Efficient Updating of UGV-Assisted Reality Digital Twin: An AoDT-Oriented Approach

Reality digital twin (DT) model needs to be updated periodically, then the physical entity can be maintained efficiently. Since some physical entities may not be able to upload the entire status information actively, and also considering the universality of practical applications, we propose to use unmanned ground vehicle (UGV) as an information collector to assist in updating the reality DTs, where the UGV iterates over each target point (TP) to gather information by on-board sensors. Furthermore, considering the large amount of updating data and the limited computing resources on the UGV, we leverage mobile edge computing (MEC) technology to collaboratively process the data. In addition, to quantify the freshness of DTs, we propose the concept Age of DTs (AoDTs) as a metric to quantify the freshness of DT model. Thus, an AoDT minimization problem is established, which jointly optimizes offloading decisions, UGV waypoints selections, and TPs’ visiting orders, while also taking the obstacle avoidance into account. Considering the difficulty of the problem, we propose a novel low-complexity iterative algorithm to solve it. During which, a modified traveling salesman problem (TSP) solution is also proposed by taking into consideration the additional distance required to bypass the obstacles on each interwaypoints path. Finally, extensive simulation results show that the proposed algorithm can effectively reduce the AoDT, comparing to the benchmark algorithms.