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  • Journal article
    Yu X, Wu T, Nguyen T-TN, Ghajari Met al., 2022,

    Investigation of blast-induced cerebrospinal fluid cavitation: Insights from a simplified head surrogate

    , International Journal of Impact Engineering, Vol: 162, Pages: 104146-104146, ISSN: 0734-743X
  • Journal article
    Bahshwan M, Gee M, Nunn J, Myant CW, Reddyhoff Tet al., 2022,

    In situ observation of anisotropic tribological contact evolution in 316L steel formed by selective laser melting

    , Wear, Vol: 490-491, Pages: 1-12, ISSN: 0043-1648

    A consensus on the tribological performance of components by additive-versus conventional manufacturing has not been achieved; mainly because the tribological test set-ups thus far were not suited for investigating the underlying microstructure's influence on the tribological properties. As a result, utilization of additive manufacturing techniques, such as selective laser melting (SLM), for tribological applications remains questionable. Here, we investigate the anisotropic tribological response of SLM 316L stainless steel via in situ SEM reciprocating micro-scratch testing to highlight the microstructure's role. As-built 316L SLM specimens were compared against annealed wire-drawn 316L. We found that: (i) microgeometric conformity was the main driver for achieving steady-state friction, (ii) the anisotropic friction of the additively manufactured components is limited to the break-in and is caused by the lack of conformity, (iii) the cohesive bonds, whose strength is proportional to frictional forces, are stronger in the additively manufactured specimens likely due to the dislocation-dense, cellular structures, (iv) low Taylor-factor grains with large dimension stimulate microcutting in the form of long, thin sheets with serrated edges. These findings uncover some microstructurally driven tribological complexities when comparing additive to conventional manufacturing.

  • Journal article
    Caputo C, Cardin M-A, 2022,

    Analyzing real options and flexibility in engineering systems design using decision rules and deep reinforcement learning

    , Journal of Mechanical Design - Transactions of the ASME, Vol: 144, ISSN: 1050-0472

    Engineering systems provide essential services to society e.g., power generation,transportation. Their performance, however, is directly affected by their ability to cope withuncertainty, especially given the realities of climate change and pandemics. Standard designmethods often fail to recognize uncertainty in early conceptual activities, leading to rigidsystems that are vulnerable to change. Real Options and Flexibility in Design are importantparadigms to improve a system’s ability to adapt and respond to unforeseen conditions.Existing approaches to analyze flexibility, however, do not leverage sufficiently recentdevelopments in machine learning enabling deeper exploration of the computational designspace. There is untapped potential for new solutions that are not readily accessible usingexisting methods. Here, a novel approach to analyze flexibility is proposed based on DeepReinforcement Learning (DRL). It explores available datasets systematically and considers awider range of adaptability strategies. The methodology is evaluated on an example waste-toenergy system. Low and high flexibility DRL models are compared against stochasticallyoptimal inflexible and flexible solutions using decision rules. The results show highly dynamicsolutions, with action space parametrized via artificial neural network. They show improvedexpected economic value up to 69% compared to previous solutions. Combining informationfrom action space probability distributions along expert insights and risk tolerance helps makebetter decisions in real-world design and system operations. Out of sample testing shows thatthe policies are generalizable, but subject to tradeoffs between flexibility and inherentlimitations of the learning process.

  • Journal article
    AlAttar A, Chappell D, Kormushev P, 2022,

    Kinematic-model-free predictive control for robotic manipulator target reaching with obstacle avoidance

    , Frontiers in Robotics and AI, ISSN: 2296-9144

    Model predictive control is a widely used optimal control method for robot path planning andobstacle avoidance. This control method, however, requires a system model to optimize controlover a finite time horizon and possible trajectories. Certain types of robots, such as softrobots, continuum robots, and transforming robots, can be challenging to model, especiallyin unstructured or unknown environments. Kinematic-model-free control can overcome thesechallenges by learning local linear models online. This paper presents a novel perception-basedrobot motion controller, the kinematic-model-free predictive controller, that is capable of controllingrobot manipulators without any prior knowledge of the robot’s kinematic structure and dynamicparameters and is able to perform end-effector obstacle avoidance. Simulations and physicalexperiments were conducted to demonstrate the ability and adaptability of the controller toperform simultaneous target reaching and obstacle avoidance.

