2016
Bezier Curve Model for Efficient Bio-Inspired Locomotion of Low Cost Four Legged Robot
2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016)
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- First page
- 4443
- Last page
- 4448
- Language
- English
- Publishing type
- Research paper (international conference proceedings)
- DOI
- 10.1109/IROS.2016.7759654
- Publisher
- IEEE
This paper presents Bezier curve based passive neural control applied in bio-inspired locomotion in order to decrease the computational cost implemented for 4 legged animal robot which has 3 joints in each leg. Neural oscillator model is applied for generating the walking pattern in bio-inspired locomotion. Bezier curve based optimization represents passive neural control supported by evolutionary algorithm tor representing the relationship equation between neuron signal and reference joint signal. Passive neural control is implemented in order to reduce the neuron complexity in neuro-based locomotion by controlling 3 joints with one signal without decreasing the performance both in walking pattern and in its stability level, whereas one leg is represented by one motor neuron. Therefore, the 4 legged robot is controlled by 4 motor neurons which have feedback connection with ground and inertial sensor. In order to prove the effectiveness, we implemented the model in computer simulation and in a small 4 legged robot. This model can decrease the computational cost so it is possible to apply the model in either animal or humanoid robot with low frequency processor.
- Link information
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- DOI
- https://doi.org/10.1109/IROS.2016.7759654
- DBLP
- https://dblp.uni-trier.de/rec/conf/iros/SaputraTTBK16
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000391921704070&DestApp=WOS_CPL
- URL
- http://dblp.uni-trier.de/db/conf/iros/iros2016.html#conf/iros/SaputraTTBK16
- ID information
-
- DOI : 10.1109/IROS.2016.7759654
- DBLP ID : conf/iros/SaputraTTBK16
- Web of Science ID : WOS:000391921704070