Publications
160 results found
Godden T, Mulvey B, Redgrave E, et al., 2024, PaTS-Wheel: A Passively-Transformable Single-part Wheel for Mobile Robot Navigation on Unstructured Terrain, IEEE Robotics and Automation Letters, ISSN: 2377-3766
Zhou H, Yang S, Halamek L, et al., 2023, A method to use haptic feedback of laryngoscope force vector for endotracheal intubation training, IEEE International Conference on Robotics and Automation : ICRA, Publisher: IEEE, Pages: 6810-6816, ISSN: 2152-4092
Endotracheal intubation is a mandatory competency for most medical staff. This procedure involves opening the entrance of the patient's upper windpipe using a laryngoscope and then inserting a tube into the windpipe to supply Oxygen to the patient. This time critical intervention requires careful control of the force vector on the tongue to lift it parallel to the jaw than to push the jaw to open the mouth. However, traditional intubation training methods in which novices practice intubation on prostheses lack haptic feedback to improve force control. We designed a sensorised intubation training phantom that can provide trainees with vibrotactile feedback reflecting the laryngoscope's force on the tongue. The critical component of this phantom is a silicon rubber tongue embedded with magnets and hall effect sensors. We calibrated the hall effect sensor readings to predict the force vector exerted on the tongue with errors less than 0.5 N in the lifting and pushing directions. We conducted a controlled experiment, mainly comparing the training results between participants with and without haptic feedback. Results show a statistically significant drop in the undesired forces due to haptic feedback, and the skill is retained when tested after 24 hours without haptic feedback.
Hauser H, Nanayakkara T, Forni F, 2023, Leveraging Morphological Computation for Controlling Soft Robots LEARNING FROM NATURE TO CONTROL SOFT ROBOTS, IEEE CONTROL SYSTEMS MAGAZINE, Vol: 43, Pages: 114-129, ISSN: 1066-033X
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- Citations: 3
Mulvey B, Lalitharatne TD, Nanayakkara T, 2023, DeforMoBot: a bio-inspired deformable mobile robot for navigation among obstacles, IEEE Robotics and Automation Letters, Vol: 8, Pages: 3828-3835, ISSN: 2377-3766
Many animals can move in cluttered environments by conforming their body shape to geometric constraints in their surroundings such as narrow gaps. Most robots are rigid structures and do not possess these capabilities. Navigation around movable or compliant obstacles results in a loss of efficiency—and possible mission failure—compared to progression through them. In this paper, we propose the novel design of a deformable mobile robot; it can adopt a wider stance for greater stability (and possible higher payload capacity), or a narrower stance to become capable of fitting through small gaps and progressing through flexible obstacles. We use a whisker-based feedback control approach in order to match the amount of the robot's deformation with the compliance level of the obstacle. We present a real-time algorithm which uses whisker feedback and performs shape adjustment in uncalibrated environments. The developed robot was tested navigating among obstacles with varying physical properties from different approach angles. Our results highlight the importance of co-development of environment perception and physical reaction capabilities for improved performance of mobile robots in unstructured environments.
Seminara L, Dosen S, Mastrogiovanni F, et al., 2023, A hierarchical sensorimotor control framework for human-in-the-loop robotic hands., Science Robotics, Vol: 8, Pages: 1-8, ISSN: 2470-9476
Human manual dexterity relies critically on touch. Robotic and prosthetic hands are much less dexterous and make little use of the many tactile sensors available. We propose a framework modeled on the hierarchical sensorimotor controllers of the nervous system to link sensing to action in human-in-the-loop, haptically enabled, artificial hands.
Yu Z, Sadati H, Perera S, et al., 2023, Tapered whisker reservoir computing for real-time terrain identification-based navigation, Scientific Reports, Vol: 13, Pages: 1-13, ISSN: 2045-2322
This paper proposes a new method for real-time terrain recognition-based navigation for mobile robots. Mobile robots performing tasks in unstructured environments need to adapt their trajectories in real-time to achieve safe and efficient navigation in complex terrains. However, current methods largely depend on visual and IMU (inertial measurement units) that demand high computational resources for real-time applications. In this paper, a real-time terrain identification-based navigation method is proposed using an on-board tapered whisker-based reservoir computing system. The nonlinear dynamic response of the tapered whisker was investigated in various analytical and Finite Element Analysis frameworks to demonstrate its reservoir computing capabilities. Numerical simulations and experiments were cross-checked with each other to verify that whisker sensors can separate different frequency signals directly in the time domain and demonstrate the computational superiority of the proposed system, and that different whisker axis locations and motion velocities provide variable dynamical response information. Terrain surface-following experiments demonstrated that our system could accurately identify changes in the terrain in real-time and adjust its trajectory to stay on specific terrain.
