Robotic Sensor Research Articles

Superflexible Carbon Nanofibers for Multidimensional Complex Deformation Sensing in Soft Robots.

Soft robots can make complex motions or deformations due to their infinite freedom, which poses great challenges for monitoring their motion and position. While previous investigations of flexible sensing either focused on stretchable or compression deformations in one or two directions, the complex multidimensional deformations that occur on the surfaces of soft robots have been frequently overlooked. In this work, inspired by spider silk, superflexible carbon nanofibers with a bundled structure were biomimetically designed and fabricated using electrospinning technology and carbonization treatment. The fabricated fibers can be microscopically folded at 180° and can sustain multidimensional shrinkage deformation without microstructural damage during 200,000 times of repeated folding. In addition, the fibers process ultrasmall bending resistance that is two orders of magnitude lower than that of A4 paper and commercial conductive fibers, demonstrating excellent flexibility that is ideal for fabricating sensors in soft robots. Combining the study of origami techniques and mechanical simulations, the bending resistance of the fibers was found to have a step change in response to different deformation angles and radii. As a demonstration, a sensor based on this flexible carbon nanofiber successfully monitors the irregular shrinkage deformation of soft parts, showing great potential in applications of grasping, recognition, and perception. This work sheds light on the design of ultraflexible conductive carbon materials and provides an avenue for the extreme shape-morphing monitoring of soft robots.

Assessment of wearable robotics performance in patients with neurological conditions.

While wearable robotics is expanding within clinical settings, particularly for neurological rehabilitation, there is still a lack of consensus on how to effectively assess the performance of these devices. This review focuses on the most common metrics, whose selection and design are crucial for optimizing treatment outcomes and potentially improve the standard care. The literature reveals that while wearable robots are equipped with various embedded sensors, most studies still rely on traditional, nontechnological methods for assessment. Recent studies have shown that, although quantitative data from embedded sensors are available (e.g., kinematics), these are underutilized in favor of qualitative assessments. A trend toward integrating automatic assessments from the devices themselves is emerging, with a few notable studies pioneering this approach. Our analysis suggests a critical need for developing standardized metrics that leverage the data from embedded sensors in wearable robots. This shift could enhance the accuracy of patient assessments and the effectiveness of rehabilitation strategies, ultimately leading to better patient outcomes in neurological rehabilitation.

A flexible proximity-pressure–temperature tri-mode robotic sensor with stimulus discriminability, high sensitivity and wide perception range

Along with the explosive utilization of intelligent and bionic robotics, the rise of somatosensory system with excellent flexibility and multiple biological sensing characteristic emerges as a substantial crux of this domain. Herein, we propose a flexible high-performance multi-mode sensor for real-time proximity–pressure–temperature perception based on a monolithic sensing unit with fingerprint-like hierarchical architecture. The monolithic sensing unit, primarily constituted by a double-permeable ionic liquids/Multi-walled nanotubes conductive network, demonstrates dual-functionality in detecting pressure and temperature. Making use of the further synergy of rational topographical architecture engineering and feasible decoupling algorithm construction, extraordinary progress in sensing performances for both pressure and temperature are attained with negligible mutual interferences. Additionally, the sensor is capable of switching to touchless mode to detect objects at distance up to 200 mm, validating its remarkable proximity sensing ability. The multifunctional nature of sensor is further substantiated through its integration with a robotic hand, highlighting its practical applicability in advanced robotic systems.

Competencies for Human-Robot Collaboration in the Construction Industry – Academia’s Perspective

ABSTRACT This paper investigates the perceptions of Construction Engineering and Management instructors regarding the competencies for Human-Robot Collaboration (HRC) in construction to determine knowledge areas, skills, and abilities prioritized for facilitating effective collaboration between humans and robots in the industry. A two-round Delphi study was employed to evaluate the perceptions of construction instructors regarding HRC competencies. This study’s findings revealed that human-robot collaboration knowledge areas prioritized by the instructors include HRC ethics and regulation, robot anatomy and technical specifications, construction robot applications, sensors, and task planning. The instructors prioritized skills such as task planning, application of machine learning algorithms, safety management, human-robot interface proficiency, and effective communication. HRC abilities prioritized include decision-making, continuous learning, critical thinking, attention to detail, analytical aptitude, and adaptability. This research established the competencies prioritized by the instructors for implementing HRC in the construction industry and recommended future research directions.

