Imperial College London

Dr Nicolas Rojas

Faculty of EngineeringDyson School of Design Engineering

Senior Lecturer
 
 
 
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Contact

 

n.rojas

 
 
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Location

 

Dyson BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
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74 results found

Clark A, Baron N, Orr L, Kovac M, Rojas Net al., 2022, On a balanced delta robot for precise aerial manipulation: implementation, testing, and lessons for future designs, IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE

Using a delta-manipulator for stabilisation of anend-effector to perform precise spatial positioning is a currentarea of interest in aerial manipulation. High speed precisionmovements of a manipulator can cause disturbances to theaerial platform, which hinders trajectory tracking and in somecases could be sufficient to cause a loss of control of the vehicle.In this paper, a statically balanced delta aerial manipulator isdeveloped and evaluated. The system is balanced using threecounter-masses to reduce the force imparted onto the base andthus reduce perturbations to the movement of the drone. Thesystem is thoroughly tested following trajectories while mountedto a force sensor and while on-board an aerial vehicle. Resultsshow that the forces transmitted to the base in all axes arereduced considerably, however improvements in overall flightaccuracy are not observed in aerial settings. Design lessonsto make a balanced delta-manipulator viable for practicalimplementation on an aerial vehicle are discussed in depth.A video summarising the flight testing results is available athttps://youtu.be/fXKnosnVKCk.

Conference paper

Li K, Baron N, Zhang X, Rojas Net al., 2022, EfficientGrasp: a unified data-efficient learning to grasp method for multi-fingered robot hands, IEEE Robotics and Automation Letters, Pages: 1-8, ISSN: 2377-3766

Autonomous grasping of novel objects that are previously unseen to a robot is an ongoing challenge in robotic manipulation. In the last decades, many approaches have been presented to address this problem for specific robot hands. The UniGrasp framework, introduced recently, has the ability to generalize to different types of robotic grippers; however, this method does not work on grippers with closed-loop constraints and is data-inefficient when applied to robot hands with multi-grasp configurations. In this paper, we present EfficientGrasp, a generalized grasp synthesis and gripper control method that is independent of gripper model specifications. EfficientGrasp utilizes a gripper workspace feature rather than UniGrasp’s gripper attribute inputs. This reduces memory use by 81.7% during training and makes it possible to generalize to more types of grippers, such as grippers with closed-loop constraints. The effectiveness of EfficientGrasp is evaluated by conducting object grasping experiments both in simulation and real-world; results show that the proposed method also outperforms UniGrasp when considering only grippers without closed-loop constraints. In these cases, EfficientGrasp shows 9.85% higher accuracy in generating contact points and 3.10% higher grasping success rate in simulation. The real-world experiments are conducted with a gripper with closed-loop constraints, which UniGrasp fails to handle while EfficientGrasp achieves a success rate of 83.3%. The main causes of grasping failures of the proposed method are analyzed, highlighting ways of enhancing grasp performance.

Journal article

Clark A, Rojas N, 2022, Malleable robots: reconfigurable robotic arms with continuum links of variable stiffness, IEEE Transactions on Robotics, ISSN: 1552-3098

Through the implementation of reconfigurability toachieve flexibility and adaptation to tasks by morphology changesrather than by increasing the number of joints, malleable robotspresent advantages over traditional serial robot arms in regardsto reduced weight, size, and cost. While limited in degrees offreedom (DOF), malleable robots still provide versatility acrossoperations typically served by systems using higher DOF thanrequired by the tasks. In this paper, we present the creationof a 2-DOF malleable robot, detailing the design of jointsand malleable link, along with its modelling through forwardand inverse kinematics, and a reconfiguration methodology thatinforms morphology changes based on end effector location—determining how the user should reshape the robot to enablea task previously unattainable. The recalibration and motionplanning for making robot motion possible after reconfigurationare also discussed, and thorough experiments with the prototypeto evaluate accuracy and reliability of the system are presented.Results validate the approach and pave the way for furtherresearch in the area

Journal article

Berkovic A, Laganier C, Chappell D, Nanayakkara T, Kormushev P, Bello F, Rojas Net 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.

