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Special Issue "Applications of Multisensory Fusion for Automation and Control of Robotic Systems"

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

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editors

Prof. Dr. Abdelaziz Benallegue
E-Mail Website
Guest Editor
Versailles Engineering Systems Laboratory, University of Versailles Saint-Quentin, 78000 Versailles, France
Interests: multi-sensor fusion; control systems; robotics and mechatronic systems; autonomous vehicles; UAV's; aerial manipulation; humanoids
Dr. A. El Hadri
E-Mail Website
Guest Editor
Versailles Engineering Systems Laboratory, University of Versailles Saint-Quentin, 78000 Versailles, France
Interests: multi-sensor fusion; systems modeling and analysis; control systems; robotics and mechatronic systems; autonomous vehicle; UAV's; aerial manipulation

Special Issue Information

Dear Colleagues,

For decades, the tasks assigned to robots have constantly evolved to give birth to today's robots, that which participate more and more in the daily life of humans. The robots of yesteryear were very heavy manipulators, static and confined in factories to perform repetitive tasks in often familiar and static environments. Today's robots are increasingly mobile, independent, intelligent, and autonomous to provide more services that were, not so long ago, pure science fiction. The continuous improvement of their autonomy and efficiency has enabled their implementation in many fields such as transportation, medicine, construction, agriculture, and human services. Advances in sensor technologies and their integration into intelligent devices and systems have accelerated the increase in the autonomy and efficiency of robotic systems, enabling them to perform tasks that are more complex.

In this Special Issue, we invite original review and research papers addressing multi-sensor integration and fusion for the control of robotic systems such as UAVs, humanoid robots, collaborative robots, robotic manipulators, aerial manipulators, automated vehicles, etc.

Topics of interest include, but are not limited to, the following:

  • Advanced robotics
  • Advances in robotic applications
  • Advances in perception and control
  • Multi-sensor system design
  • Integration of sensors in control systems
  • Position and localization systems
  • Sensor technique in robotic applications
  • Sensor-based robot navigation, task, and motion planning
  • Sensor integration and fusion for positioning and navigation
  • Flight control and surveillance systems
  • Guidance control systems
  • Intelligent transportation systems
  • Locomotion and manipulation in robot systems

Prof. Dr. Abdelaziz Benallegue
Dr. A. El Hadri
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. Sensors is an international peer-reviewed open access semimonthly 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 2200 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

  • Robot sensing and data fusion
  • Perception systems
  • Robust control systems
  • Nonlinear control systems
  • Smart sensors
  • Fusion techniques
  • Multisensory integration and fusion
  • Vision-based sensing

Published Papers (3 papers)

