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Applied Sciences
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Artificial Intelligence within Robot Swarms
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Artificial Intelligence within Robot Swarms

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 August 2022) | Viewed by 13166

Special Issue Editor

Dr. Yara Khaluf

E-Mail Website
Guest Editor
Information Technology Group, Department of Social Sciences, Wageningen University and Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands
Interests: robot swarms; artificial intelligence; mathematical modeling; design of distributed systems; computational modeling; distributed AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Swarm robotics is a research field that focuses on the combination of swarm intelligence and robotics. Swarm intelligence describes the mechanisms of intelligence achieved at the system (global) level resulting from individuals’ simple behaviors and intensive interactions. Swarm intelligence is observed across different natural systems, including ant colonies, bird colonies, etc. Importing swarm intelligence to simple robots evolves into a promising distributed and autonomous system that shows great potential in several application areas, collectively referred to as swarm robotics.

Today, despite the significant advantages of robot swarms (e.g., their high resilience and scalability), they are mostly still restricted to the laboratory. This is due to a number of challenges including the safety of these systems, the dynamic environments in which they are deployed, their coordination under realistic circumstances, and their communication mechanisms. To overcome such challenges and advance swarm robotics research, these systems can benefit from numerous approaches developed recently in the field of artificial intelligence, including machine learning algorithms, complex networks, advanced decision-making algorithms, and others.

Dr. Yara Khaluf
Guest Editor

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

  • swarm robotics
  • decision-making algorithms (mechanisms/strategies)
  • communication (complex) networks
  • self-organized algorithms for robot swarms
  • machine learning algorithms

Published Papers (4 papers)

