A High-Precision Collaborative Control Algorithm for Multi-Agent System Based on Enhanced Depth Image Fusion Positioning

Abstract

The collaborative control of the multi-agent system (MAS) marks the trend of intelligent transportation system (ITS). However, the collaborative control of MAS with flexible sampling periods remains a challenge, because under-driven systems are prone to random delays, data loss and sensor failures in semi-unstructured environment. Against the background of the semi-unstructured environment in a Dutch greenhouse, this paper puts forward a universal collaborative motion control algorithm for the MAS of automated guided vehicles (AGVs), in the light of the first-order dynamics of the system. The proposed algorithm is called continuous-step-rotate-run (CSRR). Besides, the enhanced depth image fusion positioning (EDIFP) scheme was designed to mitigate the disturbances on the control algorithm, arising from flexible sampling periods and data loss. To verify its effectiveness, the CSRR control algorithm was tested on an MAS of three under-driven BigPan AGVs. The results demonstrate that our algorithm can collaboratively control the AGVs in an effective and stable manner. The simple algorithm offers a desirable solution to the collaborative control of various MASs.