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.
| Document Type: | Conference Object |
|---|---|
| Language: | English |
| 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 |
| DOI: | https://doi.org/10.1007/978-3-319-46073-4_6 |
| 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 |