Conference paper

Wang X, Rojas N, 2022, A data-efficient model-based learning framework for the closed-loop control of continuum robots, IEEE International Conference on Soft Robotics, Publisher: IEEE

Traditional dynamic models of continuum robotsare in general computationally expensive and not suitablefor real-time control. Recent approaches using learning-basedmethods to approximate the dynamic model of continuumrobots for control have been promising, although real datahungry—which may cause potential damage to robots andbe time consuming—and getting poorer performance whentrained with simulation data only. This paper presents a modelbased learning framework for continuum robot closed-loopcontrol that, by combining simulation and real data, showsto require only 100 real data to outperform a real-data-onlycontroller trained using up to 10000 points. The introduceddata-efficient framework with three control policies has utilizeda Gaussian process regression (GPR) and a recurrent neuralnetwork (RNN). Control policy A uses a GPR model and a RNNtrained in simulation to optimize control outputs for simulatedtargets; control policy B retrains the RNN in policy A with datagenerated from the GPR model to adapt to real robot physics;control policy C utilizes policy A and B to form a hybrid policy.Using a continuum robot with soft spines, we show that ourapproach provides an efficient framework to bridge the sim-to-real gap in model-based learning for continuum robots.

Conference paper

Chappell D, Yang Z, Son HW, Bello F, Kormushev P, Rojas Net al., 2022, Towards instant calibration in myoelectric prosthetic hands: a highly data-efficient controller based on the Wasserstein distance, International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE

Prosthetic hand control research typically focuses on developing increasingly complex controllers to achieve diminishing returns in pattern recognition of muscle activity signals, making models less suitable for user calibration. Some works have investigated transfer learning to alleviate this, but such approaches increase model size dramatically—thus reducing their suitability for implementation on real prostheses. In this work, we propose a novel, non-parametric controller that uses the Wasserstein distance to compare the distribution of incoming signals to those of a set of reference distributions,with the intended action classified as the closest distribution. This controller requires only a single capture of data per reference distribution, making calibration almost instantaneous. Preliminary experiments building a reference library show that, in theory, users are able to produce up to 9 distinguishable muscle activity patterns. However, in practice, variation whenrepeating actions reduces this. Controller accuracy results show that 10 non-disabled and 1 disabled participant were able to use the controller with a maximum of two recalibrations to perform 6 actions at an average accuracy of 89.9% and 86.7% respectively. Practical experiments show that the controller allows users to complete all tasks of the Jebsen-Taylor HandFunction Test, although the task of picking and placing small common objects required on average more time than the protocol’s maximum time.

Conference paper

Chappell D, Son HW, Clark AB, Yang Z, Bello F, Kormushev P, Rojas Net al., 2022, Virtual reality pre-prosthetic hand training with physics simulation and robotic force interaction, IEEE Robotics and Automation Letters, Vol: 7, Pages: 1-1, ISSN: 2377-3766

Virtual reality (VR) rehabilitation systems have been proposed to enable prosthetic hand users to perform training before receiving their prosthesis. Improving pre-prosthetic training to be more representative and better prepare the patient for prosthesis use is a crucial step forwards in rehabilitation. However, existing VR platforms lack realism and accuracy in terms of the virtual hand and the forces produced when interacting with the environment. To address these shortcomings, this work presents a VR training platform based on accurate simulation of an anthropomorphic prosthetic hand, utilising an external robot arm to render realistic forces that the user would feel at the attachment point of their prosthesis. Experimental results with non-disabled participants show that training with this platform leads to a significant improvement in Box and Block scores compared to training in VR alone and a control group with no prior training. Results performing pick-and-place tasks with a wider range of objects demonstrates that training in VR alone negatively impacts performance, whereas the proposed platform has no significant impact on performance. User perception results highlight that the platform is much closer to using a physical prosthesis in terms of physical demand and effort, however frustration is significantly higher during training.

Journal article

Sarabandi S, Lu Q, Chen G, Rojas Net al., 2022, In-hand manipulation with soft fingertips, IEEE International Conference on Soft Robotics

This paper introduces an approach for solving thein-hand manipulation problem, that is, the change of a graspedobject pose from an initial configuration to a final one withoutbreaking contact, with robot fingers having out-of-plane motion,redundancy, and equipped with soft fingertips. The proposedtechnique is based on keeping the initial grasp equilibriumcondition as a constraint to resolve finger redundancy. Two important aspects of in-hand manipulation with soft fingertips arethen studied; namely: i) the modeling of soft fingertip contactsbetween fingers and 3D objects, and ii) an efficient methodfor computing joint angles to move a grasped object betweendifferent poses without losing grasp equilibrium. Numericaland empirical experiments using soft fingertips with differentshore hardnesses with a two-fingered robot hand, composedof four-degree-of-freedom fingers with out-of-plane motion, areconducted. Results successfully validate the introduced strategyand its components.

