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Advancements in Sensing Technologies and Control Mechanisms for Assistive Robotics: Enhancing Human–Robot Interaction and Assistance

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 7101

Special Issue Editor

Dr. Brahim Brahmi

E-Mail Website
Guest Editor
Electrical Engineering Department, College Ahuntsic, Montreal, QC H2M 1Y8, Canada
Interests: nonlinear and adaptive control; bio-robotics; rehabilitation robots; industrial automation; IIoT; fundamental motion control concepts for nonholonomic/underactuated vehicle systems; haptic systems; intelligent and autonomous control of unmanned systems; intelligent systems; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Assistive robotics has gained significant traction in recent years, offering invaluable support to humans across a wide spectrum of activities, from everyday tasks to intricate procedures. However, the current state of technology and tools utilized in these systems presents certain limitations, resulting in restricted functionality and limited human–robot interaction. To unlock the full potential of assistive robotics, there is a pressing need for innovative advancements in sensing technologies and control mechanisms. These breakthroughs will enable seamless communication and interaction between humans and robots, fostering a more efficient, effective, and safe approach to task execution. This Special Issue aims to explore the latest developments in sensing technologies and control mechanisms for assistive robotics, foster interdisciplinary research, and showcase cutting-edge solutions that empower humans and revolutionize the field of assistive robotics. We invite submissions on, but not limited to, the following subject areas:

  • Rehabilitation robots;
  • Intelligent robotic systems;
  • Human–robot interaction;
  • Tele-operation and haptics;
  • Collaborative robotics;
  • Robot sensors and vision;
  • Control systems;
  • Intelligent sensing;
  • Three-dimensional sensing and modeling;
  • Sensor fusion;
  • Emerging sensor technologies;
  • Distributed sensing;
  • Motion planning and control;
  • Wearable robotics’ cybersecurity

Dr. Brahim Brahmi
Guest Editor

Manuscript Submission Information

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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

  • rehabilitation robot
  • upper limb orthosis
  • lower limb orthosis
  • power augmentation
  • exoskeleton actuators
  • exoskeleton sensors
  • control systems
  • assistive robotics
  • emerging sensors
  • adaptive control approach

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Published Papers (5 papers)

