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Applied Sciences
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Artificial Intelligence (AI) for Sensing and Unmanned Systems
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Artificial Intelligence (AI) for Sensing and Unmanned Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 2928

Special Issue Editors

Dr. He Wang

E-Mail Website
Guest Editor
School of Mathematics, Southeast University, Nanjing 210096, China
Interests: anti-disturbance control; distributed control; distributed observer
Special Issues, Collections and Topics in MDPI journals
Dr. Huazhou Hou

E-Mail Website
Guest Editor
School of Automation, Southeast University, Nanjing 210096, China
Interests: sliding mode control and its applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guilin Qi

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Interests: natural language processing; knowledge graph; multimodal learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Countries worldwide are working to develop artificial intelligence (AI). Since the beginning of 2018 alone, more than 15 countries have launched national strategies for AI. Following the rise of the Internet of Things, network-based swarm intelligence has become an inevitable trend, and will be widely applied in many important fields, such as national defense, autonomous systems, intelligent transportation, intelligent logistics, and smart power grids. One of the core ideologies of swarm intelligence is cooperation. Accordingly, this Special Issue aims to present the state-of-the-art ideas of cooperation in swarm intelligence.

Dr. He Wang
Dr. Huazhou Hou
Prof. Dr. Gui-Lin Qi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI)
  • automatic control
  • cooperative perception
  • cooperative decision
  • cooperative optimization
  • cooperative control
  • cooperative estimation
  • natural language understanding (NLP)

Published Papers (2 papers)

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Research

25 pages, 25477 KiB  
Article
Research on Dynamic Target Search for Multi-UAV Based on Cooperative Coevolution Motion-Encoded Particle Swarm Optimization
by Yiyuan Li, Weiyi Chen, Bing Fu, Zhonghong Wu, Lingjun Hao and Guang Yang
Appl. Sci. 2024, 14(4), 1326; https://0-doi-org.brum.beds.ac.uk/10.3390/app14041326 - 06 Feb 2024
Viewed by 652
Abstract
Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search [...] Read more.
Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search algorithm is designed to tackle the moving target search issue effectively. Firstly, a Markov process-based target motion model considering the uncertainty of target motion is investigated. Secondly, Bayesian theory is used to formulate the moving target search as an optimization problem where the objective function is defined as maximizing the cumulative probability of detection of the target in finite time. Finally, the problem is solved based on the CC-MPSO algorithm to obtain the optimal search path nodes. The motion encoding mechanism converts the search path nodes into a set of motion paths, which enables more flexible handling of UAV trajectories and improves the efficiency of dynamic path planning. Meanwhile, the cooperative coevolution optimization framework enables collaboration between different UAVs to improve global search performance through multiple swarm information sharing, which helps avoid falling into local optimal solutions. The simulation results show that the CC-MPSO algorithm demonstrates efficacy, reliability, and superior overall performance when compared to the five commonly used swarm intelligence algorithms. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Sensing and Unmanned Systems)
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21 pages, 3581 KiB  
Article
Bayesian-Based Traffic Safety Evaluation Study for Driverless Infiltration
by Yinhao Wang, Junyou Zhang and Guansheng Wu
Appl. Sci. 2023, 13(22), 12291; https://0-doi-org.brum.beds.ac.uk/10.3390/app132212291 - 14 Nov 2023
Cited by 1 | Viewed by 1034
Abstract
Although driverless technology belongs to the frontier of science and technology, there is no sufficient actual data. From the lack of a comprehensive systematic evaluation method of traffic safety under driverless penetration, considering the impact of the three core systems of driverless perception, [...] Read more.
Although driverless technology belongs to the frontier of science and technology, there is no sufficient actual data. From the lack of a comprehensive systematic evaluation method of traffic safety under driverless penetration, considering the impact of the three core systems of driverless perception, decision-making, control, and the complex road factors on the safety of driving, we review the main risk causal factors through the analysis of the accident causal model STAMP and put forward the fusion of the Leaky Noisy-OR Gate and Bayesian network model. The Bayesian network professional analysis tool GeNIe 2.0 was used to simulate, analyze, and evaluate the driverless traffic risk Bayesian network model, which accurately assessed the traffic safety risk under driverless penetration and diagnosed and identified the sensitive risk factors. The results of this study concluded that, in order to effectively deal with the future traffic safety risks of driverless vehicles, vehicle enterprises, research institutions, software and hardware suppliers in the field of driverless driving should strengthen the research and development and manufacturing of key components such as perception, and enhance the depth of research and development of AI decision-making software, which provides a new way of thinking about the management of the safety risk of driverless traffic and a theoretical basis for the development and implementation of risk control measures. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Sensing and Unmanned Systems)
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