Conference paper

Yang Z, Clark A, Chappell D, Rojas Net al., 2022, Instinctive real-time sEMG-based control of prosthetic hand with reduced data acquisition and embedded deep learning training, IEEE International Conference on Robotics and Automation

Achieving instinctive multi-grasp control of prosthetic hands typically still requires a large number of sensors,such as electromyography (EMG) electrodes mounted on aresidual limb, that can be costly and time consuming to position,with their signals difficult to classify. Deep-learning-based EMGclassifiers however have shown promising results over traditional methods, yet due to high computational requirements,limited work has been done with in-prosthetic training. Bytargeting specific muscles non-invasively, separating graspingaction into hold and release states, and implementing dataaugmentation, we show in this paper that accurate results forembedded, instinctive, multi-grasp control can be achieved withonly 2 low-cost sensors, a simple neural network, and minimalamount of training data. The presented controller, which isbased on only 2 surface EMG (sEMG) channels, is implementedin an enhanced version of the OLYMPIC prosthetic hand.Results demonstrate that the controller is capable of identifyingall 7 specified grasps and gestures with 93% accuracy, and issuccessful in achieving several real-life tasks in a real worldsetting.

Conference paper

Ranne A, Clark AB, Rojas N, 2022, Augmented reality-assisted reconfiguration and workspace visualization of malleable robots: workspace modification through holographic guidance, IEEE Robotics & Automation Magazine, Vol: 29, Pages: 2-13, ISSN: 1070-9932

Malleable robots are a type of reconfigurable serial robot capable of adapting their topology, through the use of variable stiffness malleable links, to desired tasks and workspaces by varying the relative positioning between their revolute joints. However, their reconfiguration is nontrivial, lacking intuitive communication between the human and the robot, and a method of efficiently aligning the end effector to a desired position. In this article, we present the design of an interactive augmented reality (AR) alignment interface, which helps a malleable robot understand the user’s task requirements, visualizes to the user the requested robot’s configuration and its workspace, and guides the user in reconfiguring the robot to achieve that configuration. Through motion tracking of a physical two degree-of-freedom (2 DoF) malleable robot, which can achieve an infinite number of workspaces, we compute the accuracy of the system in terms of initial calibration and overall accuracy, and demonstrate its viability. The results demonstrated a good performance, with an average repositioning accuracy of 9.64 ± 2.06 mm and an average base alignment accuracy of 10.54 ± 4.32 mm in an environment the size of 2,000 mm × 2,000 mm × 1,200 mm.

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

Lu Q, Wang J, Zhang Z, Chen G, Wang H, Rojas Net al., 2021, An underactuated gripper based on car differentials for self-adaptive grasping with passive disturbance rejection, IEEE International Conference on Robotics and Automation (ICRA) 2021, Publisher: IEEE, Pages: 2605-2611

We introduce an underactuated differential-based robot gripper able to perform self-adaptive grasping with passive disturbance rejection. The gripper utilises three car differential systems to achieve self-adaptiveness with a single actuator: a base differential for distributing power from motor to fingers, and two independent finger differentials for control-ling the proximal and distal joints. Linear and torsional springs are cleverly added to these differentials to allow the return of the fingers and the gripper-object system to equilibrium, thus enabling the gripper rejecting unexpected external disturbance forces applied to the fingers after securing a grasp. Moreover, the differentials allow the gripper to perform not only self-adaptive power grasps but also precision grasps, provide it with a large force transmission efficiency, and facilitate the prediction of grasping position. We analyse the static model of the introduced differential system and evaluate the gripper design via four sets of experiments. Numerical and empirical results clearly demonstrate the viability of the proposed grasper.

Conference paper

Lu Q, Baron N, Bai G, Rojas Net al., 2021, Mechanical intelligence for adaptive precision grasp, IEEE International Conference on Robotics and Automation (ICRA) 2021, Publisher: IEEE, Pages: 4530-4536

Mechanical intelligence is the use of mechanical and other physical properties to create robotic systems adaptable to new external situations using simple control schemes. Designs of robot hands have successfully been developed and optimised following this principle to produce self-adaptive and versatile power grasps via implementations based on underactuated fingers, elastic components, and open-loop motor control. However, these characteristics, and mechanical-intelligent strategies in general, have been seldom leveraged for precision grasping. This paper proposes a mechanical-intelligent technique to facilitate not only spiral caging power grasp, but also self-adaptive precision grasp with error tolerance. This approach is exemplified by the rigorous analysis, development, and testing of a novel three-fingered, two-actuator, underactuated robot hand, called the helical hand, which is capable of self-adaptive precision grasping, and of generating spiral helical power grasps of unknown objects by simply setting two actuators at a constant speed.

