TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Tan, Chun Kwang
A1  - Plöger, Paul G.
A1  - Trappenberg, Thomas P.
T1  - A Neural Field Approach to Obstacle Avoidance
T2  - 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
N2  - 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.
U6  - https://doi.org/10.1007/978-3-319-46073-4_6
DO  - https://doi.org/10.1007/978-3-319-46073-4_6
SP  - 69
EP  - 87
ER  -