Imperial College London

Dr Nicolas Rojas

Faculty of EngineeringDyson School of Design Engineering

Senior Lecturer
 
 
 
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Contact

 

n.rojas Website

 
 
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Location

 

1M07Royal College of ScienceSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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83 results found

Wang X, Lu Q, Lee D, Gan Z, Rojas Net al., 2024, A soft continuum robot with self-controllable variable curvature, IEEE Robotics and Automation Letters, Vol: 9, Pages: 2016-2023, ISSN: 2377-3766

This letter introduces a new type of soft continuum robot, called SCoReS, which is capable of self-controlling continuously its curvature at the segment level; in contrast to previous designs which either require external forces or machine elements, or whose variable curvature capabilities are discrete—depending on the number of locking mechanisms and segments. The ability to have a variable curvature, whose control is continuous and independent from external factors, makes a soft continuum robot more adaptive in constrained environments, similar to what is observed in nature in the elephant's trunk or ostrich's neck for instance which exhibit multiple curvatures. To this end, our soft continuum robot enables reconfigurable variable curvatures utilizing a variable stiffness growing spine based on micro-particle granular jamming for the first time. We detail the design of the proposed robot, presenting its modeling through beam theory and FEA simulation—which is validated through experiments. The robot's versatile bending profiles are then explored in experiments and an application to grasp fruits at different configurations is demonstrated.

Journal article

Chen W, Rojas N, 2024, TraKDis: a transformer-based knowledge distillation approach for visual reinforcement learning with application to cloth manipulation, IEEE Robotics and Automation Letters, Vol: 9, Pages: 2455-2462, ISSN: 2377-3766

Approaching robotic cloth manipulation using reinforcement learning based on visual feedback is appealing as robot perception and control can be learned simultaneously. However, major challenges result due to the intricate dynamics of cloth and the high dimensionality of the corresponding states, what shadows the practicality of the idea. To tackle these issues, we propose TraKDis , a novel Tra nsformer-based K nowledge Dis tillation approach that decomposes the visual reinforcement learning problem into two distinct stages. In the first stage, a privileged agent is trained, which possesses complete knowledge of the cloth state information. This privileged agent acts as a teacher, providing valuable guidance and training signals for subsequent stages. The second stage involves a knowledge distillation procedure, where the knowledge acquired by the privileged agent is transferred to a vision-based agent by leveraging pre-trained state estimation and weight initialization. TraKDis demonstrates better performance when compared to state-of-the-art RL techniques, showing a higher performance of 21.9%, 13.8%, and 8.3% in cloth folding tasks in simulation. Furthermore, to validate robustness, we evaluate the agent in a noisy environment; the results indicate its ability to handle and adapt to environmental uncertainties effectively. Real robot experiments are also conducted to showcase the efficiency of our method in real-world scenarios.

Journal article

Lee D, Chen W, Chen X, Rojas Net al., 2024, G.O.G: a versatile gripper-on-gripper design for bimanual cloth manipulation with a single robotic arm, IEEE Robotics and Automation Letters, Vol: 9, Pages: 2415-2422, ISSN: 2377-3766

The manipulation of garments poses research challenges due to their deformable nature and the extensive variability in shapes and sizes. Despite numerous attempts by researchers to address these via approaches involving robot perception and control, there has been a relatively limited interest in resolving it through the co-development of robot hardware. Consequently, the majority of studies employ off-the-shelf grippers in conjunction with dual robot arms to enable bimanual manipulation and high dexterity. However, this dual-arm system increases the overall cost of the robotic system as well as its control complexity in order to tackle robot collisions and other robot coordination issues. As an alternative approach, we propose to enable bimanual cloth manipulation using a single robot arm via novel end effector design—sharing dexterity skills betweenmanipulator and gripper rather than relying entirely on robot arm coordination. To this end, we introduce a new gripper, called G.O.G., based on a gripper-on-gripper structure where the first gripper independently regulates the span, up to 500mm, between its fingers which are in turn also grippers. These finger grippers consist of a variable friction module that enables two grasping modes: firm and sliding grasps. Household item and cloth object benchmarks are employed to evaluate the performance of the proposed design, encompassing both experiments on the gripper design itself and on cloth manipulation. Experimentalresults demonstrate the potential of the introduced ideas toundertake a range of bimanual cloth manipulation tasks witha single robot arm. Supplementary material is available athttps://sites.google.com/view/gripperongripper.

