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A Neural Field Approach to Obstacle Avoidance

  • Cognitive robotics aims at understanding biological processes, though it has also the potential to improve future robotics systems. Here we show how a biologically inspired model of motor control with neural fields can be augmented with additional components such that it is able to solve a basic robotics task, that of obstacle avoidance. While obstacle avoidance is a well researched area, the focus here is on the extensibility of a biologically inspired framework. This work demonstrates how easily the biological inspired system can be used to adapt to new tasks. This flexibility is thought to be a major hallmark of biological agents.

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Document Type:Conference Object
Author:Chun Kwang Tan, Paul G. Plöger, Thomas P. Trappenberg
Parent Title (English):Friedrich, Helmert et al. (Eds.): Ki 2016: Advances in artificial intelligence. 39th Annual German Conference on AI, Klagenfurt, Austria, September 26-30, 2016, Proceedings. Lecture Notes in Computer Science, Vol. 9904
First Page:69
Last Page:87
Date of first publication:2016/09/08
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2016/09/23