Labazanova L, Peng S, Qiu L, et al., 2023, Self-Reconfigurable Soft-Rigid Mobile Agent With Variable Stiffness and Adaptive Morphology, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 8, Pages: 1643-1650, ISSN: 2377-3766
He L, Maiolino P, Leong F, et al., 2023, Robotic simulators for tissue examination training with multimodal sensory feedback, IEEE Reviews in Biomedical Engineering, Vol: 16, Pages: 514-529, ISSN: 1941-1189
Tissue examination by hand remains an essential technique in clinical practice. The effective application depends on skills in sensorimotor coordination, mainly involving haptic, visual, and auditory feedback. The skills clinicians have to learn can be as subtle as regulating finger pressure with breathing, choosing palpation action, monitoring involuntary facial and vocal expressions in response to palpation, and using pain expressions both as a source of information and as a constraint on physical examination. Patient simulators can provide a safe learning platform to novice physicians before trying real patients. This paper reviews state-of-the-art medical simulators for the training for the first time with a consideration of providing multimodal feedback to learn as many manual examination techniques as possible. The study summarizes current advances in tissue examination training devices simulating different medical conditions and providing different types of feedback modalities. Opportunities with the development of pain expression, tissue modeling, actuation, and sensing are also analyzed to support the future design of effective tissue examination simulators.
Ranjan A, Angelini F, Nanayakkara T, et al., 2023, Design Guidelines for Bioinspired Adaptive Foot for Stable Interaction With the Environment, IEEE/ASME Transactions on Mechatronics, ISSN: 1083-4435
Robotic exploration in natural environments requires adaptable, resilient, and stable interactions with uncertain terrains. Most state-of-the-art legged robots utilize flat or ball feet that lack adaptability and are prone to slip due to point contact with the ground. In this article, we present guidelines to design an adaptive foot that can interact with the terrain to achieve a stable configuration. The foot is inspired by goat hoof anatomy that incorporates roll and yaw rotations in the Fetlock and Pastern joints, respectively. To ensure adaptability with stability in physical interaction and to prevent the foot from collapsing, we provide a lower bound on each joint's stiffness. In addition, we also render an upper bound to conform to the high force exchange during interactions with the ground consisting of certain roughness. Based on these guidelines, we design the hoof and experimentally validate the theoretical results with a loading test setup in lab settings. We use four different friction materials with various triangular, rectangular, and semicircular extrusions to simulate common ground features. We observe that hooved pads require more load for the system to be unstable. Any anatomically inspired foot can be designed based on the guidelines proved analytically and experimentally in this article.
Arachchige DDK, Varshney T, Huzaifa U, et al., 2023, Study on Soft Robotic Pinniped Locomotion, IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Publisher: IEEE, Pages: 65-71, ISSN: 2159-6255
Angelini F, Angelini P, Angiolini C, et al., 2023, Robotic Monitoring of Habitats: The Natural Intelligence Approach, IEEE ACCESS, Vol: 11, Pages: 72575-72591, ISSN: 2169-3536
He L, Herzig N, Nanayakkara T, et al., 2022, 3D-printed soft sensors for adaptive sensing with online and offline tunable-stiffness, Soft Robotics, Vol: 9, Pages: 1062-1073, ISSN: 2169-5172
The stiffness of a soft robot with structural cavities can be regulated by controlling the pressure of a fluid to render predictable changes in mechanical properties. When the soft robot interacts with the environment, the mediating fluid can also be considered an inherent information pathway for sensing. This approach to using structural tuning to improve the efficacy of a sensing task with specific states has not yet been well studied. A tunable stiffness soft sensor also renders task-relevant contact dynamics in soft robotic manipulation tasks. This article proposes a type of adaptive soft sensor that can be directly three-dimensional printed and controlled using pneumatic pressure. The tunability of such a sensor helps to adjust the sensing characteristics to better capturing specific tactile features, demonstrated by detecting texture with different frequencies. We present the design, modeling, Finite Element Simulation, and experimental characterization of a single unit of such a tunable stiffness sensor. How the sensing characteristics are affected by adjusting its stiffness is studied in depth. In addition to the tunability, the results show that such types of adaptive sensors exhibit good sensitivity (up to 2.6 KPa/N), high sensor repeatability (average std <0.008 KPa/N), low hysteresis (<6%), and good manufacturing repeatability (average std = 0.0662 KPa/N).
