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Dear Colleagues,

Multiagent systems, such as collaborative robots, UAVs, and smart cars, are emerging in recent years. Localization is a critical foundation for the operation of multiagent systems. The smart agent is now widely equipped with different kinds of sensors onboard, including global positioning systems (GPS), inertial measurement units (IMUs), ultra-wide band (UWB) devices for accurate wireless ranging measurements, cameras, light detection and ranging (LIDAR) sensors, and radio detection and ranging (RADAR) sensors. The processing power of the agents is also developing very fast. Using the embedded CPU and GPU onboard, the agent can process the multimodel, multimedia sensing data collected from the sensors to conduct distributed location and mapping. This Special Issue focuses on distributed localization algorithms in multiple agent systems.

Dr. Yongcai Wang
Guest Editor

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Keywords

multiagent systems
distributed localization
coorperative localization
barycentric localization
active localization
multimodal localization
location and mapping
multisensor fusion based localization
multiagent formation control

Published Papers (2 papers)

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Research

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18 pages, 2510 KiB

Open AccessArticle

Trajectory Optimization of Laser-Charged UAVs for Charging Wireless Rechargeable Sensor Networks

by Ning Liu, Chuanwen Luo, Jia Cao, Yi Hong and Zhibo Chen

Sensors 2022, 22(23), 9215; https://doi.org/10.3390/s22239215 - 27 Nov 2022

Cited by 5 | Viewed by 1357

Abstract

This paper considers a laser-powered unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system. In the system, a UAV is dispatched as an energy transmitter to replenish energy for battery-limited sensors in a wireless rechargeable sensor network (WRSN) by transferring radio frequency (RF) signals, and a mobile unmanned vehicle (MUV)-loaded laser transmitter travels on a fixed path to charge the on-board energy-limited UAV when it arrives just below the UAV. Based on the system, we investigate the trajectory optimization of laser-charged UAVs for charging WRSNs (TOLC problem), which aims to optimize the flight trajectories of a UAV and the travel plans of an MUV cooperatively to minimize the total working time of the UAV so that the energy of every sensor is greater than or equal to the threshold. Then, we prove that the problem is NP-hard. To solve the TOLC problem, we first propose the weighted centered minimum coverage (WCMC) algorithm to cluster the sensors and compute the weighted center of each cluster. Based on the WCMC algorithm, we propose the TOLC algorithm (TOLCA) to design the detailed flight trajectory of a UAV and the travel plans of an MUV, which consists of the flight trajectory of a UAV, the hovering points of a UAV with the corresponding hovering times used for the charging sensors, the hovering points of a UAV with the corresponding hovering times used for replenishing energy itself, and the hovering times of a UAV waiting for an MUV. Numerical results are provided to verify that the suggested strategy provides an effective method for supplying wireless rechargeable sensor networks with sustainable energy. Full article

(This article belongs to the Special Issue Distributed Localization Algorithms in Multi-Agent Systems)

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Review

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28 pages, 1723 KiB

Open AccessReview

Distributed Relative Localization Algorithms for Multi-Robot Networks: A Survey

by Shuo Wang, Yongcai Wang, Deying Li and Qianchuan Zhao

Sensors 2023, 23(5), 2399; https://doi.org/10.3390/s23052399 - 21 Feb 2023

Cited by 9 | Viewed by 3157

Abstract

For a network of robots working in a specific environment, relative localization among robots is the basis for accomplishing various upper-level tasks. To avoid the latency and fragility of long-range or multi-hop communication, distributed relative localization algorithms, in which robots take local measurements and calculate localizations and poses relative to their neighbors distributively, are highly desired. Distributed relative localization has the advantages of a low communication burden and better system robustness but encounters challenges in the distributed algorithm design, communication protocol design, local network organization, etc. This paper presents a detailed survey of the key methodologies designed for distributed relative localization for robot networks. We classify the distributed localization algorithms regarding to the types of measurements, i.e., distance-based, bearing-based, and multiple-measurement-fusion-based. The detailed design methodologies, advantages, drawbacks, and application scenarios of different distributed localization algorithms are introduced and summarized. Then, the research works that support distributed localization, including local network organization, communication efficiency, and the robustness of distributed localization algorithms, are surveyed. Finally, popular simulation platforms are summarized and compared in order to facilitate future research and experiments on distributed relative localization algorithms. Full article

(This article belongs to the Special Issue Distributed Localization Algorithms in Multi-Agent Systems)

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