Projects:2019s2-23301 Robust Formation Control for Multi-Vehicle Systems

Formation control has been widely used in the control of robots, sicne it can improve the overall efficency of the system. In this project, we aim to design a robust formation control for multi-vehicle system, in which the system can deal with at least one network problem or physical failure.

Introduction

Formation control of multi-agent systems (MASs) has been widely used for cooperative tasks in such applications as terrain exploration, mobile networks and traffic control. However, the communication-induced problems and the high failure risk of increasing equipments has created a number of challenges for the security of MASs. The objective of this project is to design a robust formation control strategy for a multi-vehicle system against communication and physical failures (e.g., network attacks, link failures, packet dropouts, sensor/actuator faults).

The vehicles are designed to detect the local environments by visual navigation and achieve a self-organisation formation. The robust fault-tolerant control strategy is investigated to deal with at leas one network problem or physical failure. The effectiveness of the formation control strategy and its robustness should be verified by both simulations and experiments. Potential applications are in large flexibility MASs and high-security Cyber-Physical Systems. Currently, our lab is equipped with a multi-vehicle platform, consisting of quadrotors, ground robots and camera location systems. Algorithms are developed by either Matlab Code or C language. MATLAB, Simulink, OpenGL, Motive and Visual Studio are possiblesoftware to be chosen for this project.

Project team

Student members

• Abdul Rahim Mohammad
• Jie Yang
• Kamalpreet Singh
• Zirui Xie

Supervisors

• Prof. Peng Shi
• Prof. Cheng-Chew Lim

Advisors

• Xin Yuan
• Yuan Sun
• Yang Fei
• Zhi Lian

Objectives

Design a robust formation control for multi-vehicle system to achieve:

• Self-decision making
• Environment detection
• Communication
• Obstacle avoidance
• Tolerance to physical or network problem

Background

Autonomous Control System

Autonomous control system has the power and ability for self-governance in the performance of control functions. They’re composed of a collection of hardware and software, which can perform the necessary control functions without intervention, or over extended time periods. There’re several degrees of autonomy. Conventional fixed controllers can be considered to have a restricted class of plant parameter variations and disturbances, while in a high degree of autonomy, controller must be able to perform a number of functions beyond conventional functions, such as regulation or tracking.

Agent

For the most part, we are happy to accept computers as obedient, literal, unimaginative servants. For many applications, it is entirely acceptable. However, for an increasingly large number of applications, we require systems that can decide for themselves what they need to do in order to achieve the objectives that we delegate to them. Such computer systems are known as agents. Agents that must operate robustly in rapidly changing, unpredictable, or open environments, where there is a significant possibility that actions can fail, are known as intelligent agents, or sometimes autonomous agents.

Multi Agent System

A group of loosely connected autonomous agents act in an environment to achieve a common goal. This is done by cooperating, and sharing knowledge with each other Multi-agent systems have been widely adopted in many application domains because of the beneficial advantages offered. Some of the benefits available by using MAS technology in large systems are

• An increase in the speed and efficiency of the operation due to parallel computation and asynchronous operation
• A graceful degradation of the system when one or more of the agents fail. It thereby increases the reliability and robustness of the system
• Scalability and flexibility- Agents can be added as and when necessary
• Reduced cost- This is because individual agents cost much less than a centralized architecture
• Reusability-Agents have a modular structure and they can be easily replaced in other systems or be upgraded more easily than a monolithic system

References

[1] Wooldridge, M (2002). An Introduction to MultiAgent Systems. John Wiley & Sons. ISBN 978-0-471-49691-5

[2] Balaji, P., & Srinivasan, D. (2010). An introduction to multi-agent systems. Studies in Computational Intelligence, 310, 1-27.

[3] Hong-Jun M., & Guang-Hong Y. (2016). Adaptive Fault Tolerant Control of Cooperative Heterogeneous Systems With Actuator Faults and Unreliable Interconnections. IEEE Transactions on Automatic Control, 61(11), 3240-3255.

[4] Oh k, Park M, & Ahn H. (2015). A survey of multi-agent formation control. Automatica, 53, 424-440.

[5] Khatib, O. (1986). Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research, 5(1), 90–98. https://doi.org/10.1177/027836498600500106

[6] Autonomous Ground Vehicles Self-Guided Formation Control https://github.com/vitsensei/Trionychid-Formation-Control