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Intelligent Sensing System and Robotics
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Intelligent Sensing System and Robotics

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

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 6889

Special Issue Editors

Dr. Guangrong Chen

E-Mail Website
Guest Editor
Robotics Research Center, Beijing Jiaotong University, Beijing 100044, China
Interests: robotic sensing and control; robotic mechanisms
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Baochang Zhang

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
Interests: robotic vision; path planning of UAVs; pattern recognition; machine learning; face recognition; wavelets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of robotic technology and computer science, robotic sensing and control have combined with popular technologies, including machine learning, deep reinforcement learning, and intelligent sensing and control. This further extends to many emerging applications in diverse sectors ranging from individual robots to robots from cross domains, enabling mobile robots to implement real-time data transmission and resource sharing.

With a comprehensive perception inside and outside of robots, the appropriate controller can be established to achieve better performance. Additionally, in multi-robot systems, sensing data sharing is good for system sensing and control. To sum up, there is a critical need for researchers and practitioners from both academia and industry to discuss novel sensing and control solutions in robotics.

This Special Collection aims to collect the latest developments in robotic sensing and control, including studies on framework design, robotic mechanisms, sensing data collection and analytics, control design and performance tradeoffs, and information processing algorithms.

Potential topics for submissions include but are not limited to:

  • Robotic sensing and control;
  • Advances in sensing and control;
  • Machine learning for sensing and control;
  • End-to-end sensing and control;
  • Collaborative sensing and control;
  • Cross domain sensing and control;
  • Motion sensing and control algorithms;
  • Soft robotic sensing and control;
  • Tactile sensing and control.

Dr. Guangrong Chen
Prof. Dr. Baochang Zhang
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

  • sensors
  • control
  • actuators
  • robotics

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Published Papers (3 papers)

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Research

20 pages, 5390 KiB  
Article
LiDAR-Based Maintenance of a Safe Distance between a Human and a Robot Arm
by David Podgorelec, Suzana Uran, Andrej Nerat, Božidar Bratina, Sašo Pečnik, Marjan Dimec, Franc Žaberl, Borut Žalik and Riko Šafarič
Sensors 2023, 23(9), 4305; https://doi.org/10.3390/s23094305 - 26 Apr 2023
Cited by 1 | Viewed by 2329
Abstract
This paper demonstrates the capabilities of three-dimensional (3D) LiDAR scanners in supporting a safe distance maintenance functionality in human–robot collaborative applications. The use of such sensors is severely under-utilised in collaborative work with heavy-duty robots. However, even with a relatively modest proprietary 3D [...] Read more.
This paper demonstrates the capabilities of three-dimensional (3D) LiDAR scanners in supporting a safe distance maintenance functionality in human–robot collaborative applications. The use of such sensors is severely under-utilised in collaborative work with heavy-duty robots. However, even with a relatively modest proprietary 3D sensor prototype, a respectable level of safety has been achieved, which should encourage the development of such applications in the future. Its associated intelligent control system (ICS) is presented, as well as the sensor’s technical characteristics. It acquires the positions of the robot and the human periodically, predicts their positions in the near future optionally, and adjusts the robot’s speed to keep its distance from the human above the protective separation distance. The main novelty is the possibility to load an instance of the robot programme into the ICS, which then precomputes the future position and pose of the robot. Higher accuracy and safety are provided, in comparison to traditional predictions from known real-time and near-past positions and poses. The use of a 3D LiDAR scanner in a speed and separation monitoring application and, particularly, its specific placing, are also innovative and advantageous. The system was validated by analysing videos taken by the reference validation camera visually, which confirmed its safe operation in reasonably limited ranges of robot and human speeds. Full article
(This article belongs to the Special Issue Intelligent Sensing System and Robotics)
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19 pages, 6443 KiB  
Article
Proposal and Implementation of a Procedure for Compliance Recognition of Objects with Smart Tactile Sensors
by Raúl Lora-Rivera, Óscar Oballe-Peinado and Fernando Vidal-Verdú
Sensors 2023, 23(8), 4120; https://doi.org/10.3390/s23084120 - 19 Apr 2023
Viewed by 1870
Abstract
This paper presents a procedure for classifying objects based on their compliance with information gathered using tactile sensors. Specifically, smart tactile sensors provide the raw moments of the tactile image when the object is squeezed and desqueezed. A set of simple parameters from [...] Read more.
This paper presents a procedure for classifying objects based on their compliance with information gathered using tactile sensors. Specifically, smart tactile sensors provide the raw moments of the tactile image when the object is squeezed and desqueezed. A set of simple parameters from moment-versus-time graphs are proposed as features, to build the input vector of a classifier. The extraction of these features was implemented in the field programmable gate array (FPGA) of a system on chip (SoC), while the classifier was implemented in its ARM core. Many different options were realized and analyzed, depending on their complexity and performance in terms of resource usage and accuracy of classification. A classification accuracy of over 94% was achieved for a set of 42 different classes. The proposed approach is intended for developing architectures with preprocessing on the embedded FPGA of smart tactile sensors, to obtain high performance in real-time complex robotic systems. Full article
(This article belongs to the Special Issue Intelligent Sensing System and Robotics)
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17 pages, 4023 KiB  
Article
Signal Novelty Detection as an Intrinsic Reward for Robotics
by Martin Kubovčík, Iveta Dirgová Luptáková and Jiří Pospíchal
Sensors 2023, 23(8), 3985; https://doi.org/10.3390/s23083985 - 14 Apr 2023
Cited by 2 | Viewed by 1754
Abstract
In advanced robot control, reinforcement learning is a common technique used to transform sensor data into signals for actuators, based on feedback from the robot’s environment. However, the feedback or reward is typically sparse, as it is provided mainly after the task’s completion [...] Read more.
In advanced robot control, reinforcement learning is a common technique used to transform sensor data into signals for actuators, based on feedback from the robot’s environment. However, the feedback or reward is typically sparse, as it is provided mainly after the task’s completion or failure, leading to slow convergence. Additional intrinsic rewards based on the state visitation frequency can provide more feedback. In this study, an Autoencoder deep learning neural network was utilized as novelty detection for intrinsic rewards to guide the search process through a state space. The neural network processed signals from various types of sensors simultaneously. It was tested on simulated robotic agents in a benchmark set of classic control OpenAI Gym test environments (including Mountain Car, Acrobot, CartPole, and LunarLander), achieving more efficient and accurate robot control in three of the four tasks (with only slight degradation in the Lunar Lander task) when purely intrinsic rewards were used compared to standard extrinsic rewards. By incorporating autoencoder-based intrinsic rewards, robots could potentially become more dependable in autonomous operations like space or underwater exploration or during natural disaster response. This is because the system could better adapt to changing environments or unexpected situations. Full article
(This article belongs to the Special Issue Intelligent Sensing System and Robotics)
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