Robust Proximity Operations using Probabilistic Markov Models

TL;DR

This paper presents a robust proximity operation framework for autonomous vehicles using probabilistic Markov models, integrating sensor fusion and mode switching for improved reliability in satellite docking and aerial landing.

Contribution

It introduces a novel Markov decision process-based mode switching framework combined with a multi-sensor pose estimator for enhanced proximity operations.

Findings

Successful satellite docking demonstration

Precise aerial vehicle landing achieved

Robust performance under sensor noise and outliers

Abstract

A Markov decision process-based state switching is devised, implemented, and analyzed for proximity operations of various autonomous vehicles. The framework contains a pose estimator along with a multi-state guidance algorithm. The unified pose estimator leverages the extended Kalman filter for the fusion of measurements from rate gyroscopes, monocular vision, and ultra-wideband radar sensors. It is also equipped with Mahalonobis distance-based outlier rejection and under-weighting of measurements for robust performance. The use of probabilistic Markov models to transition between various guidance modes is proposed to enable robust and efficient proximity operations. Finally, the framework is validated through an experimental analysis of the docking of two small satellites and the precision landing of an aerial vehicle.