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Smart Sensors for Robotic Systems
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Smart Sensors for Robotic Systems

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

Deadline for manuscript submissions: closed (15 March 2021) | Viewed by 25086

Special Issue Editors

Prof. Marina Indri

E-Mail Website
Guest Editor
Dip. di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy
Interests: industrial robotics; collision detection and avoidance; friction modelling and compensation; path planning and autonomous navigation of mobile robots; Smart manufacturing and Industry 4.0
Dr. Andrea Bonci

E-Mail Website
Guest Editor
Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Ancona, Italy
Interests: systems and control theory; embedded system applications; cyber physical systems; smart manufacturing and Industry 4.0; vehicle dynamics and control; autonomous systems and control; robotics; smart materials; automation in construction; sensor fusion

Special Issue Information

Dear Colleagues,

Robotics is currently undergoing a deep revolution, not only in the industrial context, fuelled by the Industry 4.0 programs and Smart Factories concepts, but also in everyday life, with a growing autonomy and human-interaction of the robotic systems. Most of the recent, advanced applications in both scenarios rely on the integration of smart sensors, or on innovative uses of the standard ones. Thanks to this, high levels of autonomy are reached; safety and collaboration with humans can be guaranteed; and anomalies, faults, and the potential need for maintenance can be detected.

Planning and sensing or the acquisition of perceptions on the operating environment is a crucial component, as is the reliability of the sensory system, which can be increased by concurrent or redundant sensors. This allows robots to become "smart", be able to learn independently the task to be performed, and "make decisions" based on the current environmental situation in which they are found. Sensors can generate increasingly massive volumes of highly varied data, which can help build better machine learning (ML) and artificial intelligence (AI) models, which robots rely on in order to become “autonomous,” make real-time decisions, and navigate in dynamic real-world environments.

The aim of this Special Issue is to provide comprehensive insight into the most advanced potentialities of robotic systems, achievable thanks to proper and innovative sensors. Topics of interest include, but are not limited to, the following:

  • Innovative robotic applications based on smart sensors
  • Advanced sensors for robotics
  • Sensor fusion techniques for robotic systems
  • Virtual sensors for robots
  • Sensor applications for safety, predictive maintenance, and diagnostic procedures for robotic systems
  • Sensors for increasing the capabilities of ML and AI models
  • Sensor for enabling predictive actions based on how fast objects are approaching
  • Highly accurate sensors.
Prof. Marina Indri
Dr. Andrea Bonci
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. 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 2600 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

  • robots
  • sensors 
  • detection
  • perception 
  • sensor fusions
  • smart sensors 
  • virtual sensors 
  • sensor arrays 
  • combined sensors 
  • multiple sensors

Published Papers (5 papers)

