Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.7
2.9
Journals
Robotics
Special Issues
Active Methods in Autonomous Navigation
share announcement

Active Methods in Autonomous Navigation

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 8241

Special Issue Editors

Dr. Konstantinos Tsintotas

E-Mail Website
Guest Editor
Laboratory of Robotics and Automation, Democritus University of Thrace, Xanthi, Greece
Interests: autonomous robots; visual-based navigation; place recognition; loop closure detection; SLAM
Dr. Loukas Bampis

E-Mail Website
Guest Editor
Mechatronics & Systems Automation Lab, Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece
Interests: robotic vision; localization and mapping; place recognition
Dr. Nitin J Sanket

E-Mail Website
Guest Editor
Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
Interests: computer vision; robotics; quadrotors; deep learning; image processing
Prof. Dr. Antonios Gasteratos

E-Mail Website
Guest Editor
Department of Production and Management Engineering, Democritus University of Thrace, Vas. Sofias 12, GR-67100 Xanthi, Greece
Interests: robotics; computer vision; mechatronics; navigation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giulio Sandini

E-Mail Website
Guest Editor
Robotics, Brain and Cognitive Sciences, Italian Institute of Technology, Genova, Italy
Interests: robotics; brain and cognitive sciences
Prof. Dr. Yiannis Aloimonos

E-Mail Website
Guest Editor
Perception & Robotics Group, Computer Science, University of Maryland, College Park, MD, USA
Interests: robots with vision; manipulation; navigation

Special Issue Information

Dear Colleagues,

The goal of this Special Issue on “Active methods in autonomous navigation” is to bring together researchers, industry professionals, and enthusiasts across different sectors related to navigation, such as driverless car technology, unmanned aerial vehicles (UAVs), humanoid locomotion, rover navigation, social navigation, and SLAM—including re-localization and loop-closure detection, swarm navigation, and semantic mapping, coupling their functionality with perception and action recognition. Moreover, we also encourage size, weight, area, and power (SWAP)—aware navigation methods and bio-inspired approaches for mobile-robot autonomy. Our discussion will range from previously experienced problems to contemporary solutions and future trends, focusing on purposive perception planning since it remains an open problem in the community.

Call for Papers

We invite the submission of papers related to autonomous navigation based on active perception or action and perception-coupled methods. Topics of interest for this workshop and the associated Special Issue include (but are not limited to) papers describing active solutions in a series of areas:

  • Autonomous navigation;
  • Perception–action coupling;
  • Driverless cars;
  • Unmanned aerial vehicles (UAVs);
  • Size, weight, area and power (SWAP)-aware design;
  • Bio-inspired navigation;
  • Planetary exploration;
  • Swarm behavior;
  • Social navigation;
  • Semantic mapping and navigation;
  • Visual place recognition;
  • Simultaneous localization and mapping (SLAM);
  • Visual odometry;
  • Hyper-dimensional computing for perception;
  • Human action recognition;
  • Space robotics.

Dr. Konstantinos Tsintotas
Dr. Loukas Bampis
Dr. Nitin J Sanket
Prof. Dr. Antonios Gasteratos
Prof. Dr. Giulio Sandini
Prof. Dr. Yiannis Aloimonos
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. 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 3224 KiB  
Article
Large-Scale Urban Traffic Management Using Zero-Shot Knowledge Transfer in Multi-Agent Reinforcement Learning for Intersection Patterns
by Theodore Tranos, Christos Spatharis, Konstantinos Blekas and Andreas-Giorgios Stafylopatis
Robotics 2024, 13(7), 109; https://doi.org/10.3390/robotics13070109 - 19 Jul 2024
Viewed by 647
Abstract
The automatic control of vehicle traffic in large urban networks constitutes one of the most serious challenges to modern societies, with an impact on improving the quality of human life and saving energy and time. Intersections are a special traffic structure of pivotal [...] Read more.
The automatic control of vehicle traffic in large urban networks constitutes one of the most serious challenges to modern societies, with an impact on improving the quality of human life and saving energy and time. Intersections are a special traffic structure of pivotal importance as they accumulate a large number of vehicles that should be served in an optimal manner. Constructing intelligent models that manage to automatically coordinate and steer vehicles through intersections is a key point in the fragmentation of traffic control, offering active solutions through the flexibility of automatically adapting to a variety of traffic conditions. Responding to this call, this work aims to propose an integrated active solution of automatic traffic management. We introduce a multi-agent reinforcement learning framework that effectively models traffic flow at individual unsignalized intersections. It relies on a compact agent definition, a rich information state space, and a learning process characterized not only by depth and quality, but also by substantial degrees of freedom and variability. The resulting driving profiles are further transferred to larger road networks to integrate their individual elements and compose an effective automatic traffic control platform. Experiments are conducted on simulated road networks of variable complexity, demonstrating the potential of the proposed method. Full article
(This article belongs to the Special Issue Active Methods in Autonomous Navigation)
►▼ Show Figures

