Autonomous Navigation of Mobile Robots in Unstructured Environments

A special issue of Robotics (ISSN 2218-6581).

Deadline for manuscript submissions: 31 August 2024 | Viewed by 6043

Special Issue Editor


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Guest Editor
Department of Electrical and Computer Engineering, Royal Military College of Canada, Kingston, ON, Canada
Interests: autonomous navigation; autonomous exploration; learning-based robotics control; mobile manipulation; robotic search and rescue

Special Issue Information

Dear Colleagues,

The field of autonomous navigation for mobile robots in unstructured environments has gained significant attention in recent years. With advancements in technology, there is a growing need for robots that can navigate and operate in dynamic and unpredictable surroundings. This Special Issue aims to present the latest research and developments in this field, showcasing innovative approaches, algorithms, and applications that enable mobile robots to autonomously navigate through unstructured environments.

This Special Issue welcomes researchers and practitioners to contribute original research articles, reviews, case studies, and short communications on various aspects of the autonomous navigation of mobile robots in unstructured environments. The topics of interest include, but are not limited to:

  1. Sensing and perception for autonomous navigation;
  2. Mapping and localization techniques;
  3. Path planning and obstacle avoidance algorithms;
  4. Machine learning and artificial intelligence for autonomous navigation;
  5. Multi-robot systems and coordination in unstructured environments;
  6. Human-robot interaction in unstructured environments;
  7. Robustness and fault tolerance in autonomous navigation;
  8. Navigation in challenging terrains (e.g., forests, disaster zones, underwater);
  9. Applications of autonomous navigation in industries, agriculture, search and rescue, etc.

Dr. Yugang Liu
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.

Published Papers (3 papers)

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15 pages, 6675 KiB  
Article
Development of Local Path Planning Using Selective Model Predictive Control, Potential Fields, and Particle Swarm Optimization
by Mingeuk Kim, Minyoung Lee, Byeongjin Kim and Moohyun Cha
Robotics 2024, 13(3), 46; https://doi.org/10.3390/robotics13030046 - 08 Mar 2024
Viewed by 872
Abstract
This paper focuses on the real-time obstacle avoidance and safe navigation of autonomous ground vehicles (AGVs). It introduces the Selective MPC-PF-PSO algorithm, which includes model predictive control (MPC), Artificial Potential Fields (APFs), and particle swarm optimization (PSO). This approach involves defining multiple sets [...] Read more.
This paper focuses on the real-time obstacle avoidance and safe navigation of autonomous ground vehicles (AGVs). It introduces the Selective MPC-PF-PSO algorithm, which includes model predictive control (MPC), Artificial Potential Fields (APFs), and particle swarm optimization (PSO). This approach involves defining multiple sets of coefficients for adaptability to the surrounding environment. The simulation results demonstrate that the algorithm is appropriate for generating obstacle avoidance paths. The algorithm was implemented on the ROS platform using NVIDIA’s Jetson Xavier, and driving experiments were conducted with a steer-type AGV. Through measurements of computation time and real obstacle avoidance experiments, it was shown to be practical in the real world. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots in Unstructured Environments)
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26 pages, 12281 KiB  
Article
MonoGhost: Lightweight Monocular GhostNet 3D Object Properties Estimation for Autonomous Driving
by Ahmed El-Dawy, Amr El-Zawawi and Mohamed El-Habrouk
Robotics 2023, 12(6), 155; https://doi.org/10.3390/robotics12060155 - 17 Nov 2023
Viewed by 1698
Abstract
Effective environmental perception is critical for autonomous driving; thus, the perception system requires collecting 3D information of the surrounding objects, such as their dimensions, locations, and orientation in space. Recently, deep learning has been widely used in perception systems that convert image features [...] Read more.
Effective environmental perception is critical for autonomous driving; thus, the perception system requires collecting 3D information of the surrounding objects, such as their dimensions, locations, and orientation in space. Recently, deep learning has been widely used in perception systems that convert image features from a camera into semantic information. This paper presents the MonoGhost network, a lightweight Monocular GhostNet deep learning technique for full 3D object properties estimation from a single frame monocular image. Unlike other techniques, the proposed MonoGhost network first estimates relatively reliable 3D object properties depending on efficient feature extractor. The proposed MonoGhost network estimates the orientation of the 3D object as well as the 3D dimensions of that object, resulting in reasonably small errors in the dimensions estimations versus other networks. These estimations, combined with the translation projection constraints imposed by the 2D detection coordinates, allow for the prediction of a robust and dependable Bird’s Eye View bounding box. The experimental outcomes prove that the proposed MonoGhost network performs better than other state-of-the-art networks in the Bird’s Eye View of the KITTI dataset benchmark by scoring 16.73% on the moderate class and 15.01% on the hard class while preserving real-time requirements. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots in Unstructured Environments)
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28 pages, 13987 KiB  
Article
Keypoint Detection and Description through Deep Learning in Unstructured Environments
by Georgios Petrakis and Panagiotis Partsinevelos
Robotics 2023, 12(5), 137; https://doi.org/10.3390/robotics12050137 - 30 Sep 2023
Viewed by 2452
Abstract
Feature extraction plays a crucial role in computer vision and autonomous navigation, offering valuable information for real-time localization and scene understanding. However, although multiple studies investigate keypoint detection and description algorithms in urban and indoor environments, far fewer studies concentrate in unstructured environments. [...] Read more.
Feature extraction plays a crucial role in computer vision and autonomous navigation, offering valuable information for real-time localization and scene understanding. However, although multiple studies investigate keypoint detection and description algorithms in urban and indoor environments, far fewer studies concentrate in unstructured environments. In this study, a multi-task deep learning architecture is developed for keypoint detection and description, focused on poor-featured unstructured and planetary scenes with low or changing illumination. The proposed architecture was trained and evaluated using a training and benchmark dataset with earthy and planetary scenes. Moreover, the trained model was integrated in a visual SLAM (Simultaneous Localization and Maping) system as a feature extraction module, and tested in two feature-poor unstructured areas. Regarding the results, the proposed architecture provides a mAP (mean Average Precision) in a level of 0.95 in terms of keypoint description, outperforming well-known handcrafted algorithms while the proposed SLAM achieved two times lower RMSE error in a poor-featured area with low illumination, compared with ORB-SLAM2. To the best of the authors’ knowledge, this is the first study that investigates the potential of keypoint detection and description through deep learning in unstructured and planetary environments. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots in Unstructured Environments)
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