Exploiting the inherent coordination of central pattern generator in the Control of humanoid robot walking

Abstract

In this work, a bio-inspired central pattern generator (CPG) controller is developed to achieve an adaptive and robust walking control. CPG is an approach which tries to model the local control system of bipedal animals through a neural oscillator based network structure. This work includes designing a coordination connection between oscillators in the CPG; classifying the sensory feedback to the CPG; building a humanoid robot for the real implementation and controlling the robot with the proposed CPG controller.

Coordination among oscillators in the CPG is critical and important for the adaptive walking control. A CPG is usually composed of many coupled oscillators which output rhythm trajectories. These oscillators need to coordinate with other oscillators when there are external perturbations. By using the entrainment property of the neural oscillator, we develop a coordination connection between oscillators. With this connection, the main oscillator can adjust the phase of other oscillators for the coordination purpose. With this coordination connection, a CPG controller is developed to control the walking of a 2D bipedal robot. The simulation results show that the coordination connection enables the CPG controller to maintain the phase relationship among oscillators after the push applied on the robot. This helps the robot to maintain the stability after the pushes are applied.

Another topic studied in the thesis is sensory feedback classification. The sensory feedbacks modulate the output of the oscillator and enable an adaptive behavior to the environment changes. Based on the way of modification to the oscillator output, we classify the sensory inputs into three types: inhibition input, triggering input and modification input. The purpose of this classification is to make the feedback design easier. With these three types of sensory inputs, the CPG controller can generate the reference trajectories for the 3D dynamic walking. In the simulation, the CPG controller is used to control a 3D stepping motion first. The sensory feedbacks modify the output of the oscillators to balance the robot motion when pushes are applied. After the stepping experiments, a stable 3D level ground walking is achieved by adding the forward motion trajectories.

To further test the controller, we implement it to control our physical humanoid robot NUSBIP-III ASLAN. ASLAN is a newly developed robot which serves as a platform to test different walking algorithms. It is a fully autonomous humanoid robot which has an approximate height of 120cm and an approximate weight of 60kg. It has 23 DoFs it total with two arms, two legs and one head. We have successfully implemented the CPG controller on ASLAN for stepping and walking motion. The robot shows a stable walking behavior with the CPG controller.