Fast, Safe, and Propellant-Efficient Spacecraft Planning under Clohessy-Wiltshire-Hill Dynamics

TL;DR

This paper introduces a real-time, propellant-efficient spacecraft trajectory planning algorithm that guarantees safety and performance, leveraging advanced robot motion planning techniques under the Clohessy-Wiltshire-Hill dynamics model.

Contribution

It adapts the Fast Marching Tree algorithm for spacecraft trajectory planning, incorporating safety constraints and robustness to thruster failures, with theoretical guarantees and real-time applicability.

Findings

Algorithm guarantees safety and performance.

Suitable for real-time autonomous planning.

Applicable to various space missions.

Abstract

This paper presents a sampling-based motion planning algorithm for real-time and propellant-optimized autonomous spacecraft trajectory generation in near-circular orbits. Specifically, this paper leverages recent algorithmic advances in the field of robot motion planning to the problem of impulsively-actuated, propellant-optimized rendezvous and proximity operations under the Clohessy-Wiltshire-Hill (CWH) dynamics model. The approach calls upon a modified version of the Fast Marching Tree (FMT*) algorithm to grow a set of feasible trajectories over a deterministic, low-dispersion set of sample points covering the free state space. To enforce safety, the tree is only grown over the subset of actively-safe samples, from which there exists a feasible one-burn collision avoidance maneuver that can safely circularize the spacecraft orbit along its coasting arc under a given set of potential thruster failures. Key features of the proposed algorithm include: (i) theoretical guarantees in terms of trajectory safety and performance, (ii) amenability to real-time implementation, and (iii) generality, in the sense that a large class of constraints can be handled directly. As a result, the proposed algorithm offers the potential for widespread application, ranging from on-orbit satellite servicing to orbital debris removal and autonomous inspection missions.