Leong F, Chow Yin L, Siamak Farajzadeh K, et al., 2022, A surrogate model based on a finite element model of abdomen for real-time visualisation of tissue stress during physical examination training, Bioengineering, Vol: 9, ISSN: 2306-5354
Robotic patients show great potential to improve medical palpation training as they can provide feedback that cannot be obtained in a real patient. Providing information about internal organs deformation can significantly enhance palpation training by giving medical trainees visual insight based on their finger behaviours. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, thus able to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real-time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANN) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 hrs to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that the ANN has a 92.6% accuracy. We also show that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has potential to be used as a training simulator for trainees to hone their palpation skills.
Carboni G, Nanayakkara T, Takagi A, et al., 2022, Adapting the visuo-haptic perception through muscle coactivation (vol 11, 21986, 2021), SCIENTIFIC REPORTS, Vol: 12, ISSN: 2045-2322
Zhang Qiu P, Yongxuan T, Thompson O, et al., 2022, Soft tissue characterisation using a novel robotic medical percussion device with acoustic analysis and neural networks, IEEE Robotics and Automation Letters, Vol: 7, Pages: 11314-11321, ISSN: 2377-3766
Medical percussion is a common manual examination procedure used by physicians to determine the state of underlying tissues from their acoustic responses. Although it has been used for centuries, there is a limited quantitative understanding of its dynamics, leading to subjectivity and a lack of detailed standardisation. This letter presents a novel compliant two-degree-of-freedom robotic device inspired by the human percussion action, and validates its performance in two tissue characterisation experiments. In Experiment 1, spectro-temporal analysis using 1-D Continuous Wavelet Transform (CWT) proved the potential of the device to identify hard nodules, mimicking lipomas, embedded in silicone phantoms representing a patient's abdominal region. In Experiment 2, Gaussian Mixture Modelling (GMM) and Neural Network (NN) predictive models were implemented to classify composite phantom tissues of varying density and thickness. The proposed device and methods showed up to 97.5% accuracy in the classification of phantoms, proving the potential of robotic solutions to standardise and improve the accuracy of percussion diagnostic procedures.
Mazzolai B, Mondini A, Del Dottore E, et al., 2022, Roadmap on soft robotics: multifunctionality, adaptability and growth without borders, Multifunctional Materials, Vol: 5
Soft robotics aims at creating systems with improved performance of movement and adaptability in unknown, challenging, environments and with higher level of safety during interactions with humans. This Roadmap on Soft Robotics covers selected aspects for the design of soft robots significantly linked to the area of multifunctional materials, as these are considered a fundamental component in the design of soft robots for an improvement of their peculiar abilities, such as morphing, adaptivity and growth. The roadmap includes different approaches for components and systems design, bioinspired materials, methodologies for building soft robots, strategies for the implementation and control of their functionalities and behavior, and examples of soft-bodied systems showing abilities across different environments. For each covered topic, the author(s) describe the current status and research directions, current and future challenges, and perspective advances in science and technology to meet the challenges.
Lalitharatne TD, Costi L, Hasheem R, et al., 2022, Face mediated human–robot interaction for remote medical examination, Scientific Reports, Vol: 12, ISSN: 2045-2322
Realtime visual feedback from consequences of actions is useful for future safety-critical human–robot interaction applications such as remote physical examination of patients. Given multiple formats to present visual feedback, using face as feedback for mediating human–robot interaction in remote examination remains understudied. Here we describe a face mediated human–robot interaction approach for remote palpation. It builds upon a robodoctor–robopatient platform where user can palpate on the robopatient to remotely control the robodoctor to diagnose a patient. A tactile sensor array mounted on the end effector of the robodoctor measures the haptic response of the patient under diagnosis and transfers it to the robopatient to render pain facial expressions in response to palpation forces. We compare this approach against a direct presentation of tactile sensor data in a visual tactile map. As feedback, the former has the advantage of recruiting advanced human capabilities to decode expressions on a human face whereas the later has the advantage of being able to present details such as intensity and spatial information of palpation. In a user study, we compare these two approaches in a teleoperated palpation task to find the hard nodule embedded in the remote abdominal phantom. We show that the face mediated human–robot interaction approach leads to statistically significant improvements in localizing the hard nodule without compromising the nodule position estimation time. We highlight the inherent power of facial expressions as communicative signals to enhance the utility and effectiveness of human–robot interaction in remote medical examinations.
