Chengju Liu

CPG-Inspired Workspace Trajectory Generation and Adaptive Locomotion Control for Quadruped Robots

This paper deals with the locomotion control of quadruped robots inspired by the biological concept of central pattern generator (CPG). A control architecture is proposed with a 3-D workspace trajectory generator and a motion engine. The workspace trajectory generator generates adaptive workspace trajectories based on CPGs, and the motion engine realizes joint motion imputes. The proposed architecture is able to generate adaptive workspace trajectories online by tuning the parameters of the CPG network to adapt to various terrains. With feedback information, a quadruped robot can walk through various terrains with adaptive joint control signals. A quadruped platform AIBO is used to validate the proposed locomotion control system. The experimental results confirm the effectiveness of the proposed control architecture. A comparison by experiments shows the superiority of the proposed method against the traditional CPG-joint-space control method.

Central Pattern Generator Inspired Control for Adaptive Walking of Biped Robots

Inspired by the biological concept of central pattern generators (CPGs), this paper deals with adaptive walking control of biped robots. Using CPGs, a trajectory generator is designed consisting of a center-of-gravity (CoG) trajectory generator and a workspace trajectory modulation process. Entraining with feedback information, the CoG generator can generate adaptive CoG trajectories online and workspace trajectories can be modulated in real time based on the generated adaptive CoG trajectories. A motion engine maps trajectories from workspace to joint space. The proposed control strategy is able to generate adaptive joint control signals online to realize biped adaptive walking. The experimental results using a biped platform NAO confirm the effectiveness of the proposed control strategy.

CPG driven locomotion control of quadruped robot

According to biological evidences, central pattern generators (CPGs) are neural networks responsible for the generation of rhythmic movements for animals, such as breathing, heartbeat, and locomotion, even when isolated from the brain and sensory inputs. Inspired by this mechanism, researchers have proposed the CPG driven control method as a new way to generate rhythmic control policies for the locomotion of legged robots. In this work, we design a CPG control construction for controlling the locomotion of a quadruped robot, which is capable of realizing the different gaits and gait transitions. Firstly, a body CPG network is constructed by mutually coupled phase oscillators, which can produce multiple phase-locked oscillation patterns that correspond to the four basic quadruped gaits. The gait transitions can be realized by altering the internal oscillator parameters. Then, we design a robotic platform based on Webots and AIBO, and realize dynamic locomotion with the designed CPG network for AIBO. The Simulation and experimental results demonstrate the proposed CPG network is effective to generate gait patterns for quadruped robots.

Walking control strategy for biped robots based on central pattern generator

This paper deals with the walking control of biped robots inspired by biological concept of central pattern generator (CPG). A control architecture is proposed with a trajectory generator and a motion engine. The trajectory generator consists of a CoG (center of gravity) trajectory generator and a foot trajectory modulator. The CoG generator generates adaptive CoG trajectories online and the foot trajectories can be modulated based on the generated CoG trajectories. A biped platform NAO is used to validate the proposed locomotion control system. The experimental results confirm the effectiveness of the proposed control architecture.

Locomotion control of quadruped robots based on CPG-inspired workspace trajectory generation

This paper presents a locomotion control strategy for quadruped robots based on central pattern generator (CPG). The proposed control architecture consists of a workspace trajectory generator and a motion engine. The CPG-inspired trajectory generator can generate workspace trajectories and the motion engine can calculate the accurate joint control signals. Moreover, entrainment with sensory feedback information from robot-environment interaction, the presented control system can generate adaptive joint control signals. A quadruped platform AIBO is used to validate the proposed control architecture and experimental results confirm the effectiveness of the control system.

Active balance of humanoids with foot positioning compensation and non-parametric adaptation

To maintain human-like active balance for a humanoid robot, this paper proposes a novel adaptive non-parametric foot positioning compensation approach that can modify predefined step position and step duration online with sensor feedback. A constrained inverted pendulum model taking into account of supporting area to CoM acceleration is used to generate offline training samples with constrained nonlinear optimization programming. To speed up real-time computation and make online model adjustable, a non-parametric regression model based on extended Gaussian Process model is applied for online foot positioning compensation. In addition, a real-time and sample-efficient local adaptation algorithm is proposed for the non-parametric model to enable online adaptation of foot positioning compensation on a humanoid system. Simulation and experiments on a full-body humanoid robot validate the effectiveness of the proposed method. A novel adaptive non-parametric foot positioning compensation approach is proposed.A CIPM taking into account of supporting area to CoM acceleration is used.A non-parametric regression model based on extended Gaussian Process is used for online FPC.A real-time & sample-efficient local adaptation method is proposed for the non-parametric model.

Adaptive Walking Control of Biped Robots Using Online Trajectory Generation Method Based on Neural Oscillators

This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is designed to generate foot trajectory, and another set of neural oscillators can generate the trajectory of Center of Mass (CoM) online. Using a motion engine, the characteristics of the workspace are mapped to the joint space. The entraining property of the neural oscillators is exploited for adaptive walking in the absence of a priori knowledge of walking conditions. Sensory feedback is applied to modify the generated trajectories online to improve the walking quality. Furthermore, a staged evolutionary algorithm is developed to tune system parameters to improve walking performance. The developed control strategy is tested using a humanoid robot on irregular terrains. The experiments verify the success of the presented strategy. The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.

Synthesis of Matsuoka-Based Neuron Oscillator Models in Locomotion Control of Robots

In this paper we present a numerical study of the Matsuoka-based neuron oscillator model. The Matsuoka-based neuron oscillator model is one of the most popular CPG (central pattern generator) models in robot motion control. In this paper, numerical simulation is conducted to analyze the influence of the parameters on the output signals. A mass-spring-damper system is used as an example to analyze the entrainment properties of the neuron oscillator. The main engineering application methods of these CPG-inspired control methods are concluded. The motivation is to present a practical guide to researchers and engineers interested in the CPG-inspired control approaches.