In this thesis, we introduce distributed formation control strategies to reach an intended linear formation for agents with a diverse array of dynamics. The suggested technique is distributed entirely, does not include inter-agent cooperation or a barrier of orientation, and can be applied using relative location information gained by agents in their local cooperation frames. We illustrate how the control optimized for agents with the simpler dynamic model, i.e., the dynamics of the single integrator, can be expanded to holonomic agents with higher dynamics such as quadrotors and non-holonomic agents such as unicycles and cars. Our suggested approach makes feedback saturations, unmodelled dynamics, and switches stable in the sensing topology. We also indicate that the control is relaxed as agents will travel along with a rotated and scaled control direction without disrupting the convergence to the desired formation. We can implement this observation to design a distributed strategy for preventing collisions. In simulations, we explain the suggested solution and further introduce a distributed robotic framework to experimentally validate the technique. Our experimental platform is made up of off-the-shelf devices that can be used to evaluate other multi-agent algorithms and verify them