Conference paper

Clark AB, Liow L, Rojas N, 2021, Force evaluation of tendon routing for underactuated grasping, Journal of Mechanical Design, Vol: 143, Pages: 1-9, ISSN: 1050-0472

While the modeling analysis of the kinetostatic behavior of underactuated tendon-driven robotic fingers has been largely addressed in the literature, tendon routing is often not considered by these theoretical models. The tendon routing path plays a fundamental role in defining joint torques, and subsequently, the force vectors produced by the phalanges. However, dynamic tendon behavior is difficult to predict and is influenced by many external factors including tendon friction, the shape of the grasped object, the initial pose of the fingers, and finger contact points. In this paper, we present an experimental comparison of the force performance of nine fingers, with different tendon routing configurations. We use the concept of force-isotropy, in which forces are equal and distributed on each phalanx as the optimum condition for an adaptive grasp. Our results show only some of the finger designs surveyed exhibited a partial adaptive behavior, showing distributed force for the proximal and distal phalanxes throughout grasping cycles, while other routings resulted in only a single phalanx remaining in contact with the object.

Journal article

Baron N, Philippides A, Rojas N, 2021, A dynamically balanced kinematically redundant planar parallel robot, Journal of Mechanical Design, Pages: 1-12, ISSN: 1050-0472

A dynamically balanced robotic manipulator does not exert forces or moments onto the base on which it is fixed; this can be important for the performance of parallel robots as they are able to move at very high speeds, albeit usually have a reduced workspace. In recent years, kinematically redundant architectures have been proposed to mitigate the workspace limitations of parallel manipulators and increase their rotational capabilities; however, dynamically balanced versions of these architectures have not yet been presented. In this paper, a dynamically balanced kinematically redundant planar parallel architecture is introduced. The manipulator is composed of parallelogram linkages which reduces the number of counter rotary-elements required to moment balance the mechanism. The balancing conditions are derived, and the balancing parameters are optimised using Lagrange multipliers, such that the total mass and inertia of the system is minimised. The elimination of the shaking forces and moments is then verified via a simulation in the multi-body dynamic simulation software MSC Adams.</jats:p>

Journal article

Clark AB, Mathivannan V, Rojas N, 2021, A continuum manipulator for open-source surgical robotics research and shared development, IEEE Transactions on Medical Robotics and Bionics, Vol: 3, Pages: 277-280, ISSN: 2576-3202

Many have explored the application of continuum robot manipulators for minimally invasive surgery, and have successfully demonstrated the advantages their flexible design provides—with some solutions having reached commercialisation and clinical practice. However, the usual high complexity and closed-nature of such designs has traditionally restricted the shared development of continuum robots across the research area, thus impacting further progress and the solution of open challenges. In order to close this gap, this paper introduces ENDO, an open-source 3-segment continuum robot manipulator with control and actuation mechanism, whose focus is on simplicity, affordability, and accessibility. This robotic system is fabricated from low cost off-the-shelf components and rapid prototyping methods, and its information for implementation (and that of future iterations), including CAD files and source code, is available to the public on the https://github.com/OpenSourceMedicalRobots’s repository on GitHub, with the control library also available directly from Arduino. Herein, we present details of the robot design and control, validate functionality by experimentally evaluating its workspace, and discuss possible paths for future development.

Journal article

Shen M, Clark A, Rojas N, 2020, A scalable variable stiffness revolute joint based on layer jamming for robotic exoskeletons, Towards Autonomous Robotic Systems Conference ( TAROS ) 2020, Publisher: Springer Verlag, Pages: 3-14, ISSN: 0302-9743

Robotic exoskeletons have been a focal point of research due to an ever-increasing ageing population, longer life expectancy, and a desire to further improve the existing capabilities of humans. However, their effectiveness is often limited, with strong rigid structures poorly interfacing with humans and soft flexible mechanisms providing limited forces. In this paper, a scalable variable stiffness revolute joint is proposed to overcome this problem. By using layer jamming, the joint has the ability to stiffen or soften for different use cases. A theoretical and experimental study of maximum stiffness with size was conducted to determine the suitability and scalablity of this technology. Three sizes (50 mm, 37.5 mm, 25 mm diameter) of the joint were developed and evaluated. Results indicate that this technology is most suitable for use in human fingers, as the prototypes demonstrate a sufficient torque (0.054 Nm) to support finger movement.

