Modeling and Simulation of Hexapod Kinematics with Central Pattern Generator

Abstract

The revealed secrets of nature always led humans to their aspiring achievements. The fastest animal on land is Cheetah and similar robot has developed by engineers so far to attain a record speed of 20mph among legged robots. But in nature there are some insects those are far ahead of cheetah in speed with a unit of body length per second. Insects are small in their body size with legs usually countable from 4 to 12 or more. With more legs they can have more stability and can adapt to different terrain faster while walking. Six legged robot (hexapod) is generally expect to attain higher speed in terms of body length per second, since the nature has proof for it. Bio-inspired Central Pattern Generator (CPG) is in use for so far in robotic world to mimic the locomotion patterns of insects and other animals. Currently the hybrid controller of CPG and reflex is going on and this paper suggests a new architecture for the system. Neural Network modeled CPG acts as the motor neuron for each joint of the leg. In each instant a neural network models the gait of the robot by learning procedure from the reflex system. This is like the Central Nervous System (CNS) selecting gait of an animal according to the terrain that travels. CNS takes sensory feedback from eyes, force on each leg and body balance from cochlea to adapt the gait for current terrain. This paper in first place tries to simulate the gait patterns for a hexapod.