Magnetic Micromotors with Spiky Gold Nanoshells as SERS Sensors for Thiram and Bacteria Detection.

Surface-enhanced Raman scattering (SERS) is widely used in all kinds of detection due to its ultrahigh sensitivity and selectivity. Micromotors, when used as SERS sensors, or the so-called "hotspots on the fly", can combine both controlled mobility and SERS sensing capacity, and are ideal for versatile in situ detection. In this work, mobile SERS sensors are successfully fabricated by growing gold nanospikes onto magnetic microsphere surfaces. These mobile micromotors can act as normal SERS sensors, characterized by the trace detection of thiram, a highly toxic fungicide. The detection limit can reach 0.1nM, as good as most other noble metal deposited substrates. With significant magnetic gradient forces, separation of pathogenic bacteria from bulk solution is achieved once these magnetic micromotors bind with bacterial cells. Manipulated propulsion of micromotors, on the other hand, enables them to approach and contact pathogenic bacterial cells on command and further acquire Raman spectra under a controlled degree of contact, a capability never seen with passive sensors. The robotic SERS sensors have demonstrated unique sensing characteristics with controlled manipulations along with discriminative detection between bacterial species.

A Soft, Fatigue‐free, and Self‐healable Ionic Elastomer via the Synergy of Skin‐like Assembly and Bouligand Structure

AbstractSoft ionic elastomers that are self‐healable, fatigue‐free, and environment‐tolerant are ideal structural and sensing materials for artificial prosthetics, soft electronics, and robotics to survive unpredictable service conditions. However, most synthetic strategies failed to unite rapid healing, fatigue resistance, and environmental robustness, limited by their singular compositional/structural designs. Here, we present a soft, tough, fatigue‐resistant, and self‐healable ionic elastomer (STFSI elastomer), which fuses skin‐like binary assembly and Bouligand helicoidal structure into a composite of thermoplastic polyurethane (TPU) fibers and a supramolecular ionic biopolymer. The interlocked binary assembly enables skin‐like softness, high stretchability, and strain‐adaptive stiffening through a matrix‐to‐scaffold stress transfer. The Bouligand structure contributes to superhigh fracture toughness (101.6 kJ m−2) and fatigue resistance (4937 J m−2) via mechanical toughening by interlayer slipping and twisted crack propagation path. Besides, the STFSI elastomer is self‐healable through a “bridging” method and environment‐tolerant (−20 °C, strong acid/alkali, saltwater). To demonstrate the versatile structural and sensing applications, we showcase a safety cushion with efficient damping and suppressed rebounding, and a robotic sensor with excellent fatigue crack tolerance and instant sensation recovery upon cutting‐off damage. Our presented synthetic strategy is generalizable to other fiber‐reinforced tough polymers for applications involving demanding mechanical/environmental conditions.

A Soft, Fatigue-free, and Self-healable Ionic Elastomer via the Synergy of Skin-like Assembly and Bouligand Structure.

Soft ionic elastomers that are self-healable, fatigue-free, and environment-tolerant are ideal structural and sensing materials for artificial prosthetics, soft electronics, and robotics to survive unpredictable service conditions. However, most synthetic strategies failed to unite rapid healing, fatigue resistance, and environmental robustness, limited by their singular compositional/structural designs. Here, we present a soft, tough, fatigue-resistant, and self-healable ionic elastomer (STFSI elastomer), which fuses skin-like binary assembly and Bouligand helicoidal structure into a composite of thermoplastic polyurethane (TPU) fibers and a supramolecular ionic biopolymer. The interlocked binary assembly enables skin-like softness, high stretchability, and strain-adaptive stiffening through a matrix-to-scaffold stress transfer. The Bouligand structure contributes to superhigh fracture toughness (101.6 kJ m-2) and fatigue resistance (4937 J m-2) via mechanical toughening by interlayer slipping and twisted crack propagation path. Besides, the STFSI elastomer is self-healable through a "bridging" method and environment-tolerant (-20 °C, strong acid/alkali, saltwater). To demonstrate the versatile structural and sensing applications, we showcase a safety cushion with efficient damping and suppressed rebounding, and a robotic sensor with excellent fatigue crack tolerance and instant sensation recovery upon cutting-off damage. Our presented synthetic strategy is generalizable to other fiber-reinforced tough polymers for applications involving demanding mechanical/environmental conditions.