Conference paper

Wang J, Lu Q, Clark A, Rojas Net al., 2020, A passively complaint idler mechanism for underactuated dexterous grippers with dynamic tendon routing, Towards Autonomous Robotic Systems Conference (TAROS ) 2020, Publisher: Springer Verlag, Pages: 25-36, ISSN: 0302-9743

In the field of robotic hands, tendon actuation is one of the most common ways to control self-adaptive underactuated fingers thanks to its compact size. Either differential or direct drive mechanisms are usually used in these systems to perform synchronised grasping using a single actuator. However, synchronisation problems arise in underactuated grippers whose position of proximal joints varies with time to perform manipulation operations, as this results in a tendon-driven system with dynamic anchor pulleys. This paper introduces a novel passively compliant idler mechanism to avoid unsynchronisation in grippers with a dynamic multi-tendon routing system, such that adequate grasping contact forces are kept under changes in the proximal joints’ positions. A re-configurable palm underactuated dexterous gripper is used as a case study, with the performance of the proposed compliant idler system being evaluated and compared through a contact force analysis during rotation and translation in-hand manipulation tasks. Experiment results clearly demonstrate the ability of the mechanism to synchronise a dynamic tendon routing gripper. A video summarising experiments and findings can be found at https://imperialcollegelondon.box.com/s/hk58688q2hjnu8dhw7uskr7vi9tqr9r5.

Conference paper

Clark A, Rojas N, 2020, Design and workspace characterisation of malleable robots, IEEE International Conference on Robotics and Automation, Publisher: IEEE, Pages: 9021-9027

For the majority of tasks performed by traditionalserial robot arms, such as bin picking or pick and place, onlytwo or three degrees of freedom (DOF) are required for motion;however, by augmenting the number of degrees of freedom,further dexterity of robot arms for multiple tasks can beachieved. Instead of increasing the number of joints of a robotto improve flexibility and adaptation, which increases controlcomplexity, weight, and cost of the overall system, malleablerobots utilise a variable stiffness link between joints allowing therelative positioning of the revolute pairs at each end of the linkto vary, thus enabling a low DOF serial robot to adapt acrosstasks by varying its workspace. In this paper, we present thedesign and prototyping of a 2-DOF malleable robot, calculatethe general equation of its workspace using a parameterisationbased on distance geometry—suitable for robot arms of variabletopology, and characterise the workspace categories that theend effector of the robot can trace via reconfiguration. Throughthe design and construction of the malleable robot we exploredesign considerations, and demonstrate the viability of theoverall concept. By using motion tracking on the physical robot,we show examples of the infinite number of workspaces thatthe introduced 2-DOF malleable robot can achieve.

Conference paper

Baron N, Philippides A, Rojas N, 2020, A robust geometric method of singularity avoidance for kinematically redundant planar parallel robot manipulators, Mechanism and Machine Theory, Vol: 151, Pages: 1-14, ISSN: 0094-114X

Jacobian-based methods of singularity analysis are known to be unreliable when applied to kinematically redundant parallel robot manipulators, due to their potential to miss certain singularities and incorrectly identify others in the manipulator’s workspace. In this paper, a geometric method of singularity avoidance for kinematically redundant planar parallel robot manipulators is presented, which firstly determines the manipulator’s proximity to a singularity and then computes how the kinematically redundant degree(s) of freedom should be optimised for the given pose of the end-effector. The singularity analysis is conducted by examining the mechanism in terms of the instantaneous centres of rotation of its corresponding mobility one sub-mechanisms when all but one of the actuators are locked, where the manipulator is in a type-II singularity when these points either are indeterminable or coincide with one another, and an index, rmin, is introduced which describes the minimum normalised distance from such conditions being met. A predictor-corrector method is employed to compute the configuration for which rmin is optimised, and is reachable without crossing a singularity. Finally, the advantages of the geometric method of singularity analysis are shown in comparison to traditional Jacobian-based methods when applied to kinematically redundant parallel robot manipulators.

