Multi-task unmanned swarm control method based on dynamic optimal path planning

Abstract

With the continuous development of 5G, Internet of Things, unmanned driving, and cluster control technologies, the cooperative work of homogeneous or heterogeneous unmanned clusters will become the main application direction of unmanned systems. We proposed a multi-task unmanned swarm control method. The method establishes scene model by using cellular automata, uses cloud computing combined with multi-level edge computing as the control structure, and uses dynamic optimal path planning as the control algorithm to realize the coordinated control of unmanned clusters. We simulate the cluster cooperative control effect of this method and existing static control methods in theoretical scenarios and actual road network environments. This method solves the problem of deadlock caused by static shortest path planning of cluster control, and reduces the computation requirement of the whole system through multi-layer control calculation. The advanced nature of the method is illustrated by the analysis of the simulation results.