Special Issue "Latest Trends of Autonomous Aerial and Terrestrial Vehicles for Service Robotics Applications"

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

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editors

Prof. Dr. Giuseppe Quaglia
Website
Guest Editor
Dipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, Italy
Interests: robotics; mechatronics; dynamics of vehicles and mechanical systems; industrial automation and fluid automation; applied mechanics; synthesis of mechanisms; mechatronic systems for disabled; appropriate technologies and human development (systems and devices for construction, agriculture, and transport); energy saving and recovery systems
Dr. Luca Carbonari
Website
Guest Editor
Dipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, Italy
Interests: synthesis and design of mechanisms (parallel kinematics robots, mobile robots or automatic machines); robots control; multibody systems dynamics

Special Issue Information

Dear Colleagues,

In recent years, autonomous vehicles and mobile robots have been widely applied in several fields of everyday life: implementation in manufacturing processes, domestic assistance, warehouses logistics, precision agriculture, surveillance, remote presence, and much more.

The last global emergency related to COVID-19 emphasized how mobile autonomous robots can be extremely important in order to provide both adequate services to the patient and to reduce the risks for health workers.  

This increased interest drove the research community to deeply investigate several aspects that directly affect the realization of robotic systems and increase their efficiency, safety, and accessibility to a growing set of possible users.

For this Special Issue, we are looking for high quality, original research papers on the latest trends in autonomous aerial, terrestrial, or aquatic vehicles applied to the field of service robotics. The goal is a snapshot of the current research on novel mechanical structures and control strategies.

Papers are welcome on topics related to aspects of theory, design, practice, and application, including but not limited to:

  • Mechanical design of novel service aerial and terrestrial vehicles;
  • Novel applications and research frontiers;
  • Low level control strategies for safe human–robot coexistence
  • Robots fleets: communication protocols and industrial applications.;
  • Surveillance, patrolling, and rescue: robotics in extreme environments for human safety;
  • Mobile robotics for wellbeing, rehabilitation, and bio-medical applications;
  • Service robots for healthcare in dangerous conditions, like ones occurred in case of COVID-19 pandemic;
  • Simulation and modelling of mobile robots; and
  • Human–robot collaboration in non-productive environments.

Prof. Dr. Giuseppe Quaglia
Dr. Luca Carbonari
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 papers will be 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 quarterly 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 1000 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 service robots
  • healthcare
  • COVID-19
  • rescue robots
  • monitoring
  • precision agriculture

Published Papers (2 papers)

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Research

Open AccessArticle
A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge
Machines 2020, 8(3), 49; https://doi.org/10.3390/machines8030049 - 28 Aug 2020
Abstract
The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the [...] Read more.
The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications. Full article
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Open AccessArticle
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
Machines 2020, 8(2), 27; https://doi.org/10.3390/machines8020027 - 25 May 2020
Cited by 1
Abstract
With the advent of agriculture 3.0 and 4.0, in view of efficient and sustainable use of resources, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural [...] Read more.
With the advent of agriculture 3.0 and 4.0, in view of efficient and sustainable use of resources, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost, power-efficient local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm makes use of the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block generating high-level motion primitives. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model’s knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the appropriate environment. Full article
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