have different operators with different, sometimes competing goals, yet must share the airspace. We propose a negotiated, semi-distributed flight planner that optimizes UAS' flight lengths {em in a fair manner}. Current flight planners might result in some UAS being given disproportionately shorter flight paths at the expense of others. We introduce Fair-CoPlan, a planner in which operators and a Provider of Service to the UAM (PSU) together compute emph{fair} flight paths. Fair-CoPlan has three steps: First, the PSU constrains take-off and landing choices for flights based on capacity at and around vertiports. Then, operators plan independently under these constraints. Finally, the PSU resolves any conflicting paths, optimizing for path length fairness. By fairly spreading the cost of deconfliction Fair-CoPlan encourages wider participation in UAM, ensures safety of the airspace and the areas below it, and promotes greater operator flexibility. We demonstrate Fair-CoPlan through simulation experiments and find fairer outcomes than a non-fair planner with minor delays as a trade-off.

Problem

• Fair flight path planning for urban air mobility systems
• Resolving conflicting UAS paths with fairness optimization
• Balancing operator goals and airspace deconfliction constraints

Innovation

• Semi-distributed negotiated flight planning
• Fair deconfliction with path optimization
• Capacity-constrained vertiport coordination