  • Journal article
    Cursi F, Bai W, Yeatman EM, Kormushev Pet al., 2022,

    GlobDesOpt: a global optimization framework for optimal robot manipulator design

    , IEEE Access, Vol: 10, Pages: 5012-5023, ISSN: 2169-3536

    Robot design is a major component in robotics, as it allows building robots capable of performing properly in given tasks. However, designing a robot with multiple types of parameters and constraints and defining an optimization function analytically for the robot design problem may be intractable or even impossible. Therefore black-box optimization approaches are generally preferred. In this work we propose GlobDesOpt, a simple-to-use open-source optimization framework for robot design based on global optimization methods. The framework allows selecting various design parameters and optimizing for both single and dual-arm robots. The functionalities of the framework are shown here to optimally design a dual-arm surgical robot, comparing the different two optimization strategies.

  • Journal article
    Wang K, Fei H, Kormushev P, 2022,

    Fast online optimization for terrain-blind bipedal robot walking with a decoupled actuated SLIP model

    , Frontiers in Robotics and AI, ISSN: 2296-9144

    We present an online optimization algorithm which enables bipedal robots to blindly walk overvarious kinds of uneven terrains while resisting pushes. The proposed optimization algorithmperforms high level motion planning of footstep locations and center-of-mass height variationsusing the decoupled actuated Spring Loaded Inverted Pendulum (aSLIP) model. The decoupledaSLIP model simplifies the original aSLIP with Linear Inverted Pendulum (LIP) dynamics inhorizontal states and spring dynamics in the vertical state. The motion planning can beformulated as a discrete-time Model Predictive Control (MPC) problem and solved at a frequencyof 1 kHz. The output of the motion planner is fed into an inverse-dynamics based whole bodycontroller for execution on the robot. A key result of this controller is that the feet of the robot arecompliant, which further extends the robot’s ability to be robust to unobserved terrain variations.We evaluate our method in simulation with the bipedal robot SLIDER. Results show the robotcan blindly walk over various uneven terrains including slopes, wave fields and stairs. It can alsoresist pushes of up to 40 N for a duration of 0.1 s while walking on uneven terrain.

  • Journal article
    Steinhardt M, Barreras JV, Ruan H, Wu B, Offer GJ, Jossen Aet al., 2022,

    Meta-analysis of experimental results for heat capacity and thermal conductivity in lithium-ion batteries: A critical review

    , Journal of Power Sources, Vol: 522, Pages: 230829-230829, ISSN: 0378-7753
  • Journal article
    Abdin AF, Caunhye A, Zio E, Cardin M-Aet al., 2022,

    Optimizing generation expansion planning with operational uncertainty: A multistage adaptive robust approach

    , Applied Energy, Vol: 306, Pages: 1-18, ISSN: 0306-2619

    This paper presents a multistage adaptive robust generation expansion planning model, which accounts for short-term unit commitment and ramping constraints, considers multi-period and multi-regional planning, and maintains the integer representation of generation units. The uncertainty of electricity demand and renewable power generation is taken into account through bounded intervals, with parameters that permit control over the level of conservatism of the solution. The multistage robust optimization model allows the sequential representation of uncertainty realization as they are revealed over time. It also guarantees the non-anticipativity of future uncertainty realizations at the time of decision-making, which is the case in practical real-world applications, as opposed to two-stage robust and stochastic models. To render the resulting multistage robust problem tractable, decision rules are employed to cast the uncertainty-based model into an equivalent mixed integer linear (MILP) problem. The re-formulated MILP problem, while tractable, is computationally prohibitive even for moderately sized systems. We, thus, propose a solution method relying on the reduction of the information basis of the decision rules employed in the model, and validate its adequacy to efficiently solve the problem. The importance of considering multistage robust frameworks for accounting for net-load uncertainties in generation expansion planning is illustrated, particularly under a high share of renewable energy penetration. A number of renewable penetration scenarios and uncertainty levels are considered for a case study covering future generation expansion planning in Europe. The results confirm the effectiveness of the proposed approach in coping with multifold operational uncertainties and for deriving adequate generation investment decisions. Moreover, the quality of the solutions obtained and the computational performance of the proposed solution method is shown to be suitable fo

  • Journal article
    Yu Z, Perera S, Hauser H, Childs P, Nanayakkara Tet al., 2022,

    A Tapered Whisker-Based Physical Reservoir Computing System for Mobile Robot Terrain Identification in Unstructured Environments

    , IEEE Robotics and Automation Letters

    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.

