visual guidance of robot locomotion
[see Simon K. Rushton
, Jia Wen & Robert S.
Allison . Egocentric Direction and the Visual Guidance of Robot
Locomotion Background, Theory and Implementation. BMCV (forthcoming) .]
the recent suggestion  that humans rely on
perceived egocentric direction, rather than optic flow to guide
locomotion we implemented a robot guidance system.
relies on some ideas outlined elsewhere  to
guide target interception of static and moving targets.
For an brief
overview of some of the experimental work that undepins this work,
please check Simon's research page
assumption of the egocentric model is that approach to a target is
based upon maintaining a constant egocentric direction. Simply
put, you can intercept a target if on each step you check that the
target is at its previous egocentric direction, and if it is not then
turn so as to fix this. Provided that the direction you are
keeping the target at is less than 90degrees (measured relative to your
locomotor axis), then you will reach your target. The path you
will take is an equi-angular spiral. Illustrated below are a
family of constant-eccentricity trajectories (plan views) that
intercept (1) a static target; (2) a target moving with a constant
velocity; (3) an accelerating target.
system won't be calibrated (eg you don't know whether your target is at
15 degrees or -20 degrees), one way to deal with this is to use target
drift  . The image below shows the use of target
drift and over-compensation for guidance of an uncalibrated robot and
added an obstacle avoidance system that uses basic visual variables
such as time-to-contact (TTC; 4 , 5
), and trajectory [6 ,7 ].
Selection and optimisation of the calculation of these variables is
based upon recent experimental findings and computational models [ 8 ].
control law, derived from work on human interception [ 9
] and body-scaled parameters[10 ] can be used to
produce successful avoidance of static and moving obstacles.
note .avi files.
shapes - Triangle & Planar
implementation includes a simple to use interface for testing, an
object-orientated Matlab implementation and the ability for batch
processing of trials for performance evaluation.
have a partial implementation on a Nomad robot (lack of space to run
being the constraining factor).
is in development. Further information is available on request. The
work will be presented at BMCV in November. Others [ 11
, 12 ] have done work on the same problem, using
different approaches. We believe our model has significant advantages.
perception of direction
of direction with a mobile stereo head
situations-specific algorithms (such as recognition of dead-ends)
implementation on Nomad robot (partial implementation already)*
refinementy and optomisation of control laws
refinement and optomisation of calculation or pick-up of visual
varaiables (such as
trajectory and eye-movements
of egocentric direction: disparity field
of direction: flow field
of the influence of multiple obstacles
closest target by distance, TTC, direction, TTP?
of visually guided robot
* Jia Wen,
4080 project, Winter term
If you are
interested in any of these projects please email
 Rushton, S.K., Harris, J.M., Lloyd, M.L. & Wann,
J.P. (1998). Guidance of locomotion on foot uses perceived target
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 Rushton, S.K. & Harris, J.M. (submitted). The
utility of not changing direction and the visual guidance of locomotion
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 Lee, D.N.
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 Rushton, S.K. & Wann, J.P. (1999). Weighted
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 Peper, L., Bootsma, R,J,, Mestre, D.R. & Bakker,
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 Warren –
affordances and stepping
 Fajen, B.R., Warren, W.H., Temizer, S & Kaelbling,
L.P. (in press). A dynamical model of visually-guided steering,
obstacle avoidance, and route selection. International Journal of
 Khatib, O. (1986). Real-time obstacle avoidance for
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was supported in part by funds from National Science and Engineering
Research Council of Canada and Nissan Technical Center North America