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Efficient Planning and Mapping for Multi-Robot Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 13559

Special Issue Editors

Department of IT Convergence Engineering, School of Electronic Engineering, Kumoh National Institute of Technology, Gyeongbuk 39177, Republic of Korea
Interests: SLAM; autonomous navigation; multi-robot systems; deep learning for anomaly detection; FPGA-based algorithm acceleration
Special Issues, Collections and Topics in MDPI journals
Department of Mechanical and Aerospace Engineering, Princeton University, 41 Olden St., Princeton, NJ 08540, USA
Interests: robotics; multirobot control; control theory; game theory
School of Electronic Engineering, Kumoh National Institute of Technology, Gyeongbuk 39177, Republic of Korea
Interests: multiagent systems; autonomous navigation; SLAM
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multirobot systems (MRS) have received attention in recent years because they have many advantages over single-robot systems, such as time-efficiency and cost reduction. MRS can not only can perform a given single task or multi-task in a given space faster than a single robot system, but they also can efficiently cope with failure situations. In particular, they are more efficient in operating multiple inexpensive robots than a single expensive robot in situations where robot malfunctions should be considered due to unknown factors in disaster environments. Even though multiple robots can complete not only a single task faster but also multiple tasks simultaneously, there are many challenging problems to be resolved to realize multirobot systems.

This Special Issue aims to provide broad coverage of recent advances in efficient planning and mapping for multirobot systems. Both theoretical and practical works, as well as review/survey papers in the area, are welcome. The topics of interest of this Special Issue include but are not limited to:

  • Frameworks and implementation for multirobot systems;
  • Algorithms and implementation for multirobot SLAM;
  • Algorithms and implementation for multirobot path planning and control;
  • Algorithms and implementation for multirobot exploration;
  • Distributed coordination with heterogeneous multirobot systems;
  • Practical sensor fusion systems for inter-robot recognition;
  • Efficient inter-robot collision avoidance;
  • Efficient inter-robot communication.

Prof. Dr. Heoncheol Lee
Dr. Shinkyu Park
Prof. Dr. Seunghwan Lee
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • multirobot systems
  • multirobot slam
  • multirobot path planning
  • multirobot control
  • multirobot exploration
  • distributed coordination
  • inter-robot recognition
  • inter-robot collision avoidance
  • inter-robot communication

Published Papers (4 papers)

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Research

18 pages, 1438 KiB  
Article
Plugin Framework-Based Neuro-Symbolic Grounded Task Planning for Multi-Agent System
by Jiyoun Moon
Sensors 2021, 21(23), 7896; https://doi.org/10.3390/s21237896 - 26 Nov 2021
Viewed by 1926
Abstract
As the roles of robots continue to expand in general, there is an increasing demand for research on automated task planning for a multi-agent system that can independently execute tasks in a wide and dynamic environment. This study introduces a plugin framework in [...] Read more.
As the roles of robots continue to expand in general, there is an increasing demand for research on automated task planning for a multi-agent system that can independently execute tasks in a wide and dynamic environment. This study introduces a plugin framework in which multiple robots can be involved in task planning in a broad range of areas by combining symbolic and connectionist approaches. The symbolic approach for understanding and learning human knowledge is useful for task planning in a wide and static environment. The network-based connectionist approach has the advantage of being able to respond to an ever-changing dynamic environment. A planning domain definition language-based planning algorithm, which is a symbolic approach, and the cooperative–competitive reinforcement learning algorithm, which is a connectionist approach, were utilized in this study. The proposed architecture is verified through a simulation. It is also verified through an experiment using 10 unmanned surface vehicles that the given tasks were successfully executed in a wide and dynamic environment. Full article
(This article belongs to the Special Issue Efficient Planning and Mapping for Multi-Robot Systems)
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18 pages, 7398 KiB  
Article
Multi UAV Coverage Path Planning in Urban Environments
by Javier Muñoz, Blanca López, Fernando Quevedo, Concepción A. Monje, Santiago Garrido and Luis E. Moreno
Sensors 2021, 21(21), 7365; https://doi.org/10.3390/s21217365 - 05 Nov 2021
Cited by 26 | Viewed by 4406
Abstract
Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as [...] Read more.
Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints, calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments. Full article
(This article belongs to the Special Issue Efficient Planning and Mapping for Multi-Robot Systems)
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17 pages, 845 KiB  
Article
Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning
by Gyuho Eoh and Tae-Hyoung Park
Sensors 2021, 21(14), 4780; https://doi.org/10.3390/s21144780 - 13 Jul 2021
Cited by 13 | Viewed by 2608
Abstract
This paper presents a cooperative object transportation technique using deep reinforcement learning (DRL) based on curricula. Previous studies on object transportation highly depended on complex and intractable controls, such as grasping, pushing, and caging. Recently, DRL-based object transportation techniques have been proposed, which [...] Read more.
This paper presents a cooperative object transportation technique using deep reinforcement learning (DRL) based on curricula. Previous studies on object transportation highly depended on complex and intractable controls, such as grasping, pushing, and caging. Recently, DRL-based object transportation techniques have been proposed, which showed improved performance without precise controller design. However, DRL-based techniques not only take a long time to learn their policies but also sometimes fail to learn. It is difficult to learn the policy of DRL by random actions only. Therefore, we propose two curricula for the efficient learning of object transportation: region-growing and single- to multi-robot. During the learning process, the region-growing curriculum gradually extended to a region in which an object was initialized. This step-by-step learning raised the success probability of object transportation by restricting the working area. Multiple robots could easily learn a new policy by exploiting the pre-trained policy of a single robot. This single- to multi-robot curriculum can help robots to learn a transporting method with trial and error. Simulation results are presented to verify the proposed techniques. Full article
(This article belongs to the Special Issue Efficient Planning and Mapping for Multi-Robot Systems)
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25 pages, 11942 KiB  
Article
Dynamic Optimization and Heuristics Based Online Coverage Path Planning in 3D Environment for UAVs
by Aurelio G. Melo, Milena F. Pinto, Andre L. M. Marcato, Leonardo M. Honório and Fabrício O. Coelho
Sensors 2021, 21(4), 1108; https://doi.org/10.3390/s21041108 - 05 Feb 2021
Cited by 27 | Viewed by 3121
Abstract
Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about [...] Read more.
Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about the path have to be taken while the robot moves towards its goal. Among the navigation area, an important class of problems is Coverage Path Planning (CPP). The CPP technique is associated with determining a collision-free path that passes through all viewpoints in a specific area. This paper presents a method to perform CPP in 3D environment for Unmanned Aerial Vehicles (UAVs) applications, namely 3D dynamic for CPP applications (3DD-CPP). The proposed method can be deployed in an unknown environment through a combination of linear optimization and heuristics. A model to estimate cost matrices accounting for UAV power usage is proposed and evaluated for a few different flight speeds. As linear optimization methods can be computationally demanding to be used on-board a UAV, this work also proposes a distributed execution of the algorithm through fog-edge computing. Results showed that 3DD-CPP had a good performance in both local execution and fog-edge for different simulated scenarios. The proposed heuristic is capable of re-optimization, enabling execution in environments with local knowledge of the environments. Full article
(This article belongs to the Special Issue Efficient Planning and Mapping for Multi-Robot Systems)
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