Title :
A Unified Strategy for Landing and Docking Using Spherical Flow Divergence
Author :
McCarthy, Chris ; Barnes, Nick
Author_Institution :
NICTA Canberra Res. Lab., Australian Nat. Univ., Canberra, ACT, Australia
fDate :
5/1/2012 12:00:00 AM
Abstract :
We present a new visual control input from optical flow divergence enabling the design of novel, unified control laws for docking and landing. While divergence-based time-to-contact estimation is well understood, the use of divergence in visual control currently assumes knowledge of surface orientation, and/or egomotion. There exists no directly observable visual cue capable of supporting approaches to surfaces of arbitrary orientation under general motion. Central to our measure is the use of the maximum flow field divergence on the view sphere (max-div). We prove kinematic properties governing the location of max-div, and show that max-div provides a temporal measure of proximity. From this, we contribute novel control laws for regulating both approach velocity and angle of approach toward planar surfaces of arbitrary orientation, without structure-from-motion recovery. The strategy is tested in simulation, over real image sequences and in closed-loop control of docking/landing maneuvers on a mobile platform.
Keywords :
aerospace control; closed loop systems; image sequences; mobile robots; motion control; robot kinematics; robot vision; velocity control; arbitrary orientation; closed-loop control; divergence-based time-to-contact estimation; docking maneuver; egomotion; image sequences; kinematic properties; landing maneuver; max-div; maximum flow field divergence; mobile platform; optical flow divergence; proximity temporal measure; robot motor control; spherical flow divergence; surface orientation; unified control laws; view sphere; visual control; visual control input; Cameras; Joining processes; Optical imaging; Pattern analysis; Surface texture; Tin; Visualization; Robot vision; optical flow.; visual navigation; visuo-motor control;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2012.27