Multi-Agent Systems: Planning, Perception and Control

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 2118

Special Issue Editor


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Guest Editor
Department of Mechanical and Mechatronics Engineering School of Engineering Southern Illinois University Edwardsville Edwardsville, IL 62026-1805, USA
Interests: fractional-order systems; dynamics; control; robotics; nonlocal operators; time-delay systems; optimization
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Special Issue Information

Dear Colleagues,

Multi-agent systems (MASs) have become increasingly important in various scientific and engineering fields, such as robotics, autonomous vehicles, swarm robotics, and traffic modeling. In these systems, planning helps agents to make informed decisions, perception enables them to understand their surroundings, and control ensures they execute the actions effectively. The growing interest in MAS is driven by the rising complexity of real-world challenges that require intelligent coordination among multiple agents. Integrating planning, perception, and control is at the core of advancing MAS, making research in this area both timely and crucial.

This Special Issue, ‘Multi-Agent Systems: Planning, Perception, and Control,’ aims to offer a platform for researchers and practitioners to share their insights on the latest developments, methodologies, and applications in the field of MAS. This very objective aligns seamlessly with one of the broader foci of MDPI, which is to advance knowledge and innovation in electronics, computer science, and engineering. MDPI has a longstanding tradition of publishing groundbreaking research, and we are confident that this Special Issue will continue this tradition.

We cordially invite submissions of original research articles and reviews that cover a wide range of topics related to the planning, perception, and control of multi-agent systems. While the list is not exhaustive, some suggested themes for submissions include the following:

  • Multi-agent path planning and coordination;
  • Perception and sensing in multi-agent systems;
  • Strategies for controlling autonomous agents;
  • Distributed control frameworks and paradigms;
  • Applications of multi-agent systems in robotics, autonomous vehicles, and smart cities;
  • Machine learning and artificial intelligence in multi-agent systems;
  • Ethical and societal considerations in MAS.

Dr. Arman Dabiri
Guest Editor

Manuscript Submission Information

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Keywords

  • multi-agent systems
  • autonomous vehicles
  • optimization
  • sensing and fusion
  • path and trajectory planning algorithms
  • perception algorithms and technologies
  • control strategies
  • robotics
  • swarm intelligence
  • machine learning

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Published Papers (1 paper)

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Research

18 pages, 2831 KiB  
Article
Enhanced Crowd Dynamics Simulation with Deep Learning and Improved Social Force Model
by Dapeng Yan, Gangyi Ding, Kexiang Huang, Chongzhi Bai, Lian He and Longfei Zhang
Electronics 2024, 13(5), 934; https://doi.org/10.3390/electronics13050934 - 29 Feb 2024
Cited by 1 | Viewed by 1658
Abstract
The traditional social force model (SFM) in crowd simulation experiences difficulty coping with the complexity of the crowd, limited by singular physical formulas and parameters. Recent attempts to combine deep learning with these models focus more on simulating specific states of crowds. This [...] Read more.
The traditional social force model (SFM) in crowd simulation experiences difficulty coping with the complexity of the crowd, limited by singular physical formulas and parameters. Recent attempts to combine deep learning with these models focus more on simulating specific states of crowds. This paper introduces an advanced deep social force model, influenced by crowd states. It utilizes deep neural networks to accurately fit crowd trajectory features, enhancing behavior simulation capabilities. Geometrical constraints within the model provide control over varied crowd behaviors, adjustable to simulate different crowd types. Before training, we use the SFM to refine behaviors in real trajectories with excessively small distances, aiming to enhance the general applicability of the model. Comparative experiments affirm the effectiveness of the model, showing comparable performance to both classic physical models and modern learning-based hybrid models in pedestrian simulations, with reduced collisions. In addition, the model has a certain ability to simulate crowds with high density and diverse behaviors. Full article
(This article belongs to the Special Issue Multi-Agent Systems: Planning, Perception and Control)
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