TY - GEN
T1 - Bezier curve model for efficient bio-inspired locomotion of low cost four legged robot
AU - Saputra, Azhar Aulia
AU - Tay, Noel Nuo Wi
AU - Toda, Yuichiro
AU - Botzheim, János
AU - Kubota, Naoyuki
PY - 2016/11/28
Y1 - 2016/11/28
N2 - 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 bioinspired 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.
AB - 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 bioinspired 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.
KW - Bezier based optimization
KW - Bio-inspired locomotion
KW - Passive neural control
UR - http://www.scopus.com/inward/record.url?scp=85006351798&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006351798&partnerID=8YFLogxK
U2 - 10.1109/IROS.2016.7759654
DO - 10.1109/IROS.2016.7759654
M3 - Conference contribution
AN - SCOPUS:85006351798
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 4443
EP - 4448
BT - IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Y2 - 9 October 2016 through 14 October 2016
ER -