1 research outputs found
Data Driven Computational Model for Bipedal Walking and Push Recovery
In this research, we have developed the data driven computational walking
model to overcome the problem with traditional kinematics based model. Our
model is adaptable and can adjust the parameter morphological similar to human.
The human walk is a combination of different discrete sub-phases with their
continuous dynamics. Any system which exhibits the discrete switching logic and
continuous dynamics can be represented using a hybrid system. In this research,
the bipedal locomotion is analyzed which is important for understanding the
stability and to negotiate with the external perturbations. We have also
studied the other important behavior push recovery. The Push recovery is also a
very important behavior acquired by human with continuous interaction with
environment. The researchers are trying to develop robots that must have the
capability of push recovery to safely maneuver in a dynamic environment. The
push is a very commonly experienced phenomenon in cluttered environment. The
human beings can recover from external push up to a certain extent using
different strategies of hip, knee and ankle. The different human beings have
different push recovery capabilities. For example a wrestler has a better push
negotiation capability compared to normal human beings. The push negotiation
capability acquired by human, therefore, is based on learning but the learning
mechanism is still unknown to researchers. The research community across the
world is trying to develop various humanoid models to solve this mystery.
Seeing all the conventional mechanics and control based models have some
inherent limitations, a learning based computational model has been developed
to address effectively this issue. In this research we will discuss how we have
framed this problem as hybrid system