Berkovic A, Laganier C, Chappell D, et al., 2022, A multi-modal haptic armband for finger-level sensory feedback from a prosthetic hand, EuroHaptics, Publisher: Springer
This paper presents the implementation and evaluation of three specific, yet complementary, mechanisms of haptic feedback—namely, normal displacement, tangential position, and vibration—to render, at a finger-level, aspects of touch and proprioception from a prosthetic hand without specialised sensors. This feedback is executed by an armband worn around the upper arm divided into five somatotopic modules, one per each finger. To evaluate the system, just-noticeable difference experiments for normal displacement and tangential position were carried out, validating that users are most sensitive to feedback from modules located on glabrous (hairless) skin regions of the upper arm. Moreover, users identifying finger-level contact using multi-modal feedback of vibration followed by normal displacement performed significantly better than those using vibration feedback alone, particularly when reporting exact combinations of fingers. Finally, the point of subjective equality of tangential position feedback was measured simultaneously for all modules, which showed promising results, but indicated that further development is required to achieve full finger-level position rendering.
Ge Y, Nanayakkara T, Dulantha Lalitharatne T, 2022, Origami inspired design for capsule endoscope to retrograde using intestinal peristalsis, IEEE Robotics and Automation Letters, Vol: 7, Pages: 5429-5435, ISSN: 2377-3766
Capsule endoscopy has gained a lot of attentionin the medical field in the recent past as an effective way ofinvestigating unusual symptoms experienced in places such asesophagus, stomach, small intestine and colon. However, motioncontrol of the capsule endoscope is challenging and often requiresa power source and miniature actuators. To address these issues,we present a novel origami inspired structure as an attachmentto the capsule endoscope. The proposed origami structure utilizesthe wave generated by peristalsis of the intestine to move itforward and backward. When the origami structure is folded, thecapsule endoscope is propelled forward by intestinal peristalsis.When the origami structure is unfolded, the intestinal peristalsissqueezes the origami structure to drive the capsule endoscope tomove in the opposite direction. Therefore, folding and unfoldingof the proposed origami structure would allow to control themovement direction of the capsule endoscope. In this paper, wepresent the design, simulations and experimental validation ofthe proposed origami structure.
Yu Z, Sadati SMH, Hauser H, et al., 2022, A Semi-Supervised Reservoir Computing System Based on Tapered Whisker for Mobile Robot Terrain Identification and Roughness Estimation, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 7, Pages: 5655-5662, ISSN: 2377-3766
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- Citations: 3
Yu Z, Perera S, Hauser H, et al., 2022, A tapered whisker-based physical reservoir computing system for mobile robot terrain identification in unstructured environments, IEEE Robotics and Automation Letters, Vol: 7, Pages: 3608-3615, ISSN: 2377-3766
In this letter, we present for the first time the use of tapered whisker-based reservoir computing (TWRC) system mounted on a mobile robot for terrain classification and roughness estimation of unknown terrain.Hall effect sensors captured the oscillations at different locations along a tapered spring that served as a reservoir to map time-domain vibrations signals caused by the interaction perturbations from the ground to frequency domain features directly. Three hall sensors are used to measure the whisker reservoir outputs and these temporal signals could be processed efficiently by the proposed TWRC system which can provide morphological computation power for data processing and reduce the model training cost compared to the convolutional neural network (CNN) approaches.To predict the unknown terrain properties, an extended TWRC method including a novel detector is proposed based on the Mahalanobis distance in the Eigen space, which has been experimentally demonstrated to be feasible and sufficiently accurate.We achieved a prediction success rate of 94.3\% for six terrain surface classification experiments and 88.7\% for roughness estimation of the unknown terrain surface.
