Central Pattern Generator-based Locomotion Control for Robots
The aim of this research is to understand the neural pathway comprising the CPG and associated brain parts; and put it to real life practical applications. In particular, we develop algorithms for efficient autonomous neuromorphic locomotion control. In the initial stages, we developed algorithms to classify the various gait phases. This is useful in detection of abnormal gait patterns. We also developed a bio-inspired algorithm for application in a robotic exoskeleton for rehabilitation in hemiplegic stroke patients.
The goals of the project are:
- To understand the locomotion CPG in humans and its control by the various brain parts particularly the cerebellum and map based navigation through the hippocampus.
- To utilize the knowledge in developing efficient robotic control algorithms.
We use the hexapod robot and the quadcopter for implementing our locomotion algorithms.