Constant-Optic-Flow Lunar Landing: Optimality and Guidance

Neuromorphic architectures to robust and adaptive navigation based on visual clues have been proposed as automated landing systems. In particular, constant-optic-flow descents have been studied in relation to their bioinspired nature and to their promise for a substantial hardware and software simplification. The main body of work on the topic considers Earth-based systems as applications, such as micro air vehicles, and has only lately looked at planetary landings, but never in relation to their mass optimality. In this paper, constant-optic-flow descents are studied with respect to optimality, first from a theoretical point of view using Pontryagin’s maximum principle and then performing a numerical investigation on some selected cases (Apollo-like) anda comparison with unconstrained descents. The propellant mass introduced by forcing a constant optic flow during a lunar descent is estimated for typical high-gate/low-gate conditions. The effect of constraining the spacecraft pitch law during the constant-optic-flow descent is also studied, showing that an optimal pitch law is essential to lower the overall mass consumption and that linear or exponential laws may not be adequate. A guidance algorithm is then presented and discussed for use in automated planetary landing when a constant optic flow is regulated.

[1]  Nicolas Franceschini,et al.  Visual guidance based on optic flow: a biorobotic approach , 2004, Journal of Physiology-Paris.

[2]  Mandyam V. Srinivasan,et al.  Bioinspired Engineering of Exploration Systems for NASA and DoD , 2002, Artificial Life.

[3]  M. L. Chambers The Mathematical Theory of Optimal Processes , 1965 .

[4]  Andrew M. Hyslop,et al.  Autonomous Navigation in Three-Dimensional Urban Environments Using Wide-Field Integration of Optic Flow , 2010 .

[5]  Garrick Orchard,et al.  Neuromorphic computation of optic flow data Bio-inspired landing using biomorphic vision sensors Final Report , 2010 .

[6]  Nicolas H. Franceschini,et al.  Optic flow regulation: the key to aircraft automatic guidance , 2005, Robotics Auton. Syst..

[7]  Carver A. Mead,et al.  Neuromorphic electronic systems , 1990, Proc. IEEE.

[8]  Chit Hong Yam Design of missions to the outer planets and optimization of low-thrust, gravity -assist trajectories via reduced parameterization , 2008 .

[9]  J. Longuski,et al.  Low-Thrust Trajectories to Jupiter via Gravity Assists from Venus, Earth, and Mars , 2006 .

[10]  Krzysztof Sibilski,et al.  Biomimic Sensors Guided Flight Stability and Control for Flapping Wings Autonomous Micro Air Vehicles (Entomopter) , 2007 .

[11]  D. Izzo,et al.  Landing with Time-to-Contact and Ventral Optic Flow Estimates , 2012 .

[12]  G. E. Glock,et al.  Glycogen and calcification , 1940, The Journal of physiology.

[13]  Klaus Janschek,et al.  Performance Analysis for Visual Planetary Landing Navigation Using Optical Flow and DEM Matching , 2006 .

[14]  N. Franceschini,et al.  From insect vision to robot vision , 1992 .

[15]  Jan Albert Mulder,et al.  Vision-Only Control of a Flapping MAV on Mars , 2007 .

[16]  N. Franceschini,et al.  A Bio-Inspired Flying Robot Sheds Light on Insect Piloting Abilities , 2007, Current Biology.

[17]  Rahul Sarpeshkar,et al.  An ultra-low-power programmable analog bionic ear processor , 2005, IEEE Transactions on Biomedical Engineering.

[18]  Zhang,et al.  Honeybee navigation en route to the goal: visual flight control and odometry , 1996, The Journal of experimental biology.

[19]  Jon A. Sims,et al.  Preliminary Design of Low-Thrust Interplanetary Missions , 1997 .

[20]  Damon Landau,et al.  Powered Earth-Mars Cycler with Three-Synodic-Period Repeat Time , 2005 .

[21]  Dario Izzo,et al.  Constrained global optimization of low-thrust interplanetary trajectories , 2010, IEEE Congress on Evolutionary Computation.

[22]  Stephen Parkes,et al.  Planet Surface Simulation with PANGU , 2004 .

[23]  Stéphane Viollet,et al.  Biomimetic optic flow sensing applied to a lunar landing scenario , 2010, 2010 IEEE International Conference on Robotics and Automation.

[24]  Daniel W. Parcher,et al.  Gravity-assist trajectories to Jupiter using nuclear electric propulsion , 2005 .

[25]  W. Reichardt Movement perception in insects , 1969 .

[26]  Michael H. Dickinson,et al.  Integrative Model of Drosophila Flight , 2008 .

[27]  J. Meditch,et al.  Applied optimal control , 1972, IEEE Transactions on Automatic Control.

[28]  Mandyam V. Srinivasan,et al.  Landing Strategies in Honeybees and Applications to Uninhabited Airborne Vehicles , 2004, Int. J. Robotics Res..

[29]  Franck Ruffier,et al.  OCTAVE: a bioinspired visuo-motor control system for the guidance of micro-air-vehicles , 2003, SPIE Microtechnologies.