Hierarchical optimum control of a novel wheel-legged quadruped

Khan, Rezwan Al Islam; Zhang, Chenyun; Pan, Yuzhen; Zhang, Anzheng; Li, Ruijiao; Zhao, Xuan; Shang, Huiliang

doi:10.1016/j.robot.2024.104775

Robotics and Autonomous Systems

2024

DOI: 10.1016/j.robot.2024.104775

|View full text |Cite

Hierarchical optimum control of a novel wheel-legged quadruped

Rezwan Al Islam Khan,

Chenyun Zhang,

Yuzhen Pan

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Multi-Agent Reinforcement Learning Tracking Control of a Bionic Wheel-Legged Quadruped

Khan,

Zhang,

Deng

et al. 2024

Machines

View full text Add to dashboard Cite

This paper presents a novel approach to developing control strategies for mobile robots, specifically the Pegasus, a bionic wheel-legged quadruped robot with unique chassis mechanics that enable four-wheel independent steering and diverse gaits. A multi-agent (MA) reinforcement learning (RL) controller is proposed, treating each leg as an independent agent with the goal of autonomous learning. The framework involves a multi-agent setup to model torso and leg dynamics, incorporating motion guidance optimization signal in the policy training and reward function. By doing so, we address leg schedule patterns for the complex configuration of the Pegasus, the requirement for various gaits, and the design of reward functions for MA-RL agents. Agents were trained using two variations of policy networks based on the framework, and real-world tests show promising results with easy policy transfer from simulation to the actual hardware. The proposed framework models acquired higher rewards and converged faster in training than other variants. Various experiments on the robot deployed framework showed fast response (0.8 s) under disturbance and low linear, angular velocity, and heading error, which was 2.5 cm/s, 0.06 rad/s, and 4°, respectively. Overall, the study demonstrates the feasibility of the proposed MA-RL control framework.

show abstract

Multi-Agent Reinforcement Learning Tracking Control of a Bionic Wheel-Legged Quadruped

Khan,

Zhang,

Deng

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Hierarchical optimum control of a novel wheel-legged quadruped

Cited by 1 publication

References 27 publications

Multi-Agent Reinforcement Learning Tracking Control of a Bionic Wheel-Legged Quadruped

Multi-Agent Reinforcement Learning Tracking Control of a Bionic Wheel-Legged Quadruped

Contact Info

Product

Resources

About