Figure 1

22 pages, 1188 KiB  
Article
Online Odometry Calibration for Differential Drive Mobile Robots in Low Traction Conditions with Slippage
by Carlo De Giorgi, Daniela De Palma and Gianfranco Parlangeli
Robotics 2024, 13(1), 7; https://doi.org/10.3390/robotics13010007 - 27 Dec 2023
Viewed by 2182
Abstract
This paper addresses a systematic method for odometry calibration of a differential-drive mobile robot moving on arbitrary paths in the presence of slippage and an algorithm encoding it which is well fit for online applications. It exploits the redundancy of sensors commonly available [...] Read more.
This paper addresses a systematic method for odometry calibration of a differential-drive mobile robot moving on arbitrary paths in the presence of slippage and an algorithm encoding it which is well fit for online applications. It exploits the redundancy of sensors commonly available on ground mobile robots, such as encoders, gyroscopes, and IMU, to promptly detect slippage phenomena during the calibration process and effectively address their impact on odometry. The proposed technique has been validated through exhaustive numerical simulations and compared with other available odometry calibration methods. The simulation results confirm that the proposed methodology mitigates the impact of poor calibration, conducted without considering possible slipping phenomena, on reaching a target position, reducing the error by up to a maximum of 35 times. This restores the robot’s performance to a calibration condition close to that of a slip-free scenario, confirming the effectiveness of the approach and its robustness against slippage phenomena. Full article
(This article belongs to the Special Issue Active Methods in Autonomous Navigation)
►▼ Show Figures

Figure 1

33 pages, 82129 KiB  
Article
Implicit Shape Model Trees: Recognition of 3-D Indoor Scenes and Prediction of Object Poses for Mobile Robots
by Pascal Meißner and Rüdiger Dillmann
Robotics 2023, 12(6), 158; https://doi.org/10.3390/robotics12060158 - 23 Nov 2023
Viewed by 1850
Abstract
This article describes an approach for mobile robots to identify scenes in configurations of objects spread across dense environments. This identification is enabled by intertwining the robotic object search and the scene recognition on already detected objects. We proposed “Implicit Shape Model (ISM) [...] Read more.
This article describes an approach for mobile robots to identify scenes in configurations of objects spread across dense environments. This identification is enabled by intertwining the robotic object search and the scene recognition on already detected objects. We proposed “Implicit Shape Model (ISM) trees” as a scene model to solve these two tasks together. This article presents novel algorithms for ISM trees to recognize scenes and predict object poses. For us, scenes are sets of objects, some of which are interrelated by 3D spatial relations. Yet, many false positives may occur when using single ISMs to recognize scenes. We developed ISM trees, which is a hierarchical model of multiple interconnected ISMs, to remedy this. In this article, we contribute a recognition algorithm that allows the use of these trees for recognizing scenes. ISM trees should be generated from human demonstrations of object configurations. Since a suitable algorithm was unavailable, we created an algorithm for generating ISM trees. In previous work, we integrated the object search and scene recognition into an active vision approach that we called “Active Scene Recognition”. An efficient algorithm was unavailable to make their integration using predicted object poses effective. Physical experiments in this article show that the new algorithm we have contributed overcomes this problem. Full article
(This article belongs to the Special Issue Active Methods in Autonomous Navigation)
►▼ Show Figures

Figure 1

28 pages, 66441 KiB  
Article
Real-Time 3D Map Building in a Mobile Robot System with Low-Bandwidth Communication
by Alfin Junaedy, Hiroyuki Masuta, Kei Sawai, Tatsuo Motoyoshi and Noboru Takagi
Robotics 2023, 12(6), 157; https://doi.org/10.3390/robotics12060157 - 22 Nov 2023
Cited by 2 | Viewed by 2154
Abstract
This paper presents a new 3D map building technique using a combination of 2D SLAM and 3D objects that can be implemented on relatively low-cost hardware in real-time. Recently, 3D visualization of the real world became increasingly important. In robotics, it is not [...] Read more.
This paper presents a new 3D map building technique using a combination of 2D SLAM and 3D objects that can be implemented on relatively low-cost hardware in real-time. Recently, 3D visualization of the real world became increasingly important. In robotics, it is not only required for intelligent control, but also necessary for operators to provide intuitive visualization. SLAM is generally applied for this purpose, as it is considered a basic ability for truly autonomous robots. However, due to the increase in the amount of data, real-time processing is becoming a challenge. Therefore, in order to address this problem, we combine 2D data and 3D objects to create a new 3D map. The combination is simple yet robust based on rotation, translation, and clustering techniques. The proposed method was applied to a mobile robot system for indoor observation. The results show that real-time performance can be achieved by the system. Furthermore, we also combine high and low-bandwidth networks to deal with network problems that usually occur in wireless communication. Thus, robust wireless communication can be established, as it ensures that the missions can be continued even if the system loses the main network. Full article
(This article belongs to the Special Issue Active Methods in Autonomous Navigation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop