Sub-optimal Lunar Landing GNC using Non-gimbaled Bio-inspired Optic Flow Sensors - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Aerospace and Electronic Systems Year : 2015

Sub-optimal Lunar Landing GNC using Non-gimbaled Bio-inspired Optic Flow Sensors

(1, 2) , (2) , (1) , (3) , (4) , (2)
1
2
3
4

Abstract

Autonomous planetary landing is a critical phase in every exploratory space mission. Autopilots have to be safe, reliable, energy-saving, and as light as possible. The 2-D Guidance Navigation and Control (GNC) strategy presented here makes use of biologically inspired landing processes. Based solely on the relative visual motion known as the Optic Flow (OF) assessed with minimalistic 6-pixel 1-D OF sensors and Inertial Measurement Unit measurements, an optimal reference trajectory in terms of the mass was defined for the approach phase. Linear and nonlinear control laws were then implemented in order to track the optimal trajectory. To deal with the demanding weight constraints, a new method of OF estimation was applied, based on a non-gimbaled OF sensor configuration and a linear least squares algorithm. The promising results obtained with Software-In-the-Loop simulations showed that the present full GNC solution combined with our OF bio-inspired sensors is compatible with soft, fuel-efficient lunar spacecraft landing and might also be used as a backup solution in case of conventional sensor failure.
Fichier principal
Vignette du fichier
Sabiron_et_al_TAES.pdf (9.46 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01270504 , version 1 (08-02-2016)

Identifiers

Cite

Guillaume Sabiron, Thibaut Raharijaona, Laurent Burlion, Erwan Kervendal, Eric Bornschlegl, et al.. Sub-optimal Lunar Landing GNC using Non-gimbaled Bio-inspired Optic Flow Sensors. IEEE Transactions on Aerospace and Electronic Systems, 2015, IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 51 (4), pp.2525 - 2545. ⟨10.1109/TAES.2015.130573⟩. ⟨hal-01270504⟩
526 View
123 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More