Using Artificial Neural Networks in the Visual Programming of Autonomous Robots


Shawn Best
Philip T. Cox

Author Addresses: 

Faculty of Computer Science
Dalhousie University
6050 University Ave.
PO Box 15000
Halifax, Nova Scotia, Canada
B3H 4R2


The use of Artificial Neural Networks (ANNs) to control autonomous robots has been quite extensively studied. Also, in recent years researchers have begun to investigate the notion of programming such robots using visual programming languages based on various programming and robot control models. Some of this work has focused on developing visual programming-by-demonstration (PBD) systems.

Here we extend the latter approach by proposing a visual pbd environment for autonomous robots based on anns. Within this environment, sensor-to-motor rules, called sensorimotor maps, are programmed by employing anns to match sensor outputs to actuator inputs. The goal is to create a programming environment in which the end-user is not required to have any knowledge of the underlying control model, ann programming in this case. In this regard, the current proposal appears more promising than previous attempts using the subsumption model.

Tech Report Number: 
Report Date: 
June 9, 2004
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