Journal article

Lu Q, Baron N, Clark A, Rojas Net al., 2020, The RUTH Gripper: systematic object-invariant prehensile in-hand manipulation via reconfigurable underactuation, Robotics: Science and Systems, Publisher: RSS

We introduce a reconfigurable underactuated robothand able to perform systematic prehensile in-hand manipu-lations regardless of object size or shape. The hand utilisesa two-degree-of-freedom five-bar linkage as the palm of thegripper, with three three-phalanx underactuated fingers—jointlycontrolled by a single actuator—connected to the mobile revolutejoints of the palm. Three actuators are used in the robot handsystem, one for controlling the force exerted on objects by thefingers and two for changing the configuration of the palm.This novel layout allows decoupling grasping and manipulation,facilitating the planning and execution of in-hand manipulationoperations. The reconfigurable palm provides the hand withlarge grasping versatility, and allows easy computation of amap between task space and joint space for manipulation basedon distance-based linkage kinematics. The motion of objects ofdifferent sizes and shapes from one pose to another is thenstraightforward and systematic, provided the objects are keptgrasped. This is guaranteed independently and passively by theunderactuated fingers using a custom tendon routing method,which allows no tendon length variation when the relative fingerbase position changes with palm reconfigurations. We analysethe theoretical grasping workspace and manipulation capabilityof the hand, present algorithms for computing the manipulationmap and in-hand manipulation planning, and evaluate all theseexperimentally. Numerical and empirical results of several ma-nipulation trajectories with objects of different size and shapeclearly demonstrate the viability of the proposed concept.

Conference paper

Lu Q, Liang H, Nanayakkara DPT, Rojas Net al., 2020, Precise in-hand manipulation of soft objects using soft fingertips with tactile sensing and active deformation, IEEE International Conference on Soft Robotics, Publisher: IEEE, Pages: 52-57

While soft fingertips have shown significant development for grasping tasks, its ability to facilitate the manipulation of objects within the hand is still limited. Thanks to elasticity, soft fingertips enhance the ability to grasp soft objects. However, the in-hand manipulation of these objects has proved to be challenging, with both soft fingertips and traditional designs, as the control of coordinated fine fingertip motions and uncertainties for soft materials are intricate. This paper presents a novel technique for in-hand manipulating soft objects with precision. The approach is based on enhancing the dexterity of robot hands via soft fingertips with tactile sensing and active shape changing; such that pressurized air cavities act as soft tactile sensors to provide closed loop control of fingertip position and avoid object’s damage, and pneumatic-tuned positive-pressure deformations act as a localized soft gripper to perform additional translations and rotations. We model the deformation of the soft fingertips to predict the in-hand manipulation of soft objects and experimentally demonstrate the resulting in-hand manipulationcapabilities of a gripper of limited dexterity with an algorithm based on the proposed dual abilities. Results show that the introduced approach can ease and enhance the prehensile in-hand translation and rotation of soft objects for precision tasks across the hand workspace, without damage.

Conference paper

Baron N, Philippides A, Rojas N, 2020, On the false positives and false negatives of the Jacobian matrix in kinematically redundant parallel mechanisms, IEEE Transactions on Robotics, Vol: 36, ISSN: 1552-3098

The Jacobian matrix is a highly popular tool for the control and performance analysis of closed-loop robots. Its usefulness in parallel mechanisms is certainly apparent, and its application to solve motion planning problems, or other higher level questions, has been seldom queried, or limited to non-redundant systems. In this paper, we discuss the shortcomings of the use of the Jacobian matrix under redundancy, in particular when applied to kinematically redundant parallel architectures with non-serially connected actuators. These architectures have become fairly popular recently as they allow the end-effector to achieve full rotations, which is an impossible task with traditional topologies. The problems with the Jacobian matrix in these novel systems arise from the need to eliminate redundant variables when forming it, resulting in both situations where the Jacobian incorrectly identifies singularities (false positive), and where it fails to identify singularities (false negative). These issues have thus far remained unaddressed in the literature. We highlight these limitations herein by demonstrating several cases using numerical examples of both planar and spatial architectures.

