Recent Trends in Multi-Robot Systems: From Theoretical Contributions to Practical Applications

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

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 14113

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School of Production Engineering and Management, Technical University of Crete, University Campus, Kounoupidiana, 73100 Chania, Crete, Greece
Interests: multirobot teams; design of novel robotic systems; autonomous operation and navigation of unmanned vehicles
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Special Issue Information

Dear Colleagues,

In the foreseeable future, the use of a single robot will become obsolete, as there is an ever-increasing interest in multi-robot systems. This is due to the extended capabilities that teams have to offer, compared to the use of a single robot for the same task. Multi-robot systems can be used in a variety of missions including but not limited to surveillance in hostile environments (i.e., areas contaminated with biological, chemical, or even nuclear wastes), environmental monitoring (i.e., air quality monitoring, forest monitoring), and law enforcement missions (i.e., border patrol), last mile delivery, warehouse management, etc.
For the aforementioned tasks, different types of robots can be used, including aerial, ground surface, and underwater vehicles. All these robots can be controlled and coordinated in a centralized or decentralized manner and can utilize different types of sensors, combining the information gathered in order to accomplish common tasks.

This Special Issue will focus on all aspects related to multi-robot teams including but not limited to the following:

  • Theoretical foundations in multi-robot systems
  • Multi-robot systems coordination and interaction
  • Distributed control and planning
  • Mapping, localization, and navigation in multi-robot systems
  • Swarm robotics
  • Novel sensors and actuators for multi-robot systems
  • Real-world applications of multi-robot systems
  • Machine learning in multi-robot systems
  • IoT and multi-robot systems
  • Multi-robot systems in education and special education

Dr. Lefteris Doitsidis
Dr. Savvas A. Chatzichristofis
Guest Editors

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Published Papers (3 papers)

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Research

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20 pages, 3490 KiB  
Article
Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm
by Fang Ye, Jie Chen, Yuan Tian and Tao Jiang
Electronics 2020, 9(4), 687; https://doi.org/10.3390/electronics9040687 - 23 Apr 2020
Cited by 55 | Viewed by 4671
Abstract
The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, [...] Read more.
The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven. Full article
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20 pages, 2297 KiB  
Article
Virtual Pheromone Based Network Flow Control For Modular Robotic Systems
by Van Tung Le and Trung Dung Ngo
Electronics 2020, 9(3), 481; https://doi.org/10.3390/electronics9030481 - 14 Mar 2020
Cited by 1 | Viewed by 3350
Abstract
Guaranteeing data transmission between modules is the key for application development of modular robotic systems. In a multi-channel modular robotic system, intersection modules play an essential role of communication channel selection in controlling data flow toward desired destinations. The gradient-based routing algorithm is [...] Read more.
Guaranteeing data transmission between modules is the key for application development of modular robotic systems. In a multi-channel modular robotic system, intersection modules play an essential role of communication channel selection in controlling data flow toward desired destinations. The gradient-based routing algorithm is an ideal solution to create an one-way communication path from any robotic module to a designated destination. To create bi-directional communication for a communication path of robotic configuration, virtual pheromone-based routing algorithm is a promising mechanism for intersection modules due to its simplicity and distributivity. In this paper, we address a virtual pheromone based network flow control based on the integration of gradient and virtual pheromone-based routing algorithms. We validated this method through an education and entertainment application using our newly developed modular robotic system. Full article
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Review

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30 pages, 4812 KiB  
Review
On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks
by Ala Khalifeh, Kishore Rajendiran, Khalid A. Darabkh, Ahmad M. Khasawneh, Omar AlMomani and Zinon Zinonos
Electronics 2019, 8(12), 1513; https://doi.org/10.3390/electronics8121513 - 10 Dec 2019
Cited by 25 | Viewed by 5231
Abstract
Wireless sensor networks have recently been widely used in several applications and scenarios, especially because they have the ability and flexibility for establishing a scalable and reliable wireless network. Cooperative multi-robotic systems (CMRS) are one example of these applications where establishing a wireless [...] Read more.
Wireless sensor networks have recently been widely used in several applications and scenarios, especially because they have the ability and flexibility for establishing a scalable and reliable wireless network. Cooperative multi-robotic systems (CMRS) are one example of these applications where establishing a wireless network between robots is essential and paramount to their operation. Further, these robots can utilize their mobility to provide sensing functionality for areas that are not covered by the static sensor. This can be achieved by equipping the robots with specific sensors to sense the area of interest (AoI) and report the sensed data to a remote monitoring center for further processing and decision-making. However, the nodes that form the sensor network have limited energy, and, as such, efficient algorithms in clusters’ formation, packets’ routing, and energy and mobility management are paramount. In this paper, a literature survey is presented containing the most related works that have been proposed to solve these challenges utilizing fuzzy logic. Most of the literature work attempted to utilize a de-centralized approach, where certain input parameters such as the residual energy, communication link quality, network congestion status, the nodes’ distance to the sink node and its location with respect to the other nodes, and the data and their sampling rate are all used as inputs to the fuzzy logic controller. These input parameters are used to determine several performance vital factors such as the cluster formation and its cluster head, best route to the sink node, optimal power management policies in terms of sleep/awake times needed to maximize the network lifetime, nodes’ mobility management policies to maintain network connectivity, and best route in terms of packet loss and delay. Full article
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