Surveyor Robotics Journal

Surveyor Robotics Journal

	email: support@surveyor.com web: Surveyor Corporation rss: Subscribe Archives August 2010 July 2010 June 2010 May 2010 April 2010 March 2010 February 2010 January 2010 December 2009 November 2009 October 2009 September 2009 August 2009 July 2009 June 2009 May 2009 April 2009 March 2009 February 2009 January 2009 December 2008 November 2008 October 2008 September 2008 August 2008 September 2008 August 2008 July 2008 June 2008 May 2008 April 2008 March 2008 February 2008 January 2008 December 2007 November 2007 October 2007 September 2007 August 2007 July 2007 June 2007 May 2007 April 2007 March 2007 February 2007 January 2007 December 2006 November 2006 October 2006 September 2006 August 2006 July 2006 June 2006 May 2006 April 2006 March 2006 Februray 2006 January 2006	Mon, 08 Jan 2007 first steps in enabling adaptive / emergent behavior in the SRV-1 I have recently been thinking about the functions we might add to firmware that could enable the robot to locate its position based on visual cues and navigate through an area with a sense of purpose. We need this capability for RoboCup soccer play as well as variety of other activies (self-guided remote monitoring, object searches, execution of specific tasks, etc). This is not a new problem - many researchers have focused energy on "simultaneous localization and mapping (SLAM)", and a number of interesting techniques have been explored. My original thought was to develop some kind of map storage mechanism and some pattern matching routines, and that would give us a base level of functionality, but such an approach wouldn't easily adapt to changes in the environment or varying lighting conditions. Though both were written 20+ years ago, two books that have shaped my thinking on this subject are "Vehicles, Experiments in Synthetic Psychology" by Valentino Braitenberg (1984) and "Self-Organization and Associate Memory" by Teuvo Kohonen (1987) (plus I ordered a more recent volume by the same author called "Self Organizing Maps"). Both books essentially explore artificial neural network structures which might apply nicely to the types of capabilities we wish to develop. In particular, there are some specific functions which I'd like to add: color classification that can handle to various lighting conditions, textures, and multi-color patterns (think of a checkerboard floor or a mixed color carpet) in place of our current fixed threshold approach shape/pattern recognition, so that we might discriminate blobs of like color that have different form spacial localization, based on the occurrence of features that the robot had previously "learned", as well as adaptation to changes In all cases, it would seem that artificial neural network techniques might be appropriate, especially if we can scale the input and output dimensions in order to keep the computational requirements within reasonable bounds. I found a nice tool for modeling some of these techniques - it is the Python-based "Conx" code that is found in the Pyro Robotics toolkit. Pyro has a forthcoming interface to the SRV-1, plus we're already working in Python with the pySRV1Console, so this should be helpful in modeling some of the techniques on the host before committing to implementation in firmware on the robot. Once we have something interesting to test, we'll post something here in the journal. Posted Mon, 08 Jan 2007 14:25 \| HTML Link \| see additional stories ...