Journal article

Wang X, Rojas N, 2024, Cosserat rod modeling and validation for a soft continuum robot with self-controllable variable curvature, IEEE7th IEEE-RAS International Conference on Soft Robotics (ROBOSOFT 2024), Publisher: IEEE

This paper introduces a Cosserat rod based math-ematical model for modeling a self-controllable variable curvature soft continuum robot. This soft continuum robot has a hollow inner channel and was developed with the ability to perform variable curvature utilizing a growing spine. The growing spine is able to grow and retract while modifies its stiffness through milli-size particle (glass bubble) granular jamming. This soft continuum robot can then perform continuous curvature variation, unlike previous approaches whose curvature variation is discrete and depends on the number of locking mechanisms or manual configurations. The robot poses an emergent modeling problem due to the variable stiffness growing spine which is addressed in this paper. We investigate the property of growing spine stiffness and incorporate it into the Cosserat rod model by implementing a combined stiffness approach. We conduct experiments with the soft continuum robot in various configurations and compared the results withour developed mathematical model. The results show that the mathematical model based on the adapted Cosserat rod matches the experimental results with only a 3.3% error with respect to the length of the soft continuum robot.

Conference paper

Lee D, Chen W, Rojas N, 2024, Synthetic data enables faster annotation and robust segmentation for multi-object grasping in clutter, International Conference on Mechatronics and Robotics Engineering

Conference paper

Chappell D, Bello F, Kormushev P, Rojas Net al., 2023, The hydra hand: a mode-switching underactuated gripper with precision and power grasping modes, IEEE Robotics and Automation Letters, Vol: 8, Pages: 7599-7606, ISSN: 2377-3766

Human hands are able to grasp a wide range of object sizes, shapes, and weights, achieved via reshaping and altering their apparent grasping stiffness between compliant power and rigid precision. Achieving similar versatility in robotic hands remains a challenge, which has often been addressed by adding extra controllable degrees of freedom, tactile sensors, or specialised extra grasping hardware, at the cost of control complexity and robustness. We introduce a novel reconfigurable four-fingered two-actuator underactuated gripper—the Hydra Hand—that switches between compliant power and rigid precision grasps using a single motor, while generating grasps via a single hydraulic actuator—exhibiting adaptive grasping between finger pairs, enabling the power grasping of two objects simultaneously. The mode switching mechanism and the hand's kinematics are presented and analysed, and performance is tested on two grasping benchmarks: one focused on rigid objects, and the other on items of clothing. The Hydra Hand is shown to excel at grasping large and irregular objects, and small objects with its respective compliant power and rigid precision configurations. The hand's versatility is then showcased by executing the challenging manipulation task of safely grasping and placing a bunch of grapes, and then plucking a single grape from the bunch.

Journal article

Lu Q, Gan Z, Wang X, Bai G, Zhang Z, Rojas Net al., 2023, Mechanical intelligence for prehensile in-hand manipulation of spatial trajectories, IEEE International Conference on Robotics and Automation, Publisher: Institute of Electrical and Electronics Engineers, Pages: 1-7, ISSN: 1050-4729

The application of mechanical and other physicalproperties to the development of robotic systems that can easily adapt to changing external situations is known as mechanical intelligence. Following this concept, many robot hand designs can produce self-adaptive and versatile grasps with simple underactuated fingers and open-loop control, while mechanical-intelligent strategies for dexterous manipulation are still limited. This paper proposes a mechanical-intelligent technique to facilitate dexterous manipulation, in particular prehensile in-hand manipulation. The proposed strategy is based on the generation of complex spatial trajectories of the hand-object system, controlled in open loop with the minimum number ofactuators and using simple low-level non-position modes. This approach is exemplified by the rigorous analysis and testing of a three-fingered two-actuator underactuated robot hand, called the helical hand, which is capable of generating helical prehensile in-hand manipulation of diversiform objects under error tolerance controlled by constant speed algorithm.

Conference paper

Chen W, Lee D, Chappell D, Rojas Net al., 2023, Learning to grasp clothing structural regions for garment manipulation tasks, IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE

Conference paper

Li K, Chappell D, Rojas N, 2023, Immersive Demonstrations are the Key to Imitation Learning, IEEE International Conference on Robotics and Automation

Conference paper

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, Pages: 7359-7366

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

Clark A, Rojas N, 2022, Malleable robots: reconfigurable robotic arms with continuum links of variable stiffness, IEEE Transactions on Robotics, Vol: 38, Pages: 3832-3849, 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

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

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

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, 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

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

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

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, 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

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

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

Wang J, Lu Q, Clark A, Rojas Net al., 2020, A passively compliant 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

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

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

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