Adaptive Digital Twin for UAV-Assisted Integrated Sensing, Communication, and Computation Networks

In this paper, we study a digital twin (DT)-empowered integrated sensing, communication, and computation network. Specifically, the users perform radar sensing and computation offloading on the same spectrum, while unmanned aerial vehicles (UAVs) are deployed to provide edge computing service. We first formulate a multi-objective optimization problem to minimize the beampattern performance of multi-input multi-output (MIMO) radars and the computation offloading energy consumption simultaneously. Then, we explore the prediction capability of DT to provide intelligent offloading decision, where the DT estimation deviation is considered. To track this challenge, we reformulate the original problem as a multi-agent Markov decision process and design a multi-agent proximal policy optimization (MAPPO) framework to achieve a flexible learning policy. Furthermore, the Beta-policy and attention mechanism are used to improve the training performance. Numerical results show that the proposed method is able to balance the performance tradeoff between sensing and computation functions, while reducing the energy consumption compared with the existing studies.Comment: 14 pages, 11 figures

GO-FEAP: Global Optimal UAV Planner Using Frontier-Omission-Aware Exploration and Altitude-Stratified Planning

Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Existing methods, however, are demonstrated to static local optima and two-dimensional exploration. To address these challenges, this paper introduces GO-FEAP (Global Optimal UAV Planner Using Frontier-Omission-Aware Exploration and Altitude-Stratified Planning), aiming to achieve efficient and complete three-dimensional exploration. Frontier-Omission-Aware Exploration module presented in this work takes into account multiple pivotal factors, encompassing frontier distance, nearby frontier count, frontier duration, and frontier categorization, for a comprehensive assessment of frontier importance. Furthermore, to tackle scenarios with substantial vertical variations, we introduce the Altitude-Stratified Planning strategy, which stratifies the three-dimensional space based on altitude, conducting global-local planning for each stratum. The objective of global planning is to identify the most optimal frontier for exploration, followed by viewpoint selection and local path optimization based on frontier type, ultimately generating dynamically feasible three-dimensional spatial exploration trajectories. We present extensive benchmark and real-world tests, in which our method completes the exploration tasks with unprecedented completeness compared to state-of-the-art approaches.Comment: 7 pages,29 figure