Fish-inspired tracking of underwater turbulent plumes

Autonomous ocean-exploring vehicles have begun to take advantage of onboard sensor measurements of water properties such as salinity and temperature to locate oceanic features in real time. Such targeted sampling strategies enable more rapid study of ocean environments by actively steering towards areas of high scientific value. Inspired by the ability of aquatic animals to navigate via flow sensing, this work investigates hydrodynamic cues for accomplishing targeted sampling using a palm-sized robotic swimmer. As proof-of-concept analogy for tracking hydrothermal vent plumes in the ocean, the robot is tasked with locating the center of turbulent jet flows in a 13,000-liter water tank using data from onboard pressure sensors. To learn a navigation strategy, we first implemented RL on a simulated version of the robot navigating in proximity to turbulent jets. After training, the RL algorithm discovered an effective strategy for locating the jets by following transverse velocity gradients sensed by pressure sensors located on opposite sides of the robot. When implemented on the physical robot, this gradient following strategy enabled the robot to successfully locate the turbulent plumes at more than twice the rate of random searching. Additionally, we found that navigation performance improved as the distance between the pressure sensors increased, which can inform the design of distributed flow sensors in ocean robots. Our results demonstrate the effectiveness and limits of flow-based navigation for autonomously locating hydrodynamic features of interest.

Automated Tenderness Assessment of Okra Using Robotic Non-Destructive Sensing

The quality of okra is crucial in satisfying consumer expectations, and the tenderness of okra is an essential parameter in estimating its condition. However, the current methods for assessing okra tenderness are slow and prone to errors, necessitating the development of a better, non-destructive method. The objective of the present study is to develop and test a non-destructive robotic sensor for assessing okra freshness and tenderness. A total of 120 pods were divided into two sets and stored under different conditions: 60 pods were kept in a cold chamber for 24 h (considered tender), while the other 60 pods were stored at room temperature for two days. First, the samples were assessed non-destructively using the force sensor of a collaborative robot, where a jamming pad (with internal granular fill) was capable of adapting and copying the okra shapes while controlling its force deformation. Second, the okra pods were evaluated with the referenced destructive tests, as well as weight loss, compression, and puncture tests. In order to validate the differences in the tenderness of the two sets, a discriminant analysis was carried out to segregate the okra pods into the two categories according to the destructive variables, confirming the procedure which was followed to produce tender and non-tender okra pods. After the differences in the tenderness of the two sets were confirmed, the variables extracted from the robotic sensor (maximum force (Fmax), first slope (S1), second slope (S2), the first overshoot (Os), and the steady state (Ss)) were significant predictors for the separation in the two quality categories. Discriminant analysis and logistic regression methods were applied to classify the pods into the two tenderness categories. Promising results were obtained using neural network classification with 80% accuracy in predicting tenderness from the sensor data, and a 95.5% accuracy rate was achieved in distinguishing between tender and non-tender okra pods in the validation data set. The use of the robotic sensor could be an efficient tool in evaluating the quality of okra. This process may lead to substantial savings and waste reduction, particularly considering the elevated cost and challenges associated with transporting perishable vegetables.