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Research

18 pages, 3188 KiB  
Article
Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach
by Zahid Razzaq, Nihad Brahimi, Hafiz Zia Ur Rehman and Zeashan Hameed Khan
Sensors 2024, 24(18), 5875; https://doi.org/10.3390/s24185875 - 10 Sep 2024
Viewed by 354
Abstract
Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders—such as stroke, amyotrophic lateral [...] Read more.
Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders—such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury—by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user’s intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot. Full article
(This article belongs to the Special Issue Advancements in Sensing Technologies and Control Mechanisms for Assistive Robotics: Enhancing Human–Robot Interaction and Assistance)
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15 pages, 5193 KiB  
Article
Wavelet Transforms Significantly Sparsify and Compress Tactile Interactions
by Ariel Slepyan, Michael Zakariaie, Trac Tran and Nitish Thakor
Sensors 2024, 24(13), 4243; https://doi.org/10.3390/s24134243 - 29 Jun 2024
Viewed by 831
Abstract
As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. [...] Read more.
As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. However, research on the best sparsifying transforms for tactile interactions is lagging. In this work we construct a library of orthogonal and biorthogonal wavelet transforms as sparsifying transforms for tactile interactions and compare their tradeoffs in compression and sparsity. We tested the sparsifying transforms on a publicly available high-density tactile object grasping dataset (548 sensor tactile glove, grasping 26 objects). In addition, we investigated which dimension wavelet transform—1D, 2D, or 3D—would best compress these tactile interactions. Our results show that wavelet transforms are highly efficient at compressing tactile data and can lead to very sparse and compact tactile representations. Additionally, our results show that 1D transforms achieve the sparsest representations, followed by 3D, and lastly 2D. Overall, the best wavelet for coarse approximation is Symlets 4 evaluated temporally which can sparsify to 0.5% sparsity and compress 10-bit tactile data to an average of 0.04 bits per pixel. Future studies can leverage the results of this paper to assist in the compressive sampling of large tactile arrays and free up computational resources for real-time processing on computationally constrained mobile platforms like neuroprosthetics. Full article
(This article belongs to the Special Issue Advancements in Sensing Technologies and Control Mechanisms for Assistive Robotics: Enhancing Human–Robot Interaction and Assistance)
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17 pages, 2540 KiB  
Article
Development of a Two-Finger Haptic Robotic Hand with Novel Stiffness Detection and Impedance Control
by Vahid Mohammadi, Ramin Shahbad, Mojtaba Hosseini, Mohammad Hossein Gholampour, Saeed Shiry Ghidary, Farshid Najafi and Ahad Behboodi
Sensors 2024, 24(8), 2585; https://doi.org/10.3390/s24082585 - 18 Apr 2024
Cited by 3 | Viewed by 1707
Abstract
Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic [...] Read more.
Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic limbs, offering users improved functionality and a more natural sense of touch, and within industrial automation and manufacturing, they contribute to more efficient, safe, and flexible production processes. This paper presents the development of a two-finger robotic hand that employs simple yet precise strategies to manipulate objects without damaging or dropping them. Our innovative approach fused force-sensitive resistor (FSR) sensors with the average current of servomotors to enhance both the speed and accuracy of grasping. Therefore, we aim to create a grasping mechanism that is more dexterous than grippers and less complex than robotic hands. To achieve this goal, we designed a two-finger robotic hand with two degrees of freedom on each finger; an FSR was integrated into each fingertip to enable object categorization and the detection of the initial contact. Subsequently, servomotor currents were monitored continuously to implement impedance control and maintain the grasp of objects in a wide range of stiffness. The proposed hand categorized objects’ stiffness upon initial contact and exerted accurate force by fusing FSR and the motor currents. An experimental test was conducted using a Yale–CMU–Berkeley (YCB) object set consisted of a foam ball, an empty soda can, an apple, a glass cup, a plastic cup, and a small milk packet. The robotic hand successfully picked up these objects from a table and sat them down without inflicting any damage or dropping them midway. Our results represent a significant step forward in developing haptic robotic hands with advanced object perception and manipulation capabilities. Full article
(This article belongs to the Special Issue Advancements in Sensing Technologies and Control Mechanisms for Assistive Robotics: Enhancing Human–Robot Interaction and Assistance)
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27 pages, 10516 KiB  
Article
Adaptive-Robust Controller for Smart Exoskeleton Robot
by Brahim Brahmi, Hicham Dahani, Soraya Bououden, Raouf Farah and Mohamed Habibur Rahman
Sensors 2024, 24(2), 489; https://doi.org/10.3390/s24020489 - 12 Jan 2024
Cited by 1 | Viewed by 1270
Abstract
Rehabilitation robotics has seen growing popularity in recent years due to its immense potential for improving the lives of people with disabilities. However, the complex, uncertain dynamics of these systems present significant control challenges, requiring advanced techniques. This paper introduces a novel adaptive [...] Read more.
Rehabilitation robotics has seen growing popularity in recent years due to its immense potential for improving the lives of people with disabilities. However, the complex, uncertain dynamics of these systems present significant control challenges, requiring advanced techniques. This paper introduces a novel adaptive control framework integrating modified function approximation (MFAT) and double-integral non-singular terminal sliding mode control (DINTSMC). The goal is to achieve precise tracking performance, high robustness, a fast response, a finite convergence time, reduced chattering, and effective handling of unknown system dynamics. A key feature is the incorporation of a higher-order sliding mode observer, eliminating the need for velocity feedback. This provides a new solution for overcoming the inherent variations and uncertainties in robot manipulators, enabling improved accuracy within fixed convergence times. The efficacy of the proposed approach was validated through simulations and experiments on an exoskeleton robot. The results successfully demonstrated the controller’s effectiveness. Stability analysis using Lyapunov theory proved the closed-loop system’s uniform ultimate boundedness. This contribution is expected to enable enhanced control for rehabilitation robots and improved patient outcomes. Full article
(This article belongs to the Special Issue Advancements in Sensing Technologies and Control Mechanisms for Assistive Robotics: Enhancing Human–Robot Interaction and Assistance)
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24 pages, 8573 KiB  
Article
Design, Analysis, and Development of Low-Cost State-of-the-Art Magnetorheological-Based Microprocessor Prosthetic Knee
by Muhammad Usman Qadir, Izhar Ul Haq, Muhammad Awais Khan, Kamran Shah, Houssam Chouikhi and Mohamed A. Ismail
Sensors 2024, 24(1), 255; https://doi.org/10.3390/s24010255 - 1 Jan 2024
Viewed by 2152
Abstract
For amputees, amputation is a devastating experience. Transfemoral amputees require an artificial lower limb prosthesis as a replacement for regaining their gait functions after amputation. Microprocessor-based transfemoral prosthesis has gained significant importance in the last two decades for the rehabilitation of lower limb [...] Read more.
For amputees, amputation is a devastating experience. Transfemoral amputees require an artificial lower limb prosthesis as a replacement for regaining their gait functions after amputation. Microprocessor-based transfemoral prosthesis has gained significant importance in the last two decades for the rehabilitation of lower limb amputees by assisting them in performing activities of daily living. Commercially available microprocessor-based knee joints have the needed features but are costly, making them beyond the reach of most amputees. The excessive cost of these devices can be attributed to custom sensing and actuating mechanisms, which require significant development cost, making them beyond the reach of most amputees. This research contributes to developing a cost-effective microprocessor-based transfemoral prosthesis by integrating off-the-shelf sensing and actuating mechanisms. Accordingly, a three-level control architecture consisting of top, middle, and low-level controllers was developed for the proposed prosthesis. The top-level controller is responsible for identifying the amputee intent and mode of activity. The mid-level controller determines distinct phases in the activity mode, and the low-level controller was designed to modulate the damping across distinct phases. The developed prosthesis was evaluated on unilateral transfemoral amputees. Since off-the-shelf sensors and actuators are used in i-Inspire, various trials were conducted to evaluate the repeatability of the sensory data. Accordingly, the mean coefficients of correlation for knee angle, force, and inclination were computed at slow and medium walking speeds. The obtained values were, respectively, 0.982 and 0.946 for knee angle, 0.942 and 0.928 for knee force, and 0.825 and 0.758 for knee inclination. These results confirmed that the data are highly correlated with minimum covariance. Accordingly, the sensors provide reliable and repeatable data to the controller for mode detection and intent recognition. Furthermore, the knee angles at self-selected walking speeds were recorded, and it was observed that the i-Inspire Knee maintains a maximum flexion angle between 50° and 60°, which is in accordance with state-of-the-art microprocessor-based transfemoral prosthesis. Full article
(This article belongs to the Special Issue Advancements in Sensing Technologies and Control Mechanisms for Assistive Robotics: Enhancing Human–Robot Interaction and Assistance)
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