  • Journal article
    Ruan H, Sun B, Cruden A, Zhu T, Jiang J, He X, Su X, Ghoniem Eet al., 2022,

    Optimal external heating resistance enabling rapid compound self-heating for lithium-ion batteries at low temperatures

    , APPLIED THERMAL ENGINEERING, Vol: 200, ISSN: 1359-4311
  • Journal article
    Posirisuk P, Baker C, Ghajari M, 2022,

    Computational prediction of head-ground impact kinematics in e-scooter falls

    , Accident Analysis and Prevention, ISSN: 0001-4575

    E-scooters are the fastest growing mode of micro-mobility with important environmental benefits. However, there are serious concerns about injuries caused by e-scooter accidents. Falls due to poor road surface conditions are a common cause of injury in e-scooter riders, and head injuries are one of the most common and concerning injuries in e-scooter falls. However, the head-ground impact biomechanics in e-scooter falls and its relationship with e-scooter speed and design, road surface conditions and wearing helmets remain poorly understood. To address some of these key questions, we predicted the head-ground impact force and velocity of e-scooter riders in different falls caused by potholes. We used multi-body dynamics approach to model a commercially available e-scooter and simulate 180 falls using human body models. We modelled different pothole sizes to test whether the pothole width and depth influences the onset of falls and head-ground impact speed and force. We also tested whether the e-scooter travelling speed has an influence on the head-ground impact force and velocity. The simulations were carried out with three human body models to ensure that the results of the study are inclusive of a wide range of rider sizes. For our 10inch diameter e-scooter wheels, we found a sudden increase in the occurrence of falls when the pothole depth was increased from 3cm (no falls) to 6cm (41 falls out of 60 cases). When the falls occurred, we found a head-ground impact force of 13.23.4kN, which is larger than skull fracture thresholds. The head-ground impact speed was 6.31.4m/s, which is nearly the same as the impact speed prescribed in bicycle helmet standards. All e-scooter falls resulted in oblique head impacts, with an impact angle of 6510 (measured from the ground). Decreasing the e-scooter speed reduced the head impact speed. For instance, reducing the e-scooter speed from 30km/h to 20km/h led to a 14% reduction in the mean impact speed and 12% reduction in th

  • Journal article
    Usseglio-Viretta FLE, Patel P, Bernhardt E, Mistry A, Mukherjee PP, Allen J, Cooper SJ, Laurencin J, Smith Ket al., 2022,

    MATBOX: An Open-source Microstructure Analysis Toolbox for microstructure generation, segmentation, characterization, visualization, correlation, and meshing

    , SoftwareX, Vol: 17

    MATBOX is an easy-to-use, all-in-one, MATLAB application for microstructure numerical analysis, including microstructure numerical generation, image filtering and microstructure segmentation, microstructure characterization, three-dimensional visualization, microstructure parameters correlation, and microstructure meshing. MATBOX was originally developed to analyse electrode microstructures for lithium-ion batteries; however, the algorithms provided by the toolbox are widely applicable to other heterogeneous materials. The toolbox provides a user-friendly experience thanks to a Graphic-User Interface.