Human-in-the-loop control of dynamics and robotics using augmented reality

Human-machine interaction (HMI) and human-robot interaction (HRI) offer innovative approaches for the analysis of structural dynamics both in laboratory and real-world scenarios. In vibratory experimentation, an external force is generated to evaluate dynamic responses of structures. One mode of generation is to use electrodynamic shakers. Sensors are attached to the structure to measure dynamic responses and can be deployed by robots. In post-disaster inspections robotic deployment ensures the safety of inspectors and requires control. If the interface between operators and the controls was augmented, then operators can visualize experiments, exciter levels, and define robot input while maintaining awareness of the area of interest. Robots can provide better aid to humans if control of the robot is: (1) quantified and presented to the human; and (2) conducted in real-time for human feedback. This research proposes using Augmented Reality (AR) to provide operators with sensor feedback and control for analysis of dynamic structures with robots. This method improves cognition by allowing the operator to maintain awareness of structures while adjusting conditions accordingly with the assistance of the new real-time interface. One interface application is developed to plot sensor data in addition to voltage, frequency, and duration controls for vibration simulations. Two more applications are developed following the same framework, one to control the position of a mediating robot and one to control the frequency of the robot's movement. The applications serve as a first-step approach investigating potential robotic sensor deployment in disaster assessment of structures. This paper presents the proposed model for the new vibratory control loop and then compares the new approach with a traditional method by measuring time delay in control input. The study also demonstrates that AR enhances time efficiency and accuracy in controlling robotic arm positions, matching actuator frequencies, and deploying sensors with minimal time delay.

Geometry-modulated all organic 3D printed smart PLA fibers for flextension amplified giant mechanical energy harvesting and Machine learning assisted pressure mapping

Piezoelectret sensors offer a promising avenue for harvesting abundant mechanical energy in the environment, providing a sustainable power solution for low-powered electronics. However, effectively harnessing large impact mechanical energy remains a challenge. In this study, we introduce a novel 3D cylindrical self-powered piezoelectret sensor capable of converting irregular axial impact forces into uniform radial pressures, enabling efficient large-scale impact mechanical energy harvesting. The cylindrical piezoelectret sensor, operating via a flextensional mechanism, was fabricated using 3D printed poly(lactic acid) (PLA) fibers as the piezoelectret layer and poly(3,4-ethylenedioxythiophene) (PEDOT)-coated PLA as the electrode layer. Our cylindrical sensor demonstrates the output voltage, and current of ∼ 11.4 V and 7.2 μA, representing remarkable improvement of 250 % and 160 % respectively, compared to flat fiber membrane device. Furthermore, we leverage IoT connectivity and machine learning techniques to utilize the fabricated robotic sensor for pressure mapping applications. Through machine learning algorithms, we achieve a remarkable accuracy of 100 % in character identification based on sensor data. This work highlights the potential of organic robotic sensors for efficient energy harvesting and intelligent pressure mapping applications, paving the way for the development of sustainable and adaptive sensor systems for various real-world scenarios.

Technology in Total Knee Arthroplasty in 2023

Over the past few decades, instrumentation and techniques for total knee arthroplasty have evolved from conventional manual tools to a wide range of technologies, including calibrated guides for accurate bone cuts and alignment, smart tools, dynamic intraoperative sensors for soft tissue balancing, patient-specific guides, computer navigation, and robotics. This review is intended to provide an overview of the latest advancements in total knee arthroplasty technology, address potential challenges and solutions related to the application of these technologies, and explore their limitations.

Event‐triggered resilient joint mobile robot localization and sensor fault estimation

SummaryThe event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL) problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated. From the external sensor to the estimator, to decrease the consumption of sensor energy and cut down network bandwidth resources, an event‐triggered scheme is considered. For the sake of characterizing the phenomenon induced by the inaccurate calculation of estimator gain, an uncertainty with the bounded second moment is employed. The purpose of this study is theoretically to find a feasible and effective approach to the addressed joint estimation problem such that the estimation error (EE) covariance (EEC) meets the given performance index. First, a minimum upper bound (UB) of the EEC is derived. Subsequently, in terms of the proposed joint estimation approach and the corresponding results obtained, an algorithm to address the resilient joint sensor fault estimation and MRLP is summarized. At the end, the effectiveness of the proposed algorithm is validated through conducting a set of comparisive experiments.