Tan Y, Rerolle S, Lalitharathne TD, et al., 2022, Simulating dynamic facial expressions of pain from visuo-haptic interactions with a robotic patient, Scientific Reports, Vol: 12, ISSN: 2045-2322
Medical training simulators can provide a safe and controlled environment for medical students to practice their physical examination skills. An important source of information for physicians is the visual feedback of involuntary pain facial expressions in response to physical palpation on an affected area of a patient. However, most existing robotic medical training simulators that can capture physical examination behaviours in real-time cannot display facial expressions and comprise a limited range of patient identities in terms of ethnicity and gender. Together, these limitations restrict the utility of medical training simulators because they do not provide medical students with a representative sample of pain facial expressions and face identities, which could result in biased practices. Further, these limitations restrict the utility of such medical simulators to detect and correct early signs of bias in medical training. Here, for the first time, we present a robotic system that can simulate facial expressions of pain in response to palpations, displayed on a range of patient face identities. We use the unique approach of modelling dynamic pain facial expressions using a data-driven perception-based psychophysical method combined with the visuo-haptic inputs of users performing palpations on a robot medical simulator. Specifically, participants performed palpation actions on the abdomen phantom of a simulated patient, which triggered the real-time display of six pain-related facial Action Units (AUs) on a robotic face (MorphFace), each controlled by two pseudo randomly generated transient parameters: rate of change β and activation delay τ. Participants then rated the appropriateness of the facial expression displayed in response to their palpations on a 4-point scale from “strongly disagree” to “strongly agree”. Each participant (n=16, 4 Asian females, 4 Asian males, 4 White females and 4 White males) performed 200 palpation
Yu Z, SMHadi S, Hasitha W, et al., 2021, A method to use nonlinear dynamics in a whisker sensor for terrain identification by mobile robots, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE, Pages: 8437-8443
This paper shows analytical and experimental evidence of using the vibration dynamics of a compliant whisker for accurate terrain classification during steady state motion of a mobile robot. A Hall effect sensor was used to measure whisker vibrations due to perturbations from the ground. Analytical results predict that the whisker vibrations will have a dominant frequency at the vertical perturbation frequency of the mobile robot sandwiched by two other less dominant but distinct frequency components. These frequency components may come from bifurcation of vibration frequency due to nonlinear interaction dynamics at steady state. Experimental results also exhibit distinct dominant frequency components unique to the speed of the robot and the terrain roughness. This nonlinear dynamic feature is used in a deep multi-layer perceptron neural network to classify terrains. We achieved 85.6% prediction success rate for seven flat terrain surfaces with different textures.Index Terms— Robotic whiskers, Surface identification, multi-layer perceptron, Modal analysis.
Nanayakkara T, Barfoot T, Howard T, 2021, Robotics: Science and Systems (RSS) 2020, INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, Vol: 40, Pages: 1329-1330, ISSN: 0278-3649
Carboni G, Nanayakkara T, Takagi A, et al., 2021, Adapting the visuo-haptic perception through muscle coactivation, SCIENTIFIC REPORTS, Vol: 11, ISSN: 2045-2322
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- Citations: 4
He L, Leong F, Dulantha Lalitharatne T, et al., 2021, A haptic mouse design with stiffening muscle layer for simulating guarding in abdominal palpation training, IEEE International Conference on Robotics and Automation (ICRA 2021), Publisher: IEEE, Pages: 12588-12594, ISSN: 2152-4092
A patient would contract surface muscles as areaction called muscle guarding when experiencing discomfortand pain during physical palpation. This reaction carries impor-tant information about an affected location. Training physiciansto regulate palpation forces to elicit just enough muscle tensionis a challenge using real patients. Tunable stiffness mechanismsenabled by soft robotics can be effectively integrated intomedical simulator designs for effective clinical education. Inthis paper, we propose a controllable stiffness muscle layer tosimulate guarding for abdominal palpation training. Designswith soft, fine, and rigid granular jamming, stretchable andnon-stretchable layer jamming mechanisms were tested andevaluated as methods to create controllable stiffness muscle.User studies have been carried out on10naive participants todifferentiate the tense and relaxed abdomen with the proposedjamming mechanisms. Muscle samples made of ground coffee(fine granular jamming) and latex layers (stretchable layerjamming) show good usability in simulating abdomen withdifferent stiffness with at least 75% of the user data exhibitsmore than70%of decision accuracy for both tested palpationgestures (single finger and multiple fingers) after short pre-training.