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Research

Communication
Trajectory Planner CDT-RRT* for Car-Like Mobile Robots toward Narrow and Cluttered Environments
Sensors 2021, 21(14), 4828; https://doi.org/10.3390/s21144828 - 15 Jul 2021
Viewed by 348
Abstract
In order to achieve the safe and efficient navigation of mobile robots, it is essential to consider both the environmental geometry and kinodynamic constraints of robots. We propose a trajectory planner for car-like robots on the basis of the Dual-Tree RRT (DT-RRT). DT-RRT [...] Read more.
In order to achieve the safe and efficient navigation of mobile robots, it is essential to consider both the environmental geometry and kinodynamic constraints of robots. We propose a trajectory planner for car-like robots on the basis of the Dual-Tree RRT (DT-RRT). DT-RRT utilizes two tree structures in order to generate fast-growing trajectories under the kinodynamic constraints of robots. A local trajectory generator has been newly designed for car-like robots. The proposed scheme of searching a parent node enables the efficient generation of safe trajectories in cluttered environments. The presented simulation results clearly show the usefulness and the advantage of the proposed trajectory planner in various environments. Full article
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Article
General Purpose Low-Level Reinforcement Learning Control for Multi-Axis Rotor Aerial Vehicles
Sensors 2021, 21(13), 4560; https://doi.org/10.3390/s21134560 - 02 Jul 2021
Viewed by 450
Abstract
This paper proposes a multipurpose reinforcement learning based low-level multirotor unmanned aerial vehicles control structure constructed using neural networks with model-free training. Other low-level reinforcement learning controllers developed in studies have only been applicable to a model-specific and physical-parameter-specific multirotor, and time-consuming training [...] Read more.
This paper proposes a multipurpose reinforcement learning based low-level multirotor unmanned aerial vehicles control structure constructed using neural networks with model-free training. Other low-level reinforcement learning controllers developed in studies have only been applicable to a model-specific and physical-parameter-specific multirotor, and time-consuming training is required when switching to a different vehicle. We use a 6-degree-of-freedom dynamic model combining acceleration-based control from the policy neural network to overcome these problems. The UAV automatically learns the maneuver by an end-to-end neural network from fusion states to acceleration command. The state estimation is performed using the data from on-board sensors and motion capture. The motion capture system provides spatial position information and a multisensory fusion framework fuses the measurement from the onboard inertia measurement units for compensating the time delay and low update frequency of the capture system. Without requiring expert demonstration, the trained control policy implemented using an improved algorithm can be applied to various multirotors with the output directly mapped to actuators. The algorithm’s ability to control multirotors in the hovering and the tracking task is evaluated. Through simulation and actual experiments, we demonstrate the flight control with a quadrotor and hexrotor by using the trained policy. With the same policy, we verify that we can stabilize the quadrotor and hexrotor in the air under random initial states. Full article
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Article
Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation
Sensors 2021, 21(6), 1937; https://doi.org/10.3390/s21061937 - 10 Mar 2021
Viewed by 865
Abstract
Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are [...] Read more.
Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates. The complementary filter is one of the widely adopted techniques whose performance is highly dependent on the appropriate selection of its gain parameters. This paper presents a novel cascaded architecture of the complementary filter that employs a nonlinear and linear version of the complementary filter within one framework. The nonlinear version is used to correct the gyroscope bias, while the linear version estimates the attitude angle. The significant advantage of the proposed architecture is its independence of the filter parameters, thereby avoiding tuning the filter’s gain parameters. The proposed architecture does not require any mathematical modeling of the system and is computationally inexpensive. The proposed methodology is applied to the real-world datasets, and the estimation results were found to be promising compared to the other state-of-the-art algorithms. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Building reliable control applications with wireless communication technologies. Application to robotic systems
Authors: Oscar Barambones <[email protected]>
Affiliation: Department of System Engineering and Automation, Faculty of Engineering of Vitoria, University of the Basque Country, UPV/EHU. Nieves Cano 12, 1006 Vitoria, Spain
Abstract: Wireless communications introduce several benefits in industrial environments such as flexibility, low cost, simple deployment or mobility. The problem of mobility is particularly interesting in a robotic system because usually the movement of the rotating joints are limited due to the wired connections. However, the introduction of wireless communications in critical applications is challenging due to several issues that can reduce the QoS of the applications (e.g. interferences). As a result, packages may be delayed or lost, being possible to appear communication blackouts. For this reason, the design of wireless control algorithms must be faced in a holistic way, i.e. taking into account the behavior of the wireless communication links. This paper proposes diverse mechanisms to achieve reliable wireless control systems. These mechanisms are aimed at guaranteeing the performance of the control systems, even when the QoS of the communication links. In the article complex control algorithms are implemented, which require higher performance computational platforms, not suitable to be used at the edge nodes. Several experiments are presented in order to analyze the behavior of the control application under different circumstances. These experiments were tested over diverse control algorithms and plants like DC motors usually employed in robotics. In this work, XBee technology was used, and the control algorithms were implemented in LabVIEW. The results proved that the introduction of these mechanisms may improve the performance of the control system, allowing the use of wireless communications even in critical control applications.

Title: Biosignal-based Human-Machine Interfaces for Assistance and Rehabilitation: a survey
Authors: Daniele Esposito 1,2,* Jessica Centracchio 1 Emilio Andreozzi 1,2 Gaetano D. Gargiulo 3,4 Ganesh R. Naik 3,4,* and Paolo Bifulco 1,2
Affiliation: 1. Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, Naples, Italy

2. Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy.

3. School of Computing, Engineering and Mathematics, Western Sydney University, Penrith-2747, NSW, Aus-tralia.

4. The MARCS Institute, Western Sydney University, Penrith, NSW 2751, Australia.
Abstract: A Human-Machine Interface (HMI) enables a person to interact with a device. Starting from very simple equipments, in the last decades, the availability of novel techniques and unobtrusive de-vices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various kinds of applications. Different biosignals have been proposed to ena-ble the interaction with hardware and software machines: bio-potentials (such as electromyogra-phy or electroencephalography); muscle mechanical activity (such as mechano-myography, force-myography); body motion (such as joint motion, hand gesture, eye movements) and also their combinations (hybrid systems). These biosignal-based HMIs can provide a more natural way to interact with a machine, also in industrial or automotive settings, but above all they have the invaluable merit of enabling these interactions also for people with disabilities that prevent the use of standard HMIs. Indeed, many assistive and rehabilitation technologies make use of biosig-nal-based HMIs. Glaring examples are prostheses, orthoses, rehabilitation devices, robotic appli-ances, exoskeletons, electrical stimulators, but even rehabilitative exergames make use of such HMIs. This review article provides a comprehensive survey of biosignal-based HMIs proposed for assistive and rehabilitation applications.

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