Building an Educational Automated Mechatronics-Based Sorting System

This paper discusses the development of an automated sorting machine designed as a comprehensive mechatronics educational project. The project integrates mechanical and electrical design, incorporating a robot arm, a microcontroller, sensors, and actuators. The sorting machine uses color identification to sort wooden blocks of three different colors. The blocks are stacked and dropped onto a conveyor belt by a hopper system that employs a solenoid actuator and a servo to release one block at a time at specific intervals. As the belt runs continuously, each block passes under a color sensor, which monitors the color and signals one of three servo-powered mechanical arms to guide the block into the appropriate chute. Each chute is equipped with a capacitive proximity sensor that sends a voltage signal to the robot controller, queuing commands for the robot to pick up the blocks from the bottom of each chute and return them to the hopper to form a continuously running sorting system. This paper details the design and integration of the system’s various elements and the development of the control software. The designed system can drop blocks every 8.05 s, sort each block within 5 s of being sensed, and return them to the sorting system every 12 s. It has a color-sensing accuracy of 97%, with a failure rate of around 7%. The system achieved quick and reliable sorting using various low-cost, accessible, and open-source parts. The project exemplifies a cost-effective solution suitable for mechatronics education, demonstrating the numerous challenges involved in developing automated sorting systems.

Kalman filter-based sensor fusion for Ackermann steering mobile robots

Abstract Autonomous navigation technologies are continuously improving in terms of performance and safety. A key subfield of autonomous navigation for mobile robots is localization or precise positioning, involving the robot understanding its position relative to key points of interest in the environment. Traditionally, localization is purely dependent on the quality of input received from onboard sensors and therefore localization quality degrades in the presence of high sensor error or other faults. Sensor fusion is useful to combat this. Sensor fusion is used to combine various data types from different sensors to gain a lower overall uncertainty or error in the resulting data, when compared to the separate data from each individual sensor. In this study a four-wheel mobile robot with front-wheel driving and steering is localized during navigation. The investigation is performed in simulation and with a physical prototype. The mobile robot has several sensors including a wheel encoder on each wheel, an inertial measurement unit as well as an indoor GPS system. A designed Kalman Filter called The Combined Kalman Filter has been designed to fuse the sensor input data in order to output the position data required to localize the robot in (X, Y) cartesian coordinates. This is completed in the presence of sensor bias error and noise with the output path compared against a pre-selected ground truth trajectory. The proposed filter is based on the Extended Kalman Filter to contend with the non-linear model arising from the four-wheel steerable robot with the steering system following the Ackermann steering condition. The robot model and sensors are simulated in MATLAB for a chosen ground truth trajectory with results highlighting any detected faults and final accurate position information. It was seen that the estimated filter output had high accuracy when compared with the ground truth. Compared with other investigations the proposed filter has reduced mean error and minimal deviations compared with the ground truth path. In addition, this filtering model can be used in general for the localization of other actuated steering vehicles and aims to widen the research in this field as the majority of research in this area leverages differential drive robots which require less complex modeling.

Distributed Adaptive Secure Consensus Tracking Control for Asynchronous Switching Nonlinear MASs Under Sensor Attacks and Actuator Faults

A distributed adaptive secure control algorithm is proposed for asynchronous switching nonlinear multi-agent systems (MASs) in pure-feedback form while considering the sensor attacks and actuator faults, which eliminates the effect of attack signals, actuator faults and uncertainties without knowing the sign of the control gain functions. Since the system states measured by the sensors are corrupted by the malicious attacker, only the post-attack system states can be obtained. Thus, an improved coordinate transformation is constructed by using the post-attack system states and a backstepping method based on the improved coordinate transformation is developed to solve the consensus tracking control problem of the researched systems under sensor attacks. The common Lyapunov function is designed to ensure the stability of all asynchronous switching subsystems of each agent. In the end, a simulation example is presented to illustrate the theoretical results’ validity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper deal with the distributed adaptive secure consensus tracking control problem for asynchronous switching nonlinear MASs, whose models can describe many phenomena in nature such as biological processes, robot team formation and sensor networks. It is extremely challenging to achieve the tracking control problem of the researched asynchronous switching nonlinear system in an unsafe network environment, where the unknown sensor attacks signals, actuator faults coefficients, and randomly switched non-affine nonlinearities exist together for the followers. Furthermore, the control design and stability analysis of the researched system is based on the common Lyapunov approach, which makes the developed approach more engineering oriented.

