Rapidly Exploring Random Trees with Physics-Informed Neural Networks for Constrained Energy-Optimal Rendezvous Problems

Kristofer Drozd, Roberto Furfaro, Daniele Mortari

Research output: Contribution to journalArticlepeer-review

Abstract

This article introduces physics-informed neural networks (PINNs) to the field of motion planning by utilizing a PINN framework as the steering function in the kinodynamic rapidly-exploring random tree (RRT*) algorithm. The goal of this paper is to show that PINN-based methods can be used successfully for aerospace motion planning applications. We test the RRT* algorithm coupled with PINN steering, what we call PINN-RRT*, by solving spacecraft energy-optimal motion planning problems governed by the Hill–Clohessy–Wiltshire (HCW) equations of motion and nonlinear equations of relative motion (NERM), where a deputy satellite must rendezvous with a chief satellite while avoiding spherical keep-out-zones and complying with an approach corridor. The particular PINN framework we employ approximates the solution of nonlinear two-point boundary value problems (TPBVPs), which must be solved to form connections between waypoints in the RRT* tree, via the Theory of Functional Connections (TFC). TFC enables the PINN to analytically satisfy the boundary conditions (BCs) of the TPBVP. Thus, the admissible solution search space of each nonlinear TPBVP is reduced to just the trajectories that already satisfy the BCs. Using our proposed approach, each energy-optimal TPBVP solution during the run-time of the PINN-RRT* algorithm was computed in centiseconds and with an average error on the order of machine epsilon for both the HCW and NERM dynamics.

Original languageEnglish (US)
Article number9
JournalJournal of the Astronautical Sciences
Volume71
Issue number1
DOIs
StatePublished - Feb 2024

Keywords

  • Energy-optimal control
  • Optimal motion planning
  • Physics-informed neural networks
  • Rapidly-exploring random trees
  • Spacecraft relative motion
  • Theory of functional connections

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science

Fingerprint

Dive into the research topics of 'Rapidly Exploring Random Trees with Physics-Informed Neural Networks for Constrained Energy-Optimal Rendezvous Problems'. Together they form a unique fingerprint.

Cite this