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Machines
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Advances in Mobile Robot Navigation in an Unstructured and Dynamically...
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Advances in Mobile Robot Navigation in an Unstructured and Dynamically Cluttered Environment

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 6801

Special Issue Editors

Prof. Dr. Yousef Ibrahim

E-Mail Website
Guest Editor
School of Engineering, Information Technology and Physical Sciences, Federation University Australia, Churchill, VIC 3842, Australia
Interests: industrial robotics; mobile robots; navigation algorithms
Dr. Gayan Kahandawa

E-Mail Website
Guest Editor
School of Engineering, Information Technology and Physical Sciences, Federation University Australia, Churchill, VIC 3842, Australia
Interests: mechatronic systems; sensors and signal processing; artificial intelligence; mobile robot navigation
Prof. Dr. Mihoko Niitsuma

E-Mail Website
Guest Editor
Department of Precision Mechanics, Chuo University, Tokyo 112-8551, Japan
Interests: human-robot interaction/collaboration; social robot; human interface; ambient intelligence; mobile robot navigation

Special Issue Information

Dear Colleagues,

In the last few decades, researchers in the robotics field have conducted many investigations on using robots within human–robot coexistent environments to perform complex works such as surveillance, rescue, transportation, and working in underground mines. Due to this fast growth in robots’ utilization, the challenge of mobile robots’ navigation in unstructured dynamic environments has become a field of interest to many researchers around the globe.

This Special Issue aims to discuss state-of-the-art algorithms and methodologies addressing the challenges facing researchers in the various fields and applications of modern mobile robots’ navigation. Those field can be related but not limited to:

  • Obstacle tracking and path prediction;
  • Optimal path planning;
  • Obstacle tracking and path prediction;
  • Collision-free and safe motions;
  • Manoeuvrability in a high-density dynamic environment.

Prof. Dr. Yousef Ibrahim
Dr. Gayan Kahandawa
Prof. Dr. Mihoko Niitsuma
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. Machines 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 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

  • mobile robots
  • navigation
  • obstacle tracking
  • dynamic environments

Published Papers (4 papers)

