A Multi-objective Optimization Approach for AGV-UAV Communications Based on Distributed Collaborative Beamforming

Automated guided vehicle (AGV) communications and networks have attracted extensive attention and have broad prospects in the field of wireless transmission. Unmanned aerial vehicle (UAV) can be used as flight base station (BS) to receive information from the ground AGVs. In this paper, we study an...

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Bibliographic Details
Published in:Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 158 - 163
Main Authors: Li, Yi, Wang, Aimin, Sun, Geng, Zheng, Xiaoya, Li, Jiahui
Format: Conference Proceeding
Language:English
Published: IEEE 09.10.2022
Subjects:
AGV communications
Array signal processing
Autonomous aerial vehicles
Collaboration
distributed collaborative beamforming
Energy consumption
energy efficiency
multi-objective optimization
Remotely guided vehicles
Simulation
Wireless communication
ISSN:2577-1655
Online Access:Get full text
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Summary:Automated guided vehicle (AGV) communications and networks have attracted extensive attention and have broad prospects in the field of wireless transmission. Unmanned aerial vehicle (UAV) can be used as flight base station (BS) to receive information from the ground AGVs. In this paper, we study an AGV-UAV communication scenario, in which a group of AGVs form an AGV-based virtual antenna array (AVAA) and communicate with different UAVs based on distributed collaborative beamforming (DCB). We formulate an AGV-UAV communication multi-objective optimization problem (AUCMOP) to simultaneously maximize the total transmission rate, minimize the total repositioning time of AGVs and minimize the total motion energy consumptions of AGVs by optimizing the positions, excitation current weights and moving speeds of AGVs, as well as the sequence for communicating with different UAVs. The formulated AUCMOP is complicated, and thus we propose an improved multi-objective ant lion optimization algorithm with Chebyshev chaos-opposition based learning solution initialization and hybrid solution update method (IMOALOCH). Simulation results show that the proposed IMOALOCH can solve the formulated AUCMOP effectively and it is superior to other comparison algorithms.
ISSN:2577-1655
DOI:10.1109/SMC53654.2022.9945126