Publications
68 results found
Liu J, Tong Y, Liu J, 2021, Review of snake robots in constrained environments, ROBOTICS AND AUTONOMOUS SYSTEMS, Vol: 141, ISSN: 0921-8890
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- Citations: 18
Zhang D, Liu J, Gao A, et al., 2020, An Ergonomic Shared Workspace Analysis Framework for the Optimal Placement of a Compact Master Control Console, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 5, Pages: 2995-3002, ISSN: 2377-3766
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- Citations: 9
Zhang D, Liu J, Zhang L, et al., 2020, Hamlyn CRM: a compact master manipulator for surgical robot remote control, INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, Vol: 15, Pages: 503-514, ISSN: 1861-6410
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- Citations: 9
Zhang D, Xiao B, Huang B, et al., 2019, A self-adaptive motion scaling framework for surgical robot remote control, IEEE Robotics and Automation Letters, Vol: 4, Pages: 359-366, ISSN: 2377-3766
Master-slave control is a common form of human-robot interaction for robotic surgery. To ensure seamless and intuitive control, a mechanism of self-adaptive motion scaling during teleoperaton is proposed in this letter. The operator can retain precise control when conducting delicate or complex manipulation, while the movement to a remote target is accelerated via adaptive motion scaling. The proposed framework consists of three components: 1) situation awareness, 2) skill level awareness, and 3) task awareness. The self-adaptive motion scaling ratio allows the operators to perform surgical tasks with high efficiency, forgoing the need of frequent clutching and instrument repositioning. The proposed framework has been verified on a da Vinci Research Kit to assess its usability and robustness. An in-house database is constructed for offline model training and parameter estimation, including both the kinematic data obtained from the robot and visual cues captured through the endoscope. Detailed user studies indicate that a suitable motion-scaling ratio can be obtained and adjusted online. The overall performance of the operators in terms of control efficiency and task completion is significantly improved with the proposed framework.
Zhang D, Xiao B, Huang B, et al., 2019, A Self-Adaptive Motion Scaling Framework for Surgical Robot Remote Control, IEEE Robotics and Automation Letters, Vol: 4, Pages: 359-366
Master-slave control is a common form of human-robot interaction for robotic surgery. To ensure seamless and intuitive control, a mechanism of self-adaptive motion scaling during teleoperaton is proposed in this letter. The operator can retain precise control when conducting delicate or complex manipulation, while the movement to a remote target is accelerated via adaptive motion scaling. The proposed framework consists of three components: 1) situation awareness, 2) skill level awareness, and 3) task awareness. The self-adaptive motion scaling ratio allows the operators to perform surgical tasks with high efficiency, forgoing the need of frequent clutching and instrument repositioning. The proposed framework has been verified on a da Vinci Research Kit to assess its usability and robustness. An in-house database is constructed for offline model training and parameter estimation, including both the kinematic data obtained from the robot and visual cues captured through the endoscope. Detailed user studies indicate that a suitable motion-scaling ratio can be obtained and adjusted online. The overall performance of the operators in terms of control efficiency and task completion is significantly improved with the proposed framework.
Dagnino G, Liu J, Abdelaziz M, et al., 2019, Haptic feedback and dynamic active constraints for robot-assisted endovascular catheterization, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), Publisher: IEEE
Robotic and computer assistance can bring significant benefits to endovascular procedures in terms of precision and stability, reduced radiation doses, improved comfort and access to difficult and tortuous anatomy.However,the design of current commercially available platforms tends to alter the natural bedside manipulation skills of the operator, so thatthe manually acquired experience and dexterityare not well utilized. Furthermore, most of these systems lackofhaptic feedback, preventing their acceptance and limiting the clinical usability.In this paper a new robotic platform for endovascular catheterization, the CathBot, is presented.It is an ergonomic master-slave system with navigation system and integrated vision-based haptic feedback, designed to maintain the natural bedside skills of the vascular surgeon. Unlike previous work reported in literature, dynamic motion tracking of both the vessel walls the catheter tip is incorporated to create dynamic activeconstraints. The system was evaluated through a combined quantitative and qualitative user study simulating catheterization tasks on a phantom. Forces exerted on the phantom were measured. The results showed a 70% decrease in mean force and 61% decrease in maximum force when force feedback is provided. This research provides the first integration of vision-based dynamic active constraints within an ergonomic robotic catheter manipulator. The technological advances presented here, demonstratesthat vision-based haptic feedback can improve the effectiveness, precision, and safety of robot-assisted endovascular procedures