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Research

24 pages, 1412 KiB  
Article
Learning to Optimise a Swarm of UAVs
by Gabriel Duflo, Grégoire Danoy, El-Ghazali Talbi and Pascal Bouvry
Appl. Sci. 2022, 12(19), 9587; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199587 - 24 Sep 2022
Cited by 1 | Viewed by 6212
Abstract
The use of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase in interest in the past few years. Current applications mainly depend on single UAV operations, which face critical limitations such as mission range or resilience. Using several autonomous UAVs as a [...] Read more.
The use of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase in interest in the past few years. Current applications mainly depend on single UAV operations, which face critical limitations such as mission range or resilience. Using several autonomous UAVs as a swarm is a promising approach to overcome these. However, designing an efficient swarm is a challenging task, since its global behaviour emerges solely from local decisions and interactions. These properties make classical multirobot design techniques not applicable, while evolutionary swarm robotics is typically limited to a single use case. This work, thus, proposes an automated swarming algorithm design approach, and more precisely, a generative hyper-heuristic relying on multi-objective reinforcement learning, that permits us to obtain not only efficient but also reusable swarming behaviours. Experimental results on a three-objective variant of the Coverage of a Connected UAV Swarm problem demonstrate that it not only permits one to generate swarming heuristics that outperform the state-of-the-art in terms of coverage by a swarm of UAVs but also provides high stability. Indeed, it is empirically demonstrated that the model trained on a certain class of instances generates heuristics and is capable of performing well on instances with a different size or swarm density. Full article
(This article belongs to the Special Issue Artificial Intelligence within Robot Swarms)
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12 pages, 1417 KiB  
Article
Robot Swarms Decide under Perception Errors in Best-of-N Problems
by Yara Khaluf
Appl. Sci. 2022, 12(6), 2975; https://0-doi-org.brum.beds.ac.uk/10.3390/app12062975 - 15 Mar 2022
Viewed by 1434
Abstract
Robot swarms have been used extensively to examine best-of-N decisions; however, most studies presume that robots can reliably estimate the quality values of the various options. In an attempt to bridge the gap to reality, in this study, we assume robots with low-quality [...] Read more.
Robot swarms have been used extensively to examine best-of-N decisions; however, most studies presume that robots can reliably estimate the quality values of the various options. In an attempt to bridge the gap to reality, in this study, we assume robots with low-quality sensors take inaccurate measurements in both directions of overestimating and underestimating the quality of available options. We propose the use of three algorithms for allowing robots to identify themselves individually based on both their own measurements and the measurements of their dynamic neighborhood. Within the decision-making process, we then weigh the opinions of robots who define themselves as inaccurately lower than others. Our research compares the classification accuracy of the three algorithms and looks into the swarm’s decision accuracy when the best algorithm for classification is used. Full article
(This article belongs to the Special Issue Artificial Intelligence within Robot Swarms)
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24 pages, 1260 KiB  
Article
Imitation Learning with Graph Neural Networks for Improving Swarm Robustness under Restricted Communications
by Ce Guo, Pengming Zhu, Zhiqian Zhou, Lin Lang, Zhiwen Zeng and Huimin Lu
Appl. Sci. 2021, 11(19), 9055; https://0-doi-org.brum.beds.ac.uk/10.3390/app11199055 - 28 Sep 2021
Viewed by 2308
Abstract
This paper focuses on generating distributed flocking strategies via imitation learning. The primary motivation is to improve the swarm robustness and achieve better consistency while respecting the communication constraints. This paper first proposes a quantitative metric of swarm robustness based on entropy evaluation. [...] Read more.
This paper focuses on generating distributed flocking strategies via imitation learning. The primary motivation is to improve the swarm robustness and achieve better consistency while respecting the communication constraints. This paper first proposes a quantitative metric of swarm robustness based on entropy evaluation. Then, the graph importance consistency is also proposed, which is one of the critical goals of the flocking task. Moreover, the importance-correlated directed graph convolutional networks (IDGCNs) are constructed for multidimensional feature extraction and structure-related aggregation of graph data. Next, by employing IDGCNs-based imitation learning, a distributed and scalable flocking strategy is obtained, and its performance is very close to the centralized strategy template while considering communication constraints. To speed up and simplify the training process, we train the flocking strategy with a small number of agents and set restrictions on communication. Finally, various simulation experiments are executed to verify the advantages of the obtained strategy in terms of realizing the swarm consistency and improving the swarm robustness. The results also show that the performance is well maintained while the scale of agents expands (tested with 20, 30, 40 robots). Full article
(This article belongs to the Special Issue Artificial Intelligence within Robot Swarms)
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28 pages, 4918 KiB  
Article
An Adaptive Epidemiology-Based Approach to Swarm Foraging with Dynamic Deadlines
by Hebah ElGibreen
Appl. Sci. 2021, 11(10), 4627; https://0-doi-org.brum.beds.ac.uk/10.3390/app11104627 - 19 May 2021
Cited by 1 | Viewed by 1590
Abstract
Swarm robotics is an emerging field that can offer efficient solutions to real-world problems with minimal cost. Despite recent developments in the field, however, it is still not sufficiently mature, and challenges clearly remain. The dynamic deadline problem is neglected in the literature, [...] Read more.
Swarm robotics is an emerging field that can offer efficient solutions to real-world problems with minimal cost. Despite recent developments in the field, however, it is still not sufficiently mature, and challenges clearly remain. The dynamic deadline problem is neglected in the literature, and thus, time-sensitive foraging tasks are still an open research problem. This paper proposes a novel approach—ED_Foraging—that allows simple robots with limited sensing and communication abilities to perform complex foraging tasks that are dynamic and time constrained. A new mathematical model is developed in this paper to utilize epidemiological modeling and predict the dynamics of resource deadlines. Moreover, an improved dynamic task allocation (DTA) method is proposed to assign robots to the most critical region, where a deadline is represented by a state and time. The main goal is to reduce the number of expired resources and collect them as quickly as possible by giving priority to those that are more likely to expire if not collected. The deadlines are unknown and change dynamically. Thus, the robots continuously collect local information throughout their journeys and allocate themselves dynamically to the predicted hotspots. In the experiments, the proposed approach is adapted to four DTA methods and tested with different setups using simulated foot-bot robots. The flexibility, scalability, and robustness of this approach are measured in terms of the foraging and expiration rates. The empirical results support the hypothesis that epidemiological modeling can be utilized to handle foraging tasks that are constrained by dynamic deadlines. It is also confirmed that the proposed DTA method improves the results, which were found to be flexible, scalable, and robust to changes in the number of robots and the map size. Full article
(This article belongs to the Special Issue Artificial Intelligence within Robot Swarms)
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