Event-triggered communication of multiple unmanned ground vehicles collaborative based on MADDPG.

In response to the problem of typical end-to-end communication strategies that cannot determine the communication interval and can only communicate at fixed frequencies, an event-triggered communication strategy is proposed based on deep reinforcement learning to solve the minimal communication problem in multi-unmanned ground vehicles collaboration. Firstly, an event-triggered architecture is established, which mainly includes a communication controller and provides trigger conditions. This ensures that communication occurs among multiple unmanned ground vehicle only when the conditions are met, significantly reducing the overall communication volume. Secondly, the trigger mechanism is optimized using the multiple agent deep deterministic policy gradient (MADDPG) algorithm, which improves the convergence speed of the algorithm. Simulation and real vehicle experiments show that with increasing iterations, the amount of communication data in the multiple unmanned ground vehicle system is reduced by 55. 74% while still accomplishing the collaborative tasks, thus validating the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]