Journal article

Lu Q, Clark A, Shen M, Rojas Net al., 2020, An origami-inspired variable friction surface for increasing the dexterity of robotic grippers, IEEE Robotics and Automation Letters, Vol: 5, Pages: 2538-2545, ISSN: 2377-3766

While the grasping capability of robotic grippers has shown significant development, the ability to manipulate objects within the hand is still limited. One explanation for this limitation is the lack of controlled contact variation between the grasped object and the gripper. For instance, human hands have the ability to firmly grip object surfaces, as well as slide over object faces, an aspect that aids the enhanced manipulation of objects within the hand without losing contact. In this letter, we present a parametric, origami-inspired thin surface capable of transitioning between a high friction and a low friction state, suitable for implementation as an epidermis in robotic fingers. A numerical analysis of the proposed surface based on its design parameters, force analysis, and performance in in-hand manipulation tasks is presented. Through the development of a simple two-fingered two-degree-of-freedom gripper utilizing the proposed variable-friction surfaces with different parameters, we experimentally demonstrate the improved manipulation capabilities of the hand when compared to the same gripper without changeable friction. Results show that the pattern density and valley gap are the main parameters that effect the in-hand manipulation performance. The origami-inspired thin surface with a higher pattern density generated a smaller valley gap and smaller height change, producing a more stable improvement of the manipulation capabilities of the hand.

Journal article

He L, Lu Q, Abad S-A, Rojas N, Nanayakkara DPTet al., 2020, Soft fingertips with tactile sensing and active deformation for robust grasping of delicate objects, IEEE Robotics and Automation Letters, Vol: 5, Pages: 2714-2721, ISSN: 2377-3766

Soft fingertips have shown significant adaptability for grasping a wide range of object shapes, thanks to elasticity. This ability can be enhanced to grasp soft, delicate objects by adding touch sensing. However, in these cases, the complete restraint and robustness of the grasps have proved to be challenging, as the exertion of additional forces on the fragile object can result in damage. This letter presents a novel soft fingertip design for delicate objects based on the concept of embedded air cavities, which allow the dual ability of tactile sensing and active shape-changing. The pressurized air cavities act as soft tactile sensors to control gripper position from internal pressure variation; and active fingertip deformation is achieved by applying positive pressure to these cavities, which then enable a delicate object to be kept securely in position, despite externally applied forces, by form closure. We demonstrate this improved grasping capability by comparing the displacement of grasped delicate objects exposed to high-speed motions. Results show that passive soft fingertips fail to restrain fragile objects at accelerations as low as 0.1 m/s 2 , in contrast, with the proposed fingertips delicate objects are completely secure even at accelerations of more than 5 m/s 2 .

Journal article

Liow L, Clark A, Rojas N, 2020, OLYMPIC: a modular, tendon-driven prosthetic hand with novel finger and wrist coupling mechanisms, IEEE Robotics and Automation Letters, Vol: 5, Pages: 299-306, ISSN: 2377-3766

Prosthetic hands, while having shown significant progress in affordability, typically suffer from limited repairability, specifically by the user themselves. Several modular hands have been proposed to address this, but these solutions require handling of intricate components or are unsuitable for prosthetic use due to the large volume and weight resulting from added mechanical complexity to achieve this modularity. In this paper, we propose a fully modular design for a prosthetic hand with finger and wrist level modularity, allowing the removal and attachment of tendon-driven fingers without the need for tools, retendoning, and rewiring. Our innovative design enables placement of the motors behind the hand for remote actuation of the tendons, which are contained solely within the fingers. Details of the novel coupling-transmission mechanisms enabling this are presented; and the capabilities of a prototype using a control-independent grasping benchmark are discussed. The modular detachment torque of the fingers is also computed to analyse the trade-off between intentional removal and the ability to withstand external loads. Experiment results demonstrate that the prosthetic hand is able to grasp a wide range of household and food items, of different shape, size, and weight, without resulting in the ejection of fingers, while allowing a user to remove them easily using a single hand.

Journal article

Clark A, Rojas N, 2019, Assessing the performance of variable stiffness continuum structures of large diameter, IEEE Robotics and Automation Letters, Vol: 4, Pages: 2455-2462, ISSN: 2377-3766

Variable stiffness continuum structures of large diameters are suitable for high-capability robots, such as in industrial practices where high loads and human–robot interaction are expected. Existing variable stiffness technologies have focused on application as medical manipulators, and as such have been limited to small diameter designs ( $\sim$ 15 mm). Various performance metrics have been presented for continuum structures thus far, focusing on force resistance, but no universal testing methodology for continuum structures that encapsulates their overall performance has been provided. This letter presents five individual qualities that can be experimentally quantified to establish the overall performance capability of a design with respect to its use as a variable stiffness continuum manipulator. Six large diameter ( $>$ 40 mm) continuum structures are developed following both conventional (granular and layer jamming) and novel (hybrid designs and structurally supported layer jamming) approaches and are compared using the presented testing methodology. The development of the continuum structures is discussed, and a detailed insight into the tested quality selection and experimental methodology is presented. Results of experiments demonstrate the suitability of the proposed approach for assessing variable stiffness continuum capability across the design.

