Rui Ming

Abstract: As the marine economy continues to expand, the importance of efficient and reliable marine data collection has become increasingly evident. This article investigates a multi-unmanned aerial vehicle (UAV)-assisted marine data collection network system, where multiple UAVs are deployed within a designated area to collect data from water buoy sensors (WBSs) and act as relays to offload the collected data to a central ship. The primary objective is to minimize the total system energy consumption, subject to constraints on access relationships, power scheduling, and movement trajectories. The formulated optimization problem is nonconvex and highly complex due to the coupling of multiple variables. To address this challenge, we propose an alternating optimization algorithm that jointly optimizes the trajectories of multiple UAVs, the access selection of WBSs, the trajectory of the ship, the data offloading decisions, and the UAV transmit power scheduling in an iterative manner. The algorithm leverages techniques such as successive convex approximation (SCA) and greedy strategies to efficiently solve the decomposed subproblems. Simulation results demonstrate that the proposed approach achieves significant performance improvements compared with several benchmark algorithms, highlighting its effectiveness in enhancing energy efficiency and system robustness in dynamic marine environments.

TL;DR: This thesis proposes a resource-saving navigation algorithm, DBT-RRT*, for microrobot systems, integrating target biasing, bidirectional search, and dynamic step size, achieving efficient planning in complex environments with minimal computational cost.

TL;DR: In this paper, an interval multi-objectives optimization design method based on blind number theory is presented, taking into account uncertain factors caused by manufacturing process, and the allowable interval strength of welding material and permissible interval bending and torsional stiffness are obtained by means of experiments and simulations.