Suboptimal lunar landing GNC using nongimbaled optic-flow sensors

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 strategy presented here makes use of biologically inspired landing processes. Based solely on the relative visual motion known as 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 to track the optimal trajectory. To deal with the demanding weight constraints, a new method of OF estimation was applied, based on a nongimbaled OF sensor configuration and a linear least-squares algorithm. The promising results obtained with software-in-the-loop simulations showed that the present full guidance, navigation, and control 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.

[1]  G. Flandin,et al.  VISION BASED NAVIGATION FOR PLANETARY EXPLORATION , 2009 .

[2]  F. Ruffier,et al.  Two-Directional 1-g Visual Motion Sensor Inspired by the Fly's Eye , 2013, IEEE Sensors Journal.

[3]  Yang Cheng,et al.  Landmark Based Position Estimation for Pinpoint Landing on Mars , 2005 .

[4]  Nicolás Weiss,et al.  Constant-Optic-Flow Lunar Landing: Optimality and Guidance , 2011 .

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

[6]  Timothy W. McLain,et al.  Maximizing miniature aerial vehicles , 2006, IEEE Robotics & Automation Magazine.

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

[8]  Rogelio Lozano,et al.  An adaptive vision-based autopilot for mini flying machines guidance, navigation and control , 2009, Auton. Robots.

[9]  James Sean Humbert,et al.  Implementation of wide-field integration of optic flow for autonomous quadrotor navigation , 2009, Auton. Robots.

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

[11]  D. Izzo,et al.  Nonlinear model predictive control applied to vision-based spacecraft landing , 2013 .

[12]  Steve Parkes,et al.  LIDAR-Based GNC for Planetary Landing: Simulation with PANGU , 2003 .

[13]  Stéphane Viollet,et al.  Outdoor field performances of insect‐based visual motion sensors , 2011, J. Field Robotics.

[14]  Stergios I. Roumeliotis,et al.  Vision-Aided Inertial Navigation for Spacecraft Entry, Descent, and Landing , 2009, IEEE Transactions on Robotics.

[15]  Werner Reichardt,et al.  Processing of optical data by organisms and by machines , 1969 .

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

[17]  Antonis A. Argyros,et al.  Biomimetic centering behavior [mobile robots with panoramic sensors] , 2004, IEEE Robotics & Automation Magazine.

[18]  J. Koenderink,et al.  Facts on optic flow , 1987, Biological Cybernetics.

[19]  Mandyam V Srinivasan,et al.  Honeybees as a model for the study of visually guided flight, navigation, and biologically inspired robotics. , 2011, Physiological reviews.

[20]  Shinji Hokamoto,et al.  Comparison of integrated and nonintegrated wide-field optic flow for vehicle navigation , 2013 .

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

[22]  Robert E. Mahony,et al.  A terrain-following control approach for a VTOL Unmanned Aerial Vehicle using average optical flow , 2010, Auton. Robots.

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

[24]  Alexa Riehle,et al.  Directionally Selective Motion Detection by Insect Neurons , 1989 .

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

[26]  Christopher E. Neely,et al.  Mixed-mode VLSI optic flow sensors for in-flight control of a micro air vehicle , 2000, SPIE Optics + Photonics.

[27]  Robert E. Mahony,et al.  Landing a VTOL Unmanned Aerial Vehicle on a Moving Platform Using Optical Flow , 2012, IEEE Transactions on Robotics.

[28]  Patrick Fabiani,et al.  Low-speed optic-flow sensor onboard an unmanned helicopter flying outside over fields , 2013, 2013 IEEE International Conference on Robotics and Automation.

[29]  Dario Izzo,et al.  REAL-TIME LANDING BASED ON OPTIMALITY PRINCIPLES AND VISION , 2012 .

[30]  Dario Floreano,et al.  optiPilot: control of take-off and landing using optic flow , 2009 .

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

[32]  Nicolas H. Franceschini,et al.  Aerial robot piloted in steep relief by optic flow sensors , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[34]  Matthew Garratt,et al.  Biologically inspired climbing with a hexapedal robot , 2008 .

[35]  Stein Strandmoe,et al.  Toward a vision based autonomous planetary lander , 1999 .

[36]  Eric Bornschlegl,et al.  Toward an Autonomous Lunar Landing Based on Low-Speed Optic Flow Sensors , 2013 .