ABDALAMEER. DEVELOPMENT OF AUTONOMOUS MOBILE ROBOTS CONTROL SYSTEMS

The development of an efficient control system for mobile autonomous robots is explored. It delineates key stages in this process, starting with the necessity of precise trajectory planning followed by the creation of a mathematical model for controlling the robotic device. Subsequently, the implementation of navigation algorithms in the management system is emphasized. The importance of conducting comprehensive error analysis to address discrepancies during robot movement is underscored. Post the primary phase of control system creation, evaluating the cost-effectiveness and ensuring workplace safety are deemed crucial management steps. Localization emerges as a pivotal factor enabling the robot's effective autonomous movement relative to its target position. This, in turn, aids in orienting the robot within its external environment. Spatial information acquisition is underscored through the significance of traffic planning, cartography and specialized robotics sensors. Furthermore, the article delves into proprioceptive sensors, detailing their components, such as optical and electrical conductor sensors operating via the Hall effect discovered in 1879. It provides a formula to accurately ascertain conductivity state, involving parameters like hole and electron concentration, electron and hole mobility and elementary charge. In essence, the control system parallels the human brain, requiring a recognition system, action planning and execution functions. Navigation is identified as a critical element facilitating the robot's movement in three-dimensional space. The article integrates technical intricacies about proprioceptive sensors, highlighting their pivotal role within robot control systems.

Design and Optimization of Mechano transduction Sensors for effective analysis of proteins with Robotic interference

Méchano transduction sensors convert mechanical inputs into electrical signals, allowing robots to detect and interact with their environments in various bio-imaging applications. Current bio-sensor systems have some drawbacks that prevent them from being widely used in robotic applications. These include low sensitivity, short durability, and expensive manufacturing costs regarding picture prediction and categorization. To overcome these obstacles and improve robotic mechanotransduction sensors for efficient protein analysis, this work suggests Unique Sensor Fabrication Techniques (USFT). These methods enhance sensor performance in protein analysis while keeping costs low and scalability high via integrating complex micro- and nano-scale materials and architectures. In comparison to traditional sensor designs, comprehensive evaluations based on predefined parametric criteria show significant improvements in areas such as sensitivity, response time, and predictability in protein biomolecules. In addition, assessments of scalability and manufacturability point to the possibility of widespread use in robotic systems for protein categorization and prediction. In bioimaging applications, this study helps advance sensor technology for reliable and efficient robot-environment interaction.

A thermosensitive smart robotic self-powered sensor for material identification

A thermosensitive smart robotic self-powered sensor for material identification

Conditional generative model with skip-connection structure for low-light image enhancement

Vision-guided coal mine robots often encounter challenges in low-light environments, capturing images marked by poor visibility and significant loss of detail, which complicates the advancement of smart mine safety production. Despite various advancements, achieving high visibility and detailed images through robotic vision sensors remains a formidable task. This paper introduces a conditional generative model incorporating a skip-connection structure to address these issues. We present a novel approach utilizing conditional Generative Adversarial Networks (GNAs) aimed at improving the visibility of image content. This involves an encoder-decoder framework with skip-connections functioning as the generator, and the discriminator, both tailored to enhance image detail. In addition, we implement loss functions integrated with boundary equilibrium constraints to counter the issue of model collapse in conditional GANs, thereby enhancing training stability and image fidelity. Our experimental findings demonstrate that our model delivers competitive results against other state-of-the-art low-light image enhancement models, alongside a standardized training regimen that ensures rapid and consistent convergence.