Multi-robot Systems: State of the Art and Future Progress

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "AI in Robotics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 8596

Special Issue Editor


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Guest Editor
Department of Industrial Engineering and Management, Ariel University, Ariel 4076414, Israel
Interests: cybernetics and robotics; probabilistic algorithms; uncertainty analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

On behalf of my colleagues and myself, I cordially invite you to share the results of your research in the Special Issue “Multi-robot Systems: State of the Art and Future Progress” of the MDPI’s Robotics Journal.

The studies of multi-robot systems are a part of general research of multi-agent systems with specific stress on sensing environmental states and acting in static and dynamic environments. These studies include a wide range of themes: robotic production lines, communication and information fusion, decision making, division of labor, navigation of mobile robots, and swarm dynamics.

Starting from the origins of cybernetics, the progress in each field of muti-robot systems resulted in theoretical and practical achievements up to automatic manufacturing and team activities of mobile robots. In addition, the results regarding the collective behavior of robotic systems led to a better understanding of the behavior of natural collectives such as animals herds and flocks.

Together with the undoubted success in the field of multi-robot systems, each new result gives rise to additional questions, from practical optimization of the robot activity in the group to general questions about the limits of individual rationality in the collective behavior.

The Special Issue aims to present recent results in the field of multi-robot systems and to discuss the broader themes concerning the collective activity of automatic systems acting in a dynamic environment.

Dr. Eugene Kagan
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. Robotics is an international peer-reviewed open access monthly 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 1800 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 system
  • information fusion
  • collective decision making
  • cooperative control
  • swarm dynamics

Published Papers (4 papers)