  • Journal article
    Zhao Y, Pawlak J, Sivakumar A, 2022,

    Theory for socio-demographic enrichment performance using the inverse discrete choice modelling approach

    , Transportation Research Part B: Methodological: an international journal, Vol: 155, Pages: 101-134, ISSN: 0191-2615

    In light of the growing availability of big data sources and the essential role of socio-demographic information in travel behaviour and transport demand modelling more broadly, the enrichment of socio-demographic attributes for anonymous big datasets is a key issue that continues to be explored. The common shortcoming of existing socio-demographic enrichment approaches concerns their lack of consistent theory that can link their enrichment performance (i.e. the ability to correctly enrich the required attribute) to the underlying covariance structure in the anonymous big datasets. In other words, existing approaches are unable to indicate, prior to the enrichment, to what extent it will be successful. Instead, they require undertaking the enrichment itself to assess and validate it post factum, incurring the effort and cost of the activity. An alternative and arguably preferable way would be to have a prior indicator as to whether an enrichment is likely to be sufficiently effective for the desired application.Towards this end, this paper draws upon the Inverse Discrete Choice Modelling (IDCM) approach to demonstrate what is termed as the IDCM performance theory, which systematically and in a tractable manner links the socio-demographic enrichment performance of the IDCM approach to the structure of the underlying datasets. This is achieved by recalibration of the constant, a technique adopted from conventional discrete choice modelling practice, while also drawing upon information theory employed in the context of communication systems. The established IDCM performance theory is validated in two empirical applications where performance of the IDCM approach in enriching several socio-demographic attributes, given travel behaviour patterns, is successfully estimated. Additionally, the IDCM approach is found to perform comparably to commonly used methods in previous socio-demographic enrichment efforts. It is thus argued that the capability of the IDCM performance th

  • Journal article
    Ghosh R, Marecek J, Griggs WM, Souza M, Shorten RNet al., 2022,

    Predictability and fairness in social sensing

    , IEEE Internet of Things Journal, Vol: 9, Pages: 37-54, ISSN: 2327-4662

    We consider the design of distributed algorithms that govern the manner in which agents contribute to a social sensing platform. Specifically, we are interested in situations, where fairness among the agents contributing to the platform is needed. A notable example is the platforms operated by public bodies, where fairness is a legal requirement. The design of such distributed systems is challenging due to the fact that we wish to simultaneously realize an efficient social sensing platform but also deliver a predefined quality of service to the agents (for example, a fair opportunity to contribute to the platform). In this article, we introduce iterated function systems (IFSs) as a tool for the design and analysis of systems of this kind. We show how the IFS framework can be used to realize systems that deliver a predictable quality of service to agents, can be used to underpin contracts governing the interaction of agents with the social sensing platform, and which are efficient. To illustrate our design via a use case, we consider a large, high-density network of participating parked vehicles. When awoken by an administrative center, this network proceeds to search for moving missing entities of interest using RFID-based techniques. We regulate which vehicles are actively searching for the moving entity of interest at any point in time. In doing so, we seek to equalize vehicular energy consumption across the network. This is illustrated through simulations of a search for a missing Alzheimer’s patient in Melbourne, Australia. The experimental results are presented to illustrate the efficacy of our system and the predictability of access of agents to the platform independent of initial conditions.

  • Journal article
    Chen J, Naylor Marlow M, Jiang Q, Wu Bet al., 2022,

    Peak-tracking method to quantify degradation modes in lithium-ion batteries via differential voltage and incremental capacity

    , Journal of Energy Storage, Vol: 45, Pages: 1-12, ISSN: 2352-152X

    Incremental capacity (IC) and differential voltage (DV) analyses are effective for monitoring battery health, however, the diagnosis often requires considerable parameterisation efforts and a low scan rate. In this work, a simple-to-parameterise quantitative diagnostic approach is presented, which differentiates between loss of lithium inventory and loss of active materials in the anode and cathode. With an open-circuit voltage model and a genetic algorithm optimisation routine, peak signatures in voltage and capacity differentials are used to quantify degradation modes as opposed to traditional approaches of matching the whole voltage and capacity spectra. The outputs are validated with synthetic IC-DV spectra and achieve a low root-mean-square error of ± 2.0 %. A similar level of accuracy is achieved when heterogeneity is introduced in the synthetic degradation data and also with partial discharge data. Experiments from pouch cells under 5 C discharge and 0.3 C charge cycling at 25 °C and 45 °C, together with post-mortem measurements, confirm the accuracy of this approach with diagnosis scan taken at 0.3 C. The IC-DV peak-tracking quantitative diagnostic code demonstrates a reliable and easy-to-implement means of extracting deeper insights into battery degradation and is shared alongside this manuscript to help academia and industry develop better lifetime predictions.

