Behavior Prediction Based Trust Evaluation for Adaptive Consensus of Quadrotors

Without proper treatment, a malfunctional quadrotor may bring severe consequences, e.g., becoming out of control, to the whole swarm. To tackle this problem, we develop a trust evaluations based consensus protocol. Specifically, each quadrotor in the swarm communicates with its connected neighbors, exchanging behavior predictions. By comparing the predicted and the actual behaviors of its neighbor regarding a pre-defined tolerance, each quadrotor assigns trust values to determine potentially legitimate or malfunctional companions. On this basis, an online adaptive controller adjusts each weight in the protocol corresponding to the trust evaluations designed before. We prove that, within proper tolerance, it is almost sure that the legitimate quadrotors can identify the malfunctional quadrotors through trust evaluations and ameliorate their effects on the whole system. Almost surely, the legitimate quadrotors can converge to their center in a finite time. We verify our method through MATLAB and GAZEBO. In particular, with our proposed method, the swarm system discussed in this paper is able to reach position and velocity consensus in the presence of malfunctional quadrotors.