Online ordering is currently unavailable due to technical issues. We apologise for any delays responding to customers while we resolve this. For further updates please visit our website: https://www.cambridge.org/news-and-insights/technical-incident
We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure coreplatform@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
This paper presents a neural model to solve
the visual-tactile-motor coordination problem in robotic applications. The proposed neural
controller is based on the VAMC (Vector Associative Map) model.
This algorithm is based on the human biological system and
has the ability of learning the mapping that establishes the
relationship between the spatial and the motor coordinates. These spatial
inputs are composed of visual and force parameters. The LINCE
stereohead carries out a visual detection process, detecting the positions
of the object and of the manipulator. The artificial tactile
skins placed over the two fingers of the gripper measure
the force distribution when an object is touched. The neural
controller has been implemented for robotic operations of reaching and
object grasping. The reaching process is fed back in order
to minimize the Difference Vector (DV) between the visual projections
of the object and the manipulator. The stable grasping task
processes the force distribution maps detected in the contact with
the two surfaces of the gripper, in order to direct
the object into the robotic fingers. Experimental results have demonstrated
the robustness of the model and the accuracy of the
final pick-and-place process.
Recommend this
Email your librarian or administrator to recommend adding this to your organisation's collection.