  • Journal article
    Zhao S, Haskell WB, Cardin M-A, 2021,

    A flexible system design approach for multi-facility capacity expansion problems with risk aversion

    , IISE Transactions, ISSN: 2472-5854

    This paper studies a model for risk aversion when designing a flexible capacity expansion plan for a multi-facility system. In this setting, the decision maker can dynamically expand the capacity of each facility given observations of uncertain demand. We model this situation as a multi-stage stochastic programming problem, and we express risk aversion through the conditional value-at-risk (CVaR) and a mean-CVaR objective. We optimize the multi-stage problem over a tractable family of if–then decision rules using a decomposition algorithm. This algorithm decomposes the stochastic program over scenarios and updates the solutions via the subgradients of the function of cumulative future costs. To illustrate the practical effectiveness of this method, we present a numerical study of a decentralized waste-to-energy system in Singapore. The simulation results show that the risk-averse model can improve the tail risk of investment losses by adjusting the weight factors of the mean-CVaR objective. The simulations also demonstrate that the proposed algorithm can converge to high-performance policies within a reasonable time, and that it is also more scalable than existing flexible design approaches.

  • Conference paper
    Tissera S, Jedwab R, Calvo R, Dobroff N, Glozier N, Hutchinson A, Leiter M, Manias E, Nankervis K, Rawson H, Redley Bet al., 2021,

    Older Nurses' Perceptions of an Electronic Medical Record Implementation

    , Pages: 516-521, ISSN: 0926-9630

    In Australia, almost 40% of nurses are aged 50 years and older. These nurses may be vulnerable to leaving the workforce due to challenges experienced during electronic medical record (EMR) implementations. This research explored older nurses' perceptions of factors expected to influence their adoption of an EMR, to inform recommendations to support implementation. The objectives were to: 1) measure psychological factors expected to influence older nurses' adoption of the EMR; and 2) explore older nurses' perceptions of facilitators and barriers to EMR adoption. An explanatory sequential mixed methods design was used to collect survey and focus group data from older nurses, prior to introducing an EMR system. These nurses were highly engaged with their work; 79.3% reported high wellbeing scores. However, their motivation appeared to be predominantly governed by external rather than internal influences. Themes reflecting barriers to EMR and resistance to adoption emerged in the qualitative data.

  • Conference paper
    Njane A, Jedwab R, Calvo R, Dobroff N, Glozier N, Hutchinson A, Leiter M, Manias E, Nankervis K, Rawson H, Redley Bet al., 2021,

    Perioperative Nurses' Perceptions Pre-Implementation of an Electronic Medical Record System

    , Pages: 522-524, ISSN: 0926-9630

    The use of electronic medical record (EMR) systems is transforming health care delivery in hospitals. Perioperative nurses work in a unique high-risk health setting, hence require specific considerations for EMR implementation. This research explored perioperative nurses' perceptions of facilitators and barriers to the implementation of an EMR in their workplace to make context-specific recommendations about strategies to optimise EMR adoption. Using a qualitative exploratory descriptive design, focus group data were collected from 27 perioperative nurses across three hospital sites. Thematic analyses revealed three themes: 1) The world is going to change; 2) What does it mean for me? and 3) We can do it, but we have some reservations. Mapping coded data to the Theoretical Domains Framework identified prominent facilitators and barriers, and informed recommended implementation strategies for EMR adoption by perioperative nurses.

  • Conference paper
    Osajiuba SA, Jedwab R, Calvo R, Dobroff N, Glozier N, Hutchinson A, Leiter M, Nankervis K, Rawson H, Redley B, Manias Eet al., 2021,

    Facilitators and Barriers to the Adoption of an Electronic Medical Record System by Intensive Care Nurses

    , Pages: 510-515, ISSN: 0926-9630

    Introducing new technology, such as an electronic medical record (EMR) into an Intensive Care Unit (ICU), can contribute to nurses' stress and negative consequences for patient safety. The aim of this study was to explore ICU nurses' perceptions of factors expected to influence their adoption of an EMR in their workplace. The objectives were to: 1) measure psychological factors expected to influence ICU nurses' adoption of EMR, and 2) explore perceptions of facilitators and barriers to the implementation of an EMR in their workplace. Using an explanatory sequential mixed method approach, data were collected using surveys and focus groups. ICU nurses reported high scores for motivation, work engagement and wellbeing. Focus group analyses revealed two themes: Hope the EMR will bring a new world and Fear of unintended consequences. Recommendations relate to strategies for education and training, environmental restructuring and enablement. Overall, ICU nurses were optimistic about EMR implementation.

