Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Dense Fixed-Wing Swarming using Receding-Horizon NMPC

Varun Madabushi, Yocheved Kopel, Adam Polevoy, Joseph Moore

TL;DR

This work tackles close-quarters control of agile fixed-wing UAV swarms using receding-horizon nonlinear model predictive control (NMPC) with direct trajectory optimization and shared inter-agent trajectories to enforce collision avoidance. It couples a direct transcription trajectory optimizer (with Simpson integration and SNOPT) and a discrete-time TVLQR tracker, augmented by trajectory obstacle constraints and a probabilistic safety bound derived from Hoeffding’s inequality to determine a robust collision radius $r = \rho + \epsilon$. A robust method for selecting collision distance is proposed, and the Swarm Energy Density (SED) metric is introduced to quantify performance relative to swarm size and speed. The approach is validated in simulation and hardware, with up to four fixed-wing UAVs flying within 3 wingspans at about 4 m/s, and results suggest a substantial improvement in proximity without sacrificing safety, supported by a quantitative collision-risk analysis and SED comparisons.

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.

Dense Fixed-Wing Swarming using Receding-Horizon NMPC

TL;DR

This work tackles close-quarters control of agile fixed-wing UAV swarms using receding-horizon nonlinear model predictive control (NMPC) with direct trajectory optimization and shared inter-agent trajectories to enforce collision avoidance. It couples a direct transcription trajectory optimizer (with Simpson integration and SNOPT) and a discrete-time TVLQR tracker, augmented by trajectory obstacle constraints and a probabilistic safety bound derived from Hoeffding’s inequality to determine a robust collision radius . A robust method for selecting collision distance is proposed, and the Swarm Energy Density (SED) metric is introduced to quantify performance relative to swarm size and speed. The approach is validated in simulation and hardware, with up to four fixed-wing UAVs flying within 3 wingspans at about 4 m/s, and results suggest a substantial improvement in proximity without sacrificing safety, supported by a quantitative collision-risk analysis and SED comparisons.

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.

Paper Structure

This paper contains 17 sections, 13 equations, 9 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Approach
  4. Dynamics Model
  5. Trajectory Generation
  6. Trajectory Tracking
  7. Trajectory Obstacle Constraints
  8. Nonlinear Model Predictive Control
  9. Robust Selection of Collision Distance
  10. Simulation Results
  11. Experimental Results
  12. Experimental Set-up
  13. Experiments
  14. Analysis
  15. Collision Risk
  16. ...and 2 more sections

Figures (9)

  • Figure 1: A still of four aerobatic fixed-wing UAVs executing alternating flight patterns in close proximity.
  • Figure 2: Figure shows how the trajectory tracking bound $\rho$ can combine with the re-planning deviation $\epsilon$ to increase the required collision radius.
  • Figure 3: A still from a three-plane alternating box pattern simulation experiment.
  • Figure 4: 24" wingspan Edge 540 EPP model.
  • Figure 5: Trajectories of 3 planes flying in a box pattern
  • ...and 4 more figures