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In this paper, we address the problem of flocking for networks of nonholonomic mobile robots with nonlinear dynamics given that a flocking algorithm for particles is known. Our approach relies on the use of near-identity change of coordinates that transform the nonlinear dynamics of the robot to a partially-linear normal form with a double-integrator linear subsystem. The flocking algorithm is then applied to the linear part. The inverse of the near-identity transformation provides the flocking algorithm for the networked nonholonomic robots. We prove the emergence of flocking behavior for robotic networks with nonlinear dynamics according to the formal definition of flocking in Olfati-Saber’s flocking paper (TAC ’06). Simulation results are provided for large-scale networks of two-wheeled robots with nonlinear dynamics as models of Khepera-III robots that demonstrate the effectiveness of our proposed transformation and algorithm.

Flocking for Networks of Mobile Robots with Nonlinear Dynamics