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Research

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17 pages, 2902 KiB  
Article
Design and Calibration of Robot Base Force/Torque Sensors and Their Application to Non-Collocated Admittance Control for Automated Tool Changing
by Hubert Gattringer, Andreas Müller and Philip Hoermandinger
Sensors 2021, 21(9), 2895; https://doi.org/10.3390/s21092895 - 21 Apr 2021
Cited by 3 | Viewed by 3294
Abstract
Robotic manipulators physically interacting with their environment must be able to measure contact forces/torques. The standard approach to this end is attaching force/torque sensors directly at the end-effector (EE). This provides accurate measurements, but at a significant cost. Indirect measurement of the EE-loads [...] Read more.
Robotic manipulators physically interacting with their environment must be able to measure contact forces/torques. The standard approach to this end is attaching force/torque sensors directly at the end-effector (EE). This provides accurate measurements, but at a significant cost. Indirect measurement of the EE-loads by means of torque sensors at the actuated joint of a robot is an alternative, in particular for series-elastic actuators, but requires dedicated robot designs and significantly increases costs. In this paper, two alternative sensor concept for indirect measurement of EE-loads are presented. Both sensors are located at the robot base. The first sensor design involves three load cells on which the robot is mounted. The second concept consists of a steel plate with four spokes, at which it is suspended. At each spoke, strain gauges are attached to measure the local deformation, which is related to the load at the sensor plate (resembling the main principle of a force/torque sensor). Inferring the EE-load from the so determined base wrench necessitates a dynamic model of the robot, which accounts for the static as well as dynamic loads. A prototype implementation of both concepts is reported. Special attention is given to the model-based calibration, which is crucial for these indirect measurement concepts. Experimental results are shown when the novel sensors are employed for a tool changing task, which to some extend resembles the well-known peg-in-the-hole problem. Full article
(This article belongs to the Special Issue Smart Sensors for Robotic Systems)
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17 pages, 9686 KiB  
Article
Artificial Intelligence-Based Optimal Grasping Control
by Dongeon Kim, Jonghak Lee, Wan-Young Chung and Jangmyung Lee
Sensors 2020, 20(21), 6390; https://doi.org/10.3390/s20216390 - 9 Nov 2020
Cited by 16 | Viewed by 2881
Abstract
A new tactile sensing module was proposed to sense the contact force and location of an object on a robot hand, which was attached on the robot finger. Three air pressure sensors are installed at the tip of the finger to detect the [...] Read more.
A new tactile sensing module was proposed to sense the contact force and location of an object on a robot hand, which was attached on the robot finger. Three air pressure sensors are installed at the tip of the finger to detect the contacting force at the points. To obtain a nominal contact force at the finger from data from the three air pressure sensors, a force estimation was developed based upon the learning of a deep neural network. The data from the three air pressure sensors were utilized as inputs to estimate the contact force at the finger. In the tactile module, the arrival time of the air pressure sensor data has been utilized to recognize the contact point of the robot finger against an object. Using the three air pressure sensors and arrival time, the finger location can be divided into 3 × 3 block locations. The resolution of the contact point recognition was improved to 6 × 4 block locations on the finger using an artificial neural network. The accuracy and effectiveness of the tactile module were verified using real grasping experiments. With this stable grasping, an optimal grasping force was estimated empirically with fuzzy rules for a given object. Full article
(This article belongs to the Special Issue Smart Sensors for Robotic Systems)
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32 pages, 5896 KiB  
Article
Monocular Visual SLAM Based on a Cooperative UAV–Target System
by Juan-Carlos Trujillo, Rodrigo Munguia, Sarquis Urzua, Edmundo Guerra and Antoni Grau
Sensors 2020, 20(12), 3531; https://doi.org/10.3390/s20123531 - 22 Jun 2020
Cited by 15 | Viewed by 3363
Abstract
To obtain autonomy in applications that involve Unmanned Aerial Vehicles (UAVs), the capacity of self-location and perception of the operational environment is a fundamental requirement. To this effect, GPS represents the typical solution for determining the position of a UAV operating in outdoor [...] Read more.
To obtain autonomy in applications that involve Unmanned Aerial Vehicles (UAVs), the capacity of self-location and perception of the operational environment is a fundamental requirement. To this effect, GPS represents the typical solution for determining the position of a UAV operating in outdoor and open environments. On the other hand, GPS cannot be a reliable solution for a different kind of environments like cluttered and indoor ones. In this scenario, a good alternative is represented by the monocular SLAM (Simultaneous Localization and Mapping) methods. A monocular SLAM system allows a UAV to operate in a priori unknown environment using an onboard camera to simultaneously build a map of its surroundings while at the same time locates itself respect to this map. So, given the problem of an aerial robot that must follow a free-moving cooperative target in a GPS denied environment, this work presents a monocular-based SLAM approach for cooperative UAV–Target systems that addresses the state estimation problem of (i) the UAV position and velocity, (ii) the target position and velocity, (iii) the landmarks positions (map). The proposed monocular SLAM system incorporates altitude measurements obtained from an altimeter. In this case, an observability analysis is carried out to show that the observability properties of the system are improved by incorporating altitude measurements. Furthermore, a novel technique to estimate the approximate depth of the new visual landmarks is proposed, which takes advantage of the cooperative target. Additionally, a control system is proposed for maintaining a stable flight formation of the UAV with respect to the target. In this case, the stability of control laws is proved using the Lyapunov theory. The experimental results obtained from real data as well as the results obtained from computer simulations show that the proposed scheme can provide good performance. Full article
(This article belongs to the Special Issue Smart Sensors for Robotic Systems)
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19 pages, 6217 KiB  
Article
Sorting Objects from a Conveyor Belt Using POMDPs with Multiple-Object Observations and Information-Gain Rewards
by Ady-Daniel Mezei, Levente Tamás and Lucian Buşoniu
Sensors 2020, 20(9), 2481; https://doi.org/10.3390/s20092481 - 27 Apr 2020
Cited by 1 | Viewed by 3086
Abstract
We consider a robot that must sort objects transported by a conveyor belt into different classes. Multiple observations must be performed before taking a decision on the class of each object, because the imperfect sensing sometimes detects the incorrect object class. The objective [...] Read more.
We consider a robot that must sort objects transported by a conveyor belt into different classes. Multiple observations must be performed before taking a decision on the class of each object, because the imperfect sensing sometimes detects the incorrect object class. The objective is to sort the sequence of objects in a minimal number of observation and decision steps. We describe this task in the framework of partially observable Markov decision processes, and we propose a reward function that explicitly takes into account the information gain of the viewpoint selection actions applied. The DESPOT algorithm is applied to solve the problem, automatically obtaining a sequence of observation viewpoints and class decision actions. Observations are made either only for the object on the first position of the conveyor belt or for multiple adjacent positions at once. The performance of the single- and multiple-position variants is compared, and the impact of including the information gain is analyzed. Real-life experiments with a Baxter robot and an industrial conveyor belt are provided. Full article
(This article belongs to the Special Issue Smart Sensors for Robotic Systems)
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Review

