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Electronics
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Networked Control of Multi-Robot Systems
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Networked Control of Multi-Robot Systems

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 6953

Special Issue Editors

Dr. Liangming Chen

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering (MAE), Nanyang Technological University, Singapore
Interests: multi-robot systems; networked control; formation control; distributed localization; aerial robotics
Dr. Farshad Arvin

E-Mail Website
Guest Editor
Department of Computer Science, Durham University, Durham, UK
Interests: swarm robotics; bio-inspired swarm; collective behaviour; chronorobotics; micro-robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qingkai Yang

E-Mail Website
Guest Editor
School of Automation, Beijing Institute of Technology, Beijing, China
Interests: aerial robots; motion control of multiple robots (UAV, UGV); tensegrity robots

Special Issue Information

Dear Colleagues,

With the recent technological development of broadband cellular networks, networked control of multi-robot systems has been enabled with higher accuracy, speed, and resilience in the control and estimation of large-scale robotic systems, such as multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs), etc. With this advantage for networked multi-robot systems, many theoretical and practical problems are open that are worthy of being addressed, such as multi-robot cooperative control, distributed estimation, and resilient estimation and control with the consideration of constraints, such as communication constraints, motion safety constraints, and sensing constraints. Therefore, how to develop key techniques in the control and estimation of multiple robots over wireless networks becomes significant.

This Special Issue focuses on key theoretical and practical contributions to the control and estimation problems for networked multi-robot systems. It aims to bring studies from related fields (estimation, control, navigation, mapping, localization, and communication) together. We welcome the submission of high-quality original research papers on topics including, but not limited to:

  • Cooperative control of multi-robot systems (e.g., multi-UAVs, multi-UGVs, multi-AUVs, or general multi-agent systems);
  • Networked control under communication constraints (e.g., event-triggered  communication, packet loss, communication delays);
  • Multi-robot formation control using local relative measurements, such as relative positions, bearings, and distances;
  • Navigation and localization of multi-robot systems using local relative measurements, such as relative positions, bearings, and distances;
  • Data-driven control of multi-agent systems;
  • Cooperative path planning and following of multiple mobile robots, such as aerial, ground, and underwater robots;
  • Resilient estimation and control of multi-robot systems (e.g., wireless attack and security);
  • Swarm robotic behaviors and their applications;
  • Distributed optimization of multi-robot systems.

Dr. Liangming Chen
Dr. Farshad Arvin
Dr. Qingkai Yang
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. Electronics 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

  • multi-robot systems
  • networked control
  • swarm robotics
  • cooperative control and distributed estimation

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Published Papers (4 papers)