  • Journal article
    Engel Alonso Martinez J, Goodman D, Picinali L, 2021,

    Assessing HRTF preprocessing methods for Ambisonics rendering through perceptual models

    , Acta Acustica -Peking-, ISSN: 0371-0025

    Binaural rendering of Ambisonics signals is a common way to reproduce spatial audio content. Processing Ambisonics signals at low spatial orders is desirable in order to reduce complexity, although it may degrade the perceived quality, in part due to the mismatch that occurs when a low-order Ambisonics signal is paired with a spatially dense head-related transfer function (HRTF). In order to alleviate this issue, the HRTF may be preprocessed so its spatial order is reduced. Several preprocessing methods have been proposed, but they have not been thoroughly compared yet. In this study, nine HRTF preprocessing methods were used to render anechoic binaural signals from Ambisonics representations of orders 1 to 44, and these were compared through perceptual hearing models in terms of localisation performance, externalisation and speech reception. This assessment was supported by numerical analyses of HRTF interpolation errors, interaural differences, perceptually-relevant spectral differences, and loudness stability. Models predicted that the binaural renderings’ accuracy increased with spatial order, as expected. A notable effect of the preprocessing method was observed: whereas all methods performed similarly at the highest spatial orders, some were considerably better at lower orders. A newly proposed method, BiMagLS, displayed the best performance overall and is recommended for the rendering of bilateral Ambisonics signals. The results, which were in line with previous literature, indirectly validate the perceptual models’ ability to predict listeners’ responses in a consistent and explicable manner.

  • Journal article
    Sethi SS, Ewers RM, Jones NS, Sleutel J, Shabrani A, Zulkifli N, Picinali Let al., 2021,

    Soundscapes predict species occurrence in tropical forests

    , OIKOS, Pages: 1-9, ISSN: 0030-1299

    Accurate occurrence data is necessary for the conservation of keystone or endangered species, but acquiring it is usually slow, laborious and costly. Automated acoustic monitoring offers a scalable alternative to manual surveys but identifying species vocalisations requires large manually annotated training datasets, and is not always possible (e.g. for lesser studied or silent species). A new approach is needed that rapidly predicts species occurrence using smaller and more coarsely labelled audio datasets. We investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species in 20 min recordings across a tropical forest degradation gradient in Sabah, Malaysia. Using acoustic features derived from a convolutional neural network (CNN), we characterised species indicative soundscapes by training our models on a temporally coarse labelled point-count dataset. Soundscapes successfully predicted the occurrence of 34 out of the 39 species across the two taxonomic groups, with area under the curve (AUC) metrics from 0.53 up to 0.87. The highest accuracies were achieved for species with strong temporal occurrence patterns. Soundscapes were a better predictor of species occurrence than above-ground carbon density – a metric often used to quantify habitat quality across forest degradation gradients. Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species and provide a new direction for data driven large-scale assessments of habitat suitability.

  • Journal article
    Thompson O, Mandalari AM, Haddadi H, 2021,

    Rapid IoT device identification at the edge

    , Proceedings of the 2nd ACM International Workshop on Distributed Machine Learning
  • Journal article
    He L, Herzig N, Nanayakkara T, Maiolino Pet al., 2021,

    3D-printed soft sensors for adaptive sensing with online and offline tunable-stiffness

    , Soft Robotics, ISSN: 2169-5172

    he 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 paper proposes a type of adaptive soft sensor that can be directly 3D 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, modelling, Finite Element Simulation, and experimental characterisation 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 additional to the tunability, the results show such type 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]).

