ROCVJan 22, 2023

Autonomous Rendezvous with Non-cooperative Target Objects with Swarm Chasers and Observers

arXiv:2301.09059v18 citationsh-index: 11
Originality Incremental advance
AI Analysis

This addresses the problem of space debris removal for space agencies and satellite operators, though it appears incremental as it builds on existing vision and guidance methods.

The paper tackles autonomous rendezvous with non-cooperative space debris using a swarm of chasers and observers, resulting in successful hardware-in-the-loop experiments where three drones autonomously rendezvoused with a moving satellite mockup.

Space debris is on the rise due to the increasing demand for spacecraft for com-munication, navigation, and other applications. The Space Surveillance Network (SSN) tracks over 27,000 large pieces of debris and estimates the number of small, un-trackable fragments at over 1,00,000. To control the growth of debris, the for-mation of further debris must be reduced. Some solutions include deorbiting larger non-cooperative resident space objects (RSOs) or servicing satellites in or-bit. Both require rendezvous with RSOs, and the scale of the problem calls for autonomous missions. This paper introduces the Multipurpose Autonomous Ren-dezvous Vision-Integrated Navigation system (MARVIN) developed and tested at the ORION Facility at Florida Institution of Technology. MARVIN consists of two sub-systems: a machine vision-aided navigation system and an artificial po-tential field (APF) guidance algorithm which work together to command a swarm of chasers to safely rendezvous with the RSO. We present the MARVIN architec-ture and hardware-in-the-loop experiments demonstrating autonomous, collabo-rative swarm satellite operations successfully guiding three drones to rendezvous with a physical mockup of a non-cooperative satellite in motion.

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