Abstract

Now a days, multi-robot systems are used widely in various applications. In this system, safety and coordination between the robots is an essential part. Our work investigates the constraint-based coordination for the first order multi-robot system operating under leaderless consensus (decentralized). The velocity of each robot is expressed through its cartesian (rectangular) components which are required to remain within prescribed bounds while maintaining safe separation to prevent inter-robot collisions and obstacle avoidance while formation. Formation, inter-robot collisions and obstacle avoidance are handled by formulating a control design as an optimization problem that incorporates control barrier functions, and graph-theory is applied for the coordination of robots with each other. A quadratic program controller is designed to enforce all the bounds, while minimizing a cost shaped by the barrier-function framework, yielding decentralized control inputs for each robot. The robots successfully reach the target formation in the frame under bounded velocity rectangular components while avoiding inter-robot collisions and obstacle collisions demonstrated at MATLAB simulation. First case is only the formation making of robots without any obstacles and the second one is with the obstacle placed in the frame to show that the proposed controller is efficiently achieving the task.

About the Speaker

Mr. Muzammil Ali received his bachelor’s degree in Electronic Engineering from Mehran University of Engineering and Technology (MUET), Jamshoro, Pakistan in 2021. He served as Assistant Manager in Power Cement Limited, Pakistan as Automation Engineer for 2.5 years. Currently, he is pursuing his Master’s degree in System and Control Engineering in the Control and Instrument Engineering Department at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, and hold a position as a Teaching Assistant in the same department. His research focuses on Swarm Formation Control, Intelligent control, Adaptive control, Optimization Algorithms, and Nonlinear Control.