Swarm Intelligence and Swarm Robotics: Latest Advances and Prospects

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

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 2586

Special Issue Editors


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Guest Editor
SnT, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg
Interests: bio-inspired algorithms; robotic swarms; unmanned autonomous systems; computer simulations

E-Mail Website
Guest Editor
FSTM/DCS / SnT, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg
Interests: artificial intelligence; metaheuristics; automated algorithm design; unmanned autonomous systems
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Special Issue Information

Dear Colleagues,

We are inviting submissions to our Special Issue entitled “Swarm Intelligence and Swarm Robotics: Latest Advances and Prospects”.

Distributed autonomous systems provide multiple economic advantages. A promising way to manage these distributed systems is through the utilization of swarm intelligence. However, designing fully distributed and autonomous systems remains a challenging open problem since the global system performance is difficult to predict. The problem becomes even more challenging since it is multi-objective in nature.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in swarm intelligence and swarm robotics which include, but are not limited to, the following topics: surveillance systems, distributed systems, modelling and simulation, cooperative control, swarm intelligence, swarms of drones, multi-agent systems, self-organization, robot formation control, distributed machine learning, collective mapping, and UAV traffic management (UTM).

Both theoretical and experimental studies are welcome, as well as comprehensive reviews and survey papers. We look forward to receiving submissions of your high-quality research work.

Dr. Daniel H. Stolfi
Dr. Grégoire Danoy
Guest Editor

Manuscript Submission Information

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Keywords

  • swarm intelligence
  • swarms of robots
  • bio-inspired algorithms
  • artificial intelligence
  • surveillance systems
  • machine learning
  • robot formation control
  • traffic management (UTM)
  • multi-swarm systems

Published Papers (1 paper)

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Research

16 pages, 4936 KiB  
Article
Optimising Robot Swarm Formations by Using Surrogate Models and Simulations
by Daniel H. Stolfi and Grégoire Danoy
Appl. Sci. 2023, 13(10), 5989; https://doi.org/10.3390/app13105989 - 12 May 2023
Viewed by 1134
Abstract
Optimising a swarm of many robots can be computationally demanding, especially when accurate simulations are required to evaluate the proposed robot configurations. Consequentially, the size of the instances and swarms must be limited, reducing the number of problems that can be addressed. In [...] Read more.
Optimising a swarm of many robots can be computationally demanding, especially when accurate simulations are required to evaluate the proposed robot configurations. Consequentially, the size of the instances and swarms must be limited, reducing the number of problems that can be addressed. In this article, we study the viability of using surrogate models based on Gaussian processes and artificial neural networks as predictors of the robots’ behaviour when arranged in formations surrounding a central point of interest. We have trained the surrogate models and tested them in terms of accuracy and execution time on five different case studies comprising three, five, ten, fifteen, and thirty robots. Then, the best performing predictors combined with ARGoS simulations have been used to obtain optimal configurations for the robot swarm by using our proposed hybrid evolutionary algorithm, based on a genetic algorithm and a local search. Finally, the best swarm configurations obtained have been tested on a number of unseen scenarios comprising different initial robot positions to evaluate the robustness and stability of the achieved robot formations. The best performing predictors exhibited speed increases of up to 3604 with respect to the ARGoS simulations. The optimisation algorithm converged in 91% of runs and stable robot formations were achieved in 79% of the unseen testing scenarios. Full article
(This article belongs to the Special Issue Swarm Intelligence and Swarm Robotics: Latest Advances and Prospects)
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