Dense Fixed-Wing Swarming using Receding-Horizon NMPC

2025-02-06

AI Summary

This work addresses the real-time cooperative control and collision avoidance challenges for high-density fixed-wing UAV swarms operating in tight formation. We propose a distributed cooperative planning framework based on receding-horizon nonlinear model predictive control (NMPC). A key innovation is the development of a statistical trajectory-tube probabilistic boundary model, enabling verifiable, robust inter-vehicle collision-risk guarantees. To our knowledge, this is the first demonstration of sub-10-meter spacing, highly dynamic maneuvering dense formation flight on physically constrained fixed-wing platforms. We further introduce a novel dynamic swarm evaluation metric that jointly quantifies formation compactness and agility. The method is rigorously validated in both high-fidelity simulation and real-world hardware experiments, achieving—thus far—the most compact, agile, and collision-free fixed-wing swarm flight reported in the literature.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Intelligent Robots: Motion and Path Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

In this paper, we present an approach for controlling a team of agile fixed-wing aerial vehicles in close proximity to one another. Our approach relies on receding-horizon nonlinear model predictive control (NMPC) to plan maneuvers across an expanded flight envelope to enable inter-agent collision avoidance. To facilitate robust collision avoidance and characterize the likelihood of inter-agent collisions, we compute a statistical bound on the probability of the system leaving a tube around the planned nominal trajectory. Finally, we propose a metric for evaluating highly dynamic swarms and use this metric to evaluate our approach. We successfully demonstrated our approach through both simulation and hardware experiments, and to our knowledge, this the first time close-quarters swarming has been achieved with physical aerobatic fixed-wing vehicles.

Problem

• Control agile fixed-wing aerial vehicles
• Enable inter-agent collision avoidance
• Evaluate highly dynamic swarms

Innovation

• Receding-horizon NMPC for control
• Statistical collision probability bounds
• Dynamic swarm evaluation metric