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Research

22 pages, 2100 KiB  
Article
Machine Learning-Based Agoraphilic Navigation Algorithm for Use in Dynamic Environments with a Moving Goal
by Hasitha Hewawasam, Gayan Kahandawa and Yousef Ibrahim
Machines 2023, 11(5), 513; https://doi.org/10.3390/machines11050513 - 28 Apr 2023
Cited by 1 | Viewed by 1432
Abstract
This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets [...] Read more.
This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal. Full article
(This article belongs to the Special Issue Advances in Mobile Robot Navigation in an Unstructured and Dynamically Cluttered Environment)
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19 pages, 6355 KiB  
Article
Developing a Machine Learning Algorithm for Service Robots in Industrial Applications
by Nizamettin Kulaç and Mustafa Engin
Machines 2023, 11(4), 421; https://doi.org/10.3390/machines11040421 - 25 Mar 2023
Cited by 3 | Viewed by 1782
Abstract
Robots, which have mostly been effective in areas such as industrial, agricultural, and production facilities, have started to take a place in the service sector, as their technologies have become lower in cost and more easily accessible. This situation has attracted the attention [...] Read more.
Robots, which have mostly been effective in areas such as industrial, agricultural, and production facilities, have started to take a place in the service sector, as their technologies have become lower in cost and more easily accessible. This situation has attracted the attention of companies and researchers and has accelerated studies on this subject. In this study, an algorithm was developed for the autonomous mobile robot to serve in industrial areas. In line with this study, it was ensured that the autonomous mobile robot mapped the working environment, determined the working station in this environment, and then carried out transport operations between these working stations in accordance with a given work order. After the mobile robot fulfilled the work order, it went into a waiting state until a new work order was received. For the mobile robot to save energy, it was ensured that it waited close to the point where the work order came in the most, by means of machine learning in the waiting position. The developed algorithms were designed using the NI LabVIEW environment and then simulated in the RobotinoSIM environment and physically tested using the Robotino autonomous mobile robot platform. The experimental results showed that mapping and location reporting using an RGB camera, odometry, and a QR code eliminated permanent location errors, and the robot completed 50 work orders with 100% accuracy. Full article
(This article belongs to the Special Issue Advances in Mobile Robot Navigation in an Unstructured and Dynamically Cluttered Environment)
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24 pages, 988 KiB  
Article
A Novel Optimistic Local Path Planner: Agoraphilic Navigation Algorithm in Dynamic Environment
by Hasitha Hewawasam, Yousef Ibrahim and Gayan Kahandawa
Machines 2022, 10(11), 1085; https://doi.org/10.3390/machines10111085 - 16 Nov 2022
Cited by 1 | Viewed by 1382
Abstract
This paper presents a novel local path planning algorithm developed based on the new free space attraction (Agoraphilic) concept. The proposed algorithm is capable of navigating robots in unknown static, as well as dynamically cluttered environments. Unlike the other navigation algorithms, the proposed [...] Read more.
This paper presents a novel local path planning algorithm developed based on the new free space attraction (Agoraphilic) concept. The proposed algorithm is capable of navigating robots in unknown static, as well as dynamically cluttered environments. Unlike the other navigation algorithms, the proposed algorithm takes the optimistic approach of the navigation problem. It does not look for problems to avoid, but rather for solutions to follow. This human-like decision-making behaviour distinguishes the new algorithm from all the other navigation algorithms. Furthermore, the new algorithm utilises newly developed tracking and prediction algorithms, to safely navigate mobile robots. This is further supported by a fuzzy logic controller designed to efficiently account for the inherent high uncertainties in the robot’s operational environment at a reduced computational cost. This paper also includes physical experimental results combined with bench-marking against other recent methods. The reported results verify the algorithm’s successful advantages in navigating robots in both static and dynamic environments. Full article
(This article belongs to the Special Issue Advances in Mobile Robot Navigation in an Unstructured and Dynamically Cluttered Environment)
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26 pages, 31851 KiB  
Article
Robust Tracking and Clean Background Dense Reconstruction for RGB-D SLAM in a Dynamic Indoor Environment
by Fengbo Zhu, Shunyi Zheng, Xia Huang and Xiqi Wang
Machines 2022, 10(10), 892; https://doi.org/10.3390/machines10100892 - 03 Oct 2022
Viewed by 1329
Abstract
This article proposes a two-stage simultaneous localization and mapping (SLAM) method based on using the red green blue-depth (RGB-D) camera in dynamic environments, which can not only improve tracking robustness and trajectory accuracy but also reconstruct a clean and dense static background model [...] Read more.
This article proposes a two-stage simultaneous localization and mapping (SLAM) method based on using the red green blue-depth (RGB-D) camera in dynamic environments, which can not only improve tracking robustness and trajectory accuracy but also reconstruct a clean and dense static background model in dynamic environments. In the first stage, to accurately exclude the interference of features in the dynamic region from the tracking, the dynamic object mask is extracted by Mask-RCNN and optimized by using the connected component analysis method and a reference frame-based method. Then, the feature points, lines, and planes in the nondynamic object area are used to construct an optimization model to improve the tracking accuracy and robustness. After the tracking is completed, the mask is further optimized by the multiview projection method. In the second stage, to accurately obtain the pending area, which contains the dynamic object area and the newly added area in each frame, a method is proposed, which is based on a ray-casting algorithm and fully uses the result of the first stage. To extract the static region from the pending region, this paper designs divisible and indivisible regions process methods and the bounding box tracking method. Then, the extracted static regions are merged into the map using the truncated signed distance function method. Finally, the clean static background model is obtained. Our methods have been verified on public datasets and real scenes. The results show that the presented methods achieve comparable or better trajectory accuracy and the best robustness, and can construct a clean static background model in a dynamic scene. Full article
(This article belongs to the Special Issue Advances in Mobile Robot Navigation in an Unstructured and Dynamically Cluttered Environment)
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