Costi L, Scimeca L, Maiolino P, et al., 2021, Comparative analysis of model-based predictive shared control for delayed operation in object reaching and recognition tasks with tactile sensing, Frontiers in Robotics and AI, Vol: 8, ISSN: 2296-9144
Communication delay represents a fundamental challenge in telerobotics: on one hand, it compromises the stability of teleoperated robots, on the other hand, it decreases the user’s awareness of the designated task. In scientific literature, such a problem has been addressed both with statistical models and neural networks (NN) to perform sensor prediction, while keeping the user in full control of the robot’s motion. We propose shared control as a tool to compensate and mitigate the effects of communication delay. Shared control has been proven to enhance precision and speed in reaching and manipulation tasks, especially in the medical and surgical fields. We analyse the effects of added delay and propose a unilateral teleoperated leader-follower architecture that both implements a predictive system and shared control, in a 1-dimensional reaching and recognition task with haptic sensing. We propose four different control modalities of increasing autonomy: non-predictive human control (HC), predictive human control (PHC), (shared) predictive human-robot control (PHRC), and predictive robot control (PRC). When analyzing how the added delay affects the subjects’ performance, the results show that the HC is very sensitive to the delay: users are not able to stop at the desired position and trajectories exhibit wide oscillations. The degree of autonomy introduced is shown to be effective in decreasing the total time requested to accomplish the task. Furthermore, we provide a deep analysis of environmental interaction forces and performed trajectories. Overall, the shared control modality, PHRC, represents a good trade-off, having peak performance in accuracy and task time, a good reaching speed, and a moderate contact with the object of interest.
Scimeca L, Hughes J, He L, et al., 2021, Action augmentation of tactile perception for soft-body palpation, Soft Robotics, Vol: 9, ISSN: 2169-5180
Medical palpation is a diagnostic technique in which physicians use the sense of touch to manipulate the soft human tissue. This can be to enable the diagnosis of possibly life-threatening conditions, such as cancer. Palpation is still poorly understood because of the complex interaction dynamics between the practitioners’ hands and the soft human body. To understand this complex soft body interactions, we explore robotic palpation for the purpose of diagnosing the presence of abnormal inclusions, or tumours. Using a Bayesian framework for training and classification, we show that the for the exploration of soft bodies requires complex, multi-axis, palpation trajectories. We also find that this probabilistic approach is capable of rapidly searching the large action space of the robot. This work progresses ‘robotic’ palpation, and provides frameworks for understanding and exploiting soft body interactions.
He L, Herzig N, Lusignan SD, et al., 2021, An Abdominal phantom with tunable stiffness nodules and force sensing capability for palpation training, IEEE Transactions on Robotics, Vol: 37, Pages: 1051-1064, ISSN: 1552-3098
Robotic phantoms enable advanced physical examination training before using human patients. In this article, we present an abdominal phantom for palpation training with controllable stiffness liver nodules that can also sense palpation forces. The coupled sensing and actuation approach is achieved by pneumatic control of positive-granular jammed nodules for tunable stiffness. Soft sensing is done using the variation of internal pressure of the nodules under external forces. This article makes original contributions to extend the linear region of the neo-Hookean characteristic of the mechanical behavior of the nodules by 140% compared to no-jamming conditions and to propose a method using the organ level controllable nodules as sensors to estimate palpation position and force with a root-mean-square error of 4% and 6.5%, respectively. Compared to conventional soft sensors, the method allows the phantom to sense with no interference to the simulated physiological conditions when providing quantified feedback to trainees, and to enable training following current bare-hand examination protocols without the need to wear data gloves to collect data.
He L, Tan X, Suzumori K, et al., 2021, A method to 3D print a programmable continuum actuator with single material using internal constraint, Sensors and Actuators A: Physical, Vol: 324, ISSN: 0924-4247
Soft continuum robots require differential control of channel pressure across several modules to trace 3D trajectories at the tip. For current designs of such actuators, sheathing is required to prevent radial expansion when the chambers are pressurized. With the recent development of soft materials additive manufacturing, 3D printing has become a promising fabrication method for soft continuum robots. However, most current designs for continuum actuators are based on molding, which are not designed for 3D printing. This paper proposes an internal constraint-based soft continuum actuator for single material 3D printing, with tunable design parameters to render pre-defined motions. The internal constraint method maximizes the superiority of the rapid prototyping solution in terms of customizing the soft continuum actuators with high fabrication speed and design freedom. The internal constraints come in the form of internal beam elements that not only limit the undesired radial expansion (up to ∼14% of conventional design) but also allows the actuator to be pressurized at a higher driving pressure (up to ∼160%) and higher maximum bending angle (up to ∼320%) compared to conventional no-beam design. By tuning the design parameter q (determined by the number of constraint beams n per radial cross-section and the number of such sections k along the axial direction), we can render the actuator to the desired movement under specific driving pressure p. We show numerical simulation and hardware experiment results for a soft actuator to achieve specified bending and twisting motions following this design approach.
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