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29 pages, 2609 KiB  
Review
Human-Robot Perception in Industrial Environments: A Survey
by Andrea Bonci, Pangcheng David Cen Cheng, Marina Indri, Giacomo Nabissi and Fiorella Sibona
Sensors 2021, 21(5), 1571; https://doi.org/10.3390/s21051571 - 24 Feb 2021
Cited by 77 | Viewed by 11113
Abstract
Perception capability assumes significant importance for human–robot interaction. The forthcoming industrial environments will require a high level of automation to be flexible and adaptive enough to comply with the increasingly faster and low-cost market demands. Autonomous and collaborative robots able to adapt to [...] Read more.
Perception capability assumes significant importance for human–robot interaction. The forthcoming industrial environments will require a high level of automation to be flexible and adaptive enough to comply with the increasingly faster and low-cost market demands. Autonomous and collaborative robots able to adapt to varying and dynamic conditions of the environment, including the presence of human beings, will have an ever-greater role in this context. However, if the robot is not aware of the human position and intention, a shared workspace between robots and humans may decrease productivity and lead to human safety issues. This paper presents a survey on sensory equipment useful for human detection and action recognition in industrial environments. An overview of different sensors and perception techniques is presented. Various types of robotic systems commonly used in industry, such as fixed-base manipulators, collaborative robots, mobile robots and mobile manipulators, are considered, analyzing the most useful sensors and methods to perceive and react to the presence of human operators in industrial cooperative and collaborative applications. The paper also introduces two proofs of concept, developed by the authors for future collaborative robotic applications that benefit from enhanced capabilities of human perception and interaction. The first one concerns fixed-base collaborative robots, and proposes a solution for human safety in tasks requiring human collision avoidance or moving obstacles detection. The second one proposes a collaborative behavior implementable upon autonomous mobile robots, pursuing assigned tasks within an industrial space shared with human operators. Full article
(This article belongs to the Special Issue Smart Sensors for Robotic Systems)
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