Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Safe and wind-aware synchronous path planning for a fleet of fixed-wing constant speed aircraft

Maël Feurgard, Gautier Hattenberger, Nicolas Durand, Simon Lacroix

Abstract

Path planning for multiple unmanned aerial vehicles is a difficult task, and even more for a fleet of fixed-wing aircraft. One specific case is the transition to, or between, formation flight patterns, which requires synchronous arrivals while ensuring minimal separation, and ideally maintaining cruise speed. We present a centralized method to solve this problem based on enumerating different Dubins paths. Given a travel time for the fleet, it builds a set of possible paths for each aircraft. Then, it checks in parallel separation between each path pair. This yields coefficients for an Integer Linear Programming problem determining if a fleet-wide conflict-free solution exists. This process is repeated for different travel times sampled with increasing resolution until the user-defined accuracy is met. The method is benchmarked with Monte-Carlo simulations considering up to 20 aircraft simultaneously, achieving an 95% success rate for an average 8 seconds computation time.

Safe and wind-aware synchronous path planning for a fleet of fixed-wing constant speed aircraft

Abstract

Path planning for multiple unmanned aerial vehicles is a difficult task, and even more for a fleet of fixed-wing aircraft. One specific case is the transition to, or between, formation flight patterns, which requires synchronous arrivals while ensuring minimal separation, and ideally maintaining cruise speed. We present a centralized method to solve this problem based on enumerating different Dubins paths. Given a travel time for the fleet, it builds a set of possible paths for each aircraft. Then, it checks in parallel separation between each path pair. This yields coefficients for an Integer Linear Programming problem determining if a fleet-wide conflict-free solution exists. This process is repeated for different travel times sampled with increasing resolution until the user-defined accuracy is met. The method is benchmarked with Monte-Carlo simulations considering up to 20 aircraft simultaneously, achieving an 95% success rate for an average 8 seconds computation time.
Paper Structure (11 sections, 6 equations, 5 figures, 1 algorithm)

This paper contains 11 sections, 6 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem formulation
  3. Dubins paths
  4. Finding a fitting solution
  5. Conflicts detection
  6. Fleet path planning
  7. Presentation of the algorithm
  8. Algorithm analysis
  9. Algorithm extensions
  10. Simulations
  11. Conclusions and future works

Figures (5)

  • Figure 1: Examples of the six Dubins paths plus two extras (SRS, SLS)
  • Figure 2: Start, end and both extended paths: $RSR$ and $LSL$
  • Figure 3: Success rate as function of aircraft number and type of test case
  • Figure 4: Computation time log distribution as function of aircraft number
  • Figure 5: Demonstration of conflict free path planning without and with wind, from circular to chevron formations.