Lior Elazary KK6BWA

This project will implement a system for controlling a robot arm based on biologically inspired techniques. The goal of the project will be to have a robot arm reach for an object. This will be accomplished using a camera that will fovate on an object using its 2 degree-of-freedom (pan and tilt) mechanism which then will use a 6 degree-of-freedom arm to maneuver to the object, either touching the object or pointing to it. The solution for this problem resets in the mapping between vision coordinates to angle coordinates on the arm (assuming that the coordinates are already known either using motion detection or markers). Previous attempts to solve the mapping problem were to determine mathematically the formulas required for this mapping. Some of the problems with this method are that it is often very difficult to find the formulas to perform the mapping. Moreover, if one of the joins of the arm fails (due to a motor failure or arm link replacement) the formulas have to be manually recomputed.  The solution propose in this project will be to use Self-Organizing-Feature-Maps (SOFM) to learn the mapping. Such network will enable the robot to learn the mapping required to map vision coordinates to arm coordinates. Since the learning will be completely self-supervised, the arm will be able to adapt to changes as well as generalize the mapping (which means that not all coordinates mapping will have to be learned, saving on learning time). This project draws its inspiration from COG, a robotic humanoid developed at MIT. The system proposed here would be similar to the one proposed for COG in visually guided pointing. However, instead of using the cascade and ballistic maps, a SOFM will be used.