  • Journal article
    Lhachemi H, Prieur C, Shorten R, 2021,

    In-Domain Stabilization of Block Diagonal Infinite-Dimensional Systems With Time-Varying Input Delays

    , IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 66, Pages: 6017-6024, ISSN: 0018-9286
  • Journal article
    Butt H, Nissim L, Gao L, Myant C, de Boer G, Hewson Ret al., 2021,

    Transient mixed lubrication model of the human knee implant

    , Biosurface and Biotribology, Vol: 7, Pages: 206-218

    The human knee implant is computationally modelled in the mixed lubrication regime to investigate the tribological performance of the implant. This model includes the complex geometry of the implant components, unlike elliptical contact models that approximate this geometry. Film thickness and pressure results are presented for an ISO gait cycle to determine the lubrication regime present within the implant during its operation. It was found that it was possible for the lubrication regime to span between elastohydrodynamic, mixed and boundary lubrication depending on the operating conditions of the implant. It was observed that the tribological conditions present in one condyle were not necessarily representative of the other. Multiple points of contact were found within the same condyle, which cannot be computed by the elliptical contact solvers. This model can be used to balance forces in all directions, instead of only the normal loads, as often done in elliptical contact models. This work is an initial step towards understanding the role of the complex geometry in the tribological characteristics of the human knee implant when operating in physiological conditions.

  • Journal article
    Lu Q, Baron N, Clark AB, Rojas Net al., 2021,

    Systematic object-invariant in-hand manipulation via reconfigurable underactuatuation: introducing the RUTH gripper

    , International Journal of Robotics Research, Vol: 40, Pages: 1402-1418, ISSN: 0278-3649

    We introduce a reconfigurable underactuated robot hand able to perform systematic prehensile in-hand manipulations regardless of object size or shape. The hand utilises a two-degree-of-freedom five-bar linkage as the palm of the gripper, with three three-phalanx underactuated fingers—jointly controlled by a single actuator—connected to the mobile revolute joints of the palm. Three actuators are used in the robot hand system in total, one for controlling the force exerted on objects by the fingers through an underactuated tendon system, and two for changing the configuration of the palm and thus the positioning of the fingers. This novel layout allows decoupling grasping and manipulation, facilitating the planning and execution of in-hand manipulation operations. The reconfigurable palm provides the hand with a large grasping versatility, and allows easy computation of a map between task space and joint space for manipulation based on distance-based linkage kinematics. The motion of objects of different sizes and shapes from one pose to another is then straightforward and systematic, provided the objects are kept grasped.This is guaranteed independently and passively by the underactuated fingers using a custom tendon routing method, which allows no tendon length variation when the relative finger base positions change with palm reconfigurations. We analyse the theoretical grasping workspace and grasping and manipulation capability of the hand, present algorithms forcomputing the manipulation map and in-hand manipulation planning, and evaluate all these experimentally. Numericaland empirical results of several manipulation trajectories with objects of different size and shape clearly demonstrate the viability of the proposed concept.

  • Journal article
    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
  • Conference paper
    Cursi F, Chappell D, Kormushev P, 2021,

    Augmenting Loss Functions of Feedforward Neural Networks with Differential Relationships for Robot Kinematic Modelling

    , Ljubljana, Slovenia
  • Journal article
    Nissim L, Butt H, Gao L, Myant C, Hewson Ret al., 2021,

    Role of protein concentration on transient film thickness in synovial fluid lubricated joints

    , Biotribology, Vol: 28, Pages: 1-14, ISSN: 2352-5738

    A computational model of protein aggregation lubrication has been developed for predicting transient behaviour in lubricated prosthetics. The model uses an advection-diffusion equation to simulate protein transport in order to map concentration changes throughout the contact and inlet zones of an elasto-hydrodynamic contact. Concentration increases lead to exponential increase in fluid viscosity giving rise to lubricating film thicknesses an order of magnitude larger than would be expected using conventional elasto-hydrodynamic theory. The model parameters have been calibrated such that good agreement in transient film thickness is achieved with observed experimental results.KeywordsProtein aggregation lubrication; Elasto-hydrodynamic lubrication; Prostheses

  • Conference paper
    La Barbera V, Pardo F, Tassa Y, Daley M, Richards C, Kormushev P, Hutchinson Jet al., 2021,

    OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion

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