Journal article

Lu Q, Rojas N, 2019, On soft fingertips for in-hand manipulation: modelling and implications for robot hand design, IEEE Robotics and Automation Letters, Vol: 4, Pages: 2471-2478, ISSN: 2377-3766

Contact models for soft fingertips are able to precisely computedeformation when information about contact forces and object position is known, thus improving the traditional soft finger contact model. However, the functionality of these approaches for the study of in-hand manipulation with robot hands has been shown to be limited, since the location of the manipulated object is uncertain due to compliance and closed-loop constraints. This paper presents a novel, tractable approach for contact modelling of soft fingertips in within-hand dexterous manipulation settings. The proposed method is based on a relaxation of the kinematic equivalent of point contact with friction, modelling the interaction between fingertips and objects as joints with clearances rather than ideal instances, and then approximating clearances via affine arithmetic to facilitate computation. These ideas are introduced using planar manipulation to aid discussion, and are used to predict the reachable workspace of a two-fingered robot hand with fingertips of different hardness and geometry. Numerical and empirical experiments are conducted to analyse the effects of soft fingertips on manipulation operability; results demonstrate the functionality of the proposed approach, as well as a tradeoff between hardness and depth in soft fingertips to achieve better manipulation performance of dexterous robot hands.

Journal article

Cheung YH, Baron N, Rojas N, 2019, Full-rotation singularity-safe workspace for kinematically redundant parallel robots, 20th Towards Autonomous Robotic Systems Conference, Publisher: Springer Verlag, ISSN: 0302-9743

This paper introduces and computes a novel type of work-space for kinematically redundant parallel robots that defines the regionin which the end-effector can make full rotations without coming close tosingular configurations; it departs from the traditional full-rotation dex-terous workspace, which considers full rotations without encounteringsingularities but does not take into account the performance problemsresulting from closeness to these locations. Kinematically redundant ar-chitectures have the advantage of being able to be reconfigured withoutchanging the pose of the end-effector, thus being capable of avoidingsingularities and being suitable for applications where high dexterityis required. Knowing the workspace of these robots in which the end-effector is able to complete full, smooth rotations is a key design aspectto improve performance; however, since this singularity-safe workspaceis generally small, or even non-existent, in most parallel manipulators,its characterisation and calculation have not received attention in theliterature. The proposed workspace for kinematically redundant robotsis introduced using a planar parallel architecture as a case study; the for-mulation works by treating the manipulator as two halves, calculatingthe full-rotation workspace of the end-effector for each half whilst ensur-ing singularity conditions are not approached or met, and then findingthe intersection of both regions. The method is demonstrated ontwoexample robot instances, and a numerical analysis is also carried out asa comparison.

Conference paper

Nanayakkara V, Sornkaran N, Wegiriya H, Vitzilaios N, Venetsanos D, Rojas N, Sahinkaya MN, Nanayakkara Tet al., 2019, A method to estimate the oblique arch folding axis for thumb assistive devices, 20th Towards Autonomous Robotic Systems Conference, Publisher: Springer Verlag, Pages: 28-40, ISSN: 0302-9743

People who use the thumb in repetitive manipulation tasks are likelyto develop thumb related impairments from excessive loading at the base jointsof the thumb. Biologically informed wearable robotic assistive mechanisms canprovide viable solutions to prevent occurring such injuries. This paper tests thehypothesis that an external assistive force at the metacarpophalangeal joint willbe most effective when applied perpendicular to the palm folding axis in termsof maximizing the contribution at the thumb-tip as well as minimizing the pro-jections on the vulnerable base joints of the thumb. Experiments conducted usinghuman subjects validated the predictions made by a simplified kinematic modelof the thumb that includes a foldable palm, showing that: 1) the palm folding an-gle varies from 71.5◦to 75.3◦(from the radial axis in the coronal plane) for thefour thumb-finger pairs and 2) the most effective assistive force direction (fromthe ulnar axis in the coronal plane) at the MCP joint is in the range 0◦<ψ<30◦for the four thumb-finger pairs. These findings provide design guidelines for handassistive mechanisms to maximize the efficacy of thumb external assistance.

Conference paper

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