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28 pages, 8920 KiB  
Article
Probability-Based Strategy for a Football Multi-Agent Autonomous Robot System
by António Fernando Alcântara Ribeiro, Ana Carolina Coelho Lopes, Tiago Alcântara Ribeiro, Nino Sancho Sampaio Martins Pereira, Gil Teixeira Lopes and António Fernando Macedo Ribeiro
Robotics 2024, 13(1), 5; https://doi.org/10.3390/robotics13010005 - 23 Dec 2023
Viewed by 2985
Abstract
The strategies of multi-autonomous cooperative robots in a football game can be solved in multiple ways. Still, the most common is the “Skills, Tactics and Plays (STP)” architecture, developed so that robots could easily cooperate based on a group of predefined plays, called [...] Read more.
The strategies of multi-autonomous cooperative robots in a football game can be solved in multiple ways. Still, the most common is the “Skills, Tactics and Plays (STP)” architecture, developed so that robots could easily cooperate based on a group of predefined plays, called the playbook. The development of the new strategy algorithm presented in this paper, used by the RoboCup Middle Size League LAR@MSL team, had a completely different approach from most other teams for multiple reasons. Contrary to the typical STP architecture, this strategy, called the Probability-Based Strategy (PBS), uses only skills and decides the outcome of the tactics and plays in real-time based on the probability of arbitrary values given to the possible actions in each situation. The action probability values also affect the robot’s positioning in a way that optimizes the overall probability of scoring a goal. It uses a centralized decision-making strategy rather than the robot’s self-control. The robot is still fully autonomous in the skills assigned to it and uses a communication system with the main computer to synchronize all robots. Also, calibration or any strategy improvements are independent of the robots themselves. The robots’ performance affects the results but does not interfere with the strategy outcome. Moreover, the strategy outcome depends primarily on the opponent team and the probability calibration for each action. The strategy presented has been fully implemented on the team and tested in multiple scenarios, such as simulators, a controlled environment, against humans in a simulator, and in the RoboCup competition. Full article
(This article belongs to the Special Issue Multi-robot Systems: State of the Art and Future Progress)
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15 pages, 5905 KiB  
Article
Minimum Energy Utilization Strategy for Fleet of Autonomous Robots in Urban Waste Management
by Valeria Bladinieres Justo, Abhishek Gupta, Tobias Fritz Umland and Dietmar Göhlich
Robotics 2023, 12(6), 159; https://doi.org/10.3390/robotics12060159 - 23 Nov 2023
Viewed by 1427
Abstract
Many service robots have to operate in a variety of different Service Event Areas (SEAs). In the case of the waste collection robot MARBLE (Mobile Autonomous Robot for Litter Emptying) every SEA has characteristics like varying area and number of litter bins, with [...] Read more.
Many service robots have to operate in a variety of different Service Event Areas (SEAs). In the case of the waste collection robot MARBLE (Mobile Autonomous Robot for Litter Emptying) every SEA has characteristics like varying area and number of litter bins, with different distances between litter bins and uncertain filling levels of litter bins. Global positions of litter bins and garbage drop-off positions from MARBLEs after reaching their maximum capacity are defined as task-performing waypoints. We provide boundary delimitation for characteristics that describe the SEA. The boundaries interpolate synergy between individual SEAs and the developed algorithms. This helps in determining which algorithm best suits an SEA, dependent on the characteristics. The developed route-planning methodologies are based on vehicle routing with simulated annealing (VRPSA) and knapsack problems (KSPs). VRPSA uses specific weighting based on route permutation operators, initial temperature, and the nearest neighbor approach. The KSP optimizes a route’s given capacity, in this case using smart litter bins (SLBs) information. The game-theory KSP algorithm with SLBs information and the KSP algorithm without SLBs information performs better on SEAs lower than 0.5 km2, and with fewer than 50 litter bins. When the standard deviation of the fill rate of litter bins is ≈10%, the KSP without SLB is preferred, and if the standard deviation is between 25 and 40%, then the game-theory KSP is selected. Finally, the vehicle routing problem outperforms in SEAs with an area of 0.55 km2, 50–450 litter bins, and a fill rate of 10–40%. Full article
(This article belongs to the Special Issue Multi-robot Systems: State of the Art and Future Progress)
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17 pages, 5016 KiB  
Article
Non-Commutative Logic for Collective Decision-Making with Perception Bias
by Evgeny Kagan, Alexander Novoselsky, Daria Ramon and Alexander Rybalov
Robotics 2023, 12(3), 76; https://doi.org/10.3390/robotics12030076 - 22 May 2023
Viewed by 1169
Abstract
In this paper, we suggest an implementation of non-commutative logic and apply its operators for decision-making in a group of autonomous agents. The suggested operators extend the uninorm and absorbing norm aggregators and use an additional asymmetry parameter that defines the “level of [...] Read more.
In this paper, we suggest an implementation of non-commutative logic and apply its operators for decision-making in a group of autonomous agents. The suggested operators extend the uninorm and absorbing norm aggregators and use an additional asymmetry parameter that defines the “level of non-commutativity”. The value of this parameter is specified using the perception bias of humans measured in the experiments. The suggested operators and decision-making method are illustrated by the simulated behavior of mobile robots in the group, which verified the possibility of processing systematic sensing errors, as well as of distinguishing and mimicking the biased decisions. Full article
(This article belongs to the Special Issue Multi-robot Systems: State of the Art and Future Progress)
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10 pages, 43261 KiB  
Project Report
Performance Improvement of Multi-Robot Data Transmission in Aggregated Robot Processing Architecture with Caches and QoS Balancing Optimization
by Abdul Jalil, Jun Kobayashi and Takeshi Saitoh
Robotics 2023, 12(3), 87; https://doi.org/10.3390/robotics12030087 - 15 Jun 2023
Cited by 2 | Viewed by 2131
Abstract
Robot Operating System 2 (ROS 2) is a robotic software that uses a set of Quality of Service (QoS) policies to manage the quality of robot data transmissions in a network, such as the RELIABLE and KEEP_LAST options. In ROS 2 node communication, [...] Read more.
Robot Operating System 2 (ROS 2) is a robotic software that uses a set of Quality of Service (QoS) policies to manage the quality of robot data transmissions in a network, such as the RELIABLE and KEEP_LAST options. In ROS 2 node communication, the RELIABLE connection guarantees that all message data can be properly sent from the publisher to the subscriber. However, strict reliability is not guaranteed if the RELIABLE connection uses the KEEP_LAST option to transmit the robot data in the publish–subscribe communication. This study aims to analyze the efficiency of local cache, cache control, and QoS balancing optimization to improve ROS 2 node communication when using the RELIABLE and KEEP_LAST options to transmit multi-robot data in Aggregated Robot Processing (ARP) architecture. Our idea in local cache and cache control is to streamline the sensor data output before processing it when the sensor device produces the data with the same value in a row. Furthermore, QoS balancing optimization aims to balance the DEPTH and DEADLINE QoS configuration to determine the rates and buffer size in ROS 2 node communication. This study shows that combining local cache and QoS balancing optimization improves multi-robot data transmission and cooperation in ARP architecture. Full article
(This article belongs to the Special Issue Multi-robot Systems: State of the Art and Future Progress)
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