ROS-Based Multi-Agent Systems COntrol Simulation Testbed (MASCOT)

This paper presents a simulation testbed developed for testing and demonstration of decentralized control algorithms designed for multi-agent systems. Aimed at bridging a gap between theory and practical deployment of such algorithms, this testbed provides a simulator with multi-agent systems having quadcopter agents. It is used to test the control algorithms designed assuming simple agent dynamics like a single or double integrator. This is based on the fact that under certain assumptions, quadcopter dynamics can be modeled as a double integrator system. A gazebo simulator with a physics engine as ODE (Open Dynamics Engine) is used for simulating the dynamics of the quadcopter. Robot Operating System is used to develop communication networks and motion control algorithms for the different agents in the simulation testbed. The performance of the test bed is analyzed by implementing linear control laws such as position control, leaderless consensus, leader-follower consensus, and non-linear control law for min-max time consensus. This work is published as an open-source ROS package under MIT license at https://github.com/Avi241/mascot. A docker image is also developed for easy setup of the system.Comment: Accepted at 8th Indian Control Conferenc

Decentralized Coordination of Multiple Autonomous Vehicles

This dissertation focuses on the study of decentralized coordination algorithms of multiple autonomous vehicles. Here, the term decentralized coordination is used to refer to the behavior that a group of vehicles reaches the desired group behavior via local interaction. Research is conducted towards designing and analyzing distributed coordination algorithms to achieve desired group behavior in the presence of none, one, and multiple group reference states. Decentralized coordination in the absence of any group reference state is a very active research topic in the systems and controls society. We first focus on studying decentralized coordination problems for both single-integrator kinematics and double-integrator dynamics in a sampled-data setting because real systems are more appropriate to be modeled in a sampled-data setting rather than a continuous setting. Two sampled-data consensus algorithms are proposed and the conditions to guarantee consensus are presented for both fixed and switching network topologies. Because a number of coordination algorithms can be employed to guarantee coordination, it is important to study the optimal coordination problems. We further study the optimal consensus problems in both continuous-time and discrete-time settings via an linear-quadratic regulator (LQR)-based approach. Noting that fractional-order dynamics can better represent the dynamics of certain systems, especially when the systems evolve under complicated environment, the existing integer-order coordination algorithms are extended to the fractional-order case. Decentralized coordination in the presence of one group reference state is also called coordinated tracking, including both consensus tracking and swarm tracking. Consensus tracking refers to the behavior that the followers track the group reference state. Swarm tracking refers to the behavior that the followers move cohesively with the external leader while avoiding inter-vehicle collisions. In this part, consensus tracking is studied in both discrete-time setting and continuous-time settings while swarm tracking is studied in a continuous-time setting. Decentralized coordination in the presence of multiple group reference states is also called containment control, where the followers will converge to the convex hull, i.e., the minimal geometric space, formed by the group references states via local interaction. In this part, the containment control problem is studied for both single-integrator kinematics and double-integrator dynamics. In addition, experimental results are provided to validate some theoretical results