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Research

23 pages, 4008 KiB  
Article
Minimally Persistent Graph Generation and Formation Control for Multi-Robot Systems under Sensing Constraints
by Xinyue Zhao, Qingkai Yang, Qi Liu, Yuhan Yin, Yue Wei and Hao Fang
Electronics 2023, 12(2), 317; https://doi.org/10.3390/electronics12020317 - 7 Jan 2023
Cited by 1 | Viewed by 1658
Abstract
This paper presents a minimally persistent graph generation and formation control strategy for multi-robot systems with sensing constraints. Specifically, each robot has a limited field of view (FOV) and range sensing capability. To tackle this problem, one needs to construct an appropriate interaction [...] Read more.
This paper presents a minimally persistent graph generation and formation control strategy for multi-robot systems with sensing constraints. Specifically, each robot has a limited field of view (FOV) and range sensing capability. To tackle this problem, one needs to construct an appropriate interaction topology, namely assign neighbors to each robot such that all their sensing constraints are satisfied. In addition, as a stringent yet reasonable guarantee for the visual constraints, it is also required that the prescribed neighbors always stay within its visual field during the formation evolution. To this end, given a set of feasible initial positions, we first present a depth-first-search (DFS)-based algorithm to generate a minimally persistent graph, which encodes the sensing constraints via its directed edges. Then, based on the resultant graph, by invoking the gradient-based control technique and control barrier function (CBF), we propose a class of distributed formation control laws, rendering not only the convergence to the desired formation but also the satisfaction of sensing constraints. Simulation and experimental results are presented to verify the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Networked Control of Multi-Robot Systems)
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12 pages, 353 KiB  
Article
Co-Design of Output-Based Event-Triggered Protocol and Sliding Mode Control for 2D Nonlinear Fornasini-Marchesini Network under Packet Dropouts
by Jiajia Jia and Guangchen Zhang
Electronics 2022, 11(19), 2986; https://doi.org/10.3390/electronics11192986 - 21 Sep 2022
Viewed by 1022
Abstract
This paper focuses on the stability and sliding mode issues for the two-dimensional (2D) Fornasini-Marchesini (FMII) networked control system under packet dropouts. Firstly, the output-based 2D event-triggered strategy was constructed to alleviate information transmission pressure caused by limited network resources. Secondly, by considering [...] Read more.
This paper focuses on the stability and sliding mode issues for the two-dimensional (2D) Fornasini-Marchesini (FMII) networked control system under packet dropouts. Firstly, the output-based 2D event-triggered strategy was constructed to alleviate information transmission pressure caused by limited network resources. Secondly, by considering the impact of packet dropouts, we propose an output-based 2D sliding mode controller and formulate the output-based 2D error-estimation scheme accordingly. Moreover, to get rid of the nonlinear coupling of the conditions (to guarantee the mean-square stability), we established an adaptive intelligence algorithm. Finally, we provide a numerical example to verify the effectiveness and practicability of the proposed algorithm and controller design. Full article
(This article belongs to the Special Issue Networked Control of Multi-Robot Systems)
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18 pages, 4952 KiB  
Article
Economic Dispatch of Microgrid Based on Load Prediction of Back Propagation Neural Network–Local Mean Decomposition–Long Short-Term Memory
by Fengxia Xu, Xinyu Zhang, Xingming Ma, Xinyu Mao, Zhongda Lu, Lijing Wang and Ling Zhu
Electronics 2022, 11(14), 2202; https://doi.org/10.3390/electronics11142202 - 14 Jul 2022
Cited by 4 | Viewed by 1680
Abstract
To plan the work of power generation equipment, it is necessary to ensure that the power supply is sufficient and to achieve the minimum cost to ensure the safety and economy of the microgrid. Based on back propagation neural network–local mean decomposition–long short-term [...] Read more.
To plan the work of power generation equipment, it is necessary to ensure that the power supply is sufficient and to achieve the minimum cost to ensure the safety and economy of the microgrid. Based on back propagation neural network–local mean decomposition–long short-term memory (BPNN–LMD–LSTM) load prediction, the design is based on a fixed-time consistency algorithm with random delay to predict the economic dispatch of microgrids. Firstly, the initial power load prediction sequence is obtained by continuous training of the back propagation neural network (BPNN); the residual sequence with other influencing factors is decomposed by local mean decomposition (LMD); and the long short-term memory neural network (LSTM) is used to predict the output prediction residual sequence, and the final short-term power load prediction is obtained. Based on predicting load, the fixed-time consistency algorithm with random delay is used to add supply and demand balance constraints to optimize the power distribution of the power generation units of the distributed microgrid and reduce the power generation cost of the microgrid. The results show that the prediction model has better prediction accuracy, and the scheduling algorithm based on the prediction model has a faster convergence rate to reach the lowest power generation cost. Full article
(This article belongs to the Special Issue Networked Control of Multi-Robot Systems)
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17 pages, 810 KiB  
Article
Probabilistic Plan Recognition for Multi-Agent Systems under Temporal Logic Tasks
by Wentao Yu, Shanghao Li, Daiying Tian and Jinqiang Cui
Electronics 2022, 11(9), 1352; https://doi.org/10.3390/electronics11091352 - 24 Apr 2022
Cited by 1 | Viewed by 1533
Abstract
This paper studies the plan recognition problem of multi-agent systems with temporal logic tasks. The high-level temporal tasks are represented as linear temporal logic (LTL). We present a probabilistic plan recognition algorithm to predict the future goals and identify the temporal logic tasks [...] Read more.
This paper studies the plan recognition problem of multi-agent systems with temporal logic tasks. The high-level temporal tasks are represented as linear temporal logic (LTL). We present a probabilistic plan recognition algorithm to predict the future goals and identify the temporal logic tasks of the agent based on the observations of their states and actions. We subsequently build a plan library composed of Nondeterministic Bu¨chi Automation to model the temporal logic tasks. We also propose a Boolean matrix generation algorithm to map the plan library to multi-agent trajectories and a task recognition algorithm to parse the Boolean matrix. Then, the probability calculation formula is proposed to calculate the posterior goal probability distribution, and the cold start situation of the plan recognition is solved using the Bayes formula. Finally, we validate the proposed algorithm via extensive comparative simulations. Full article
(This article belongs to the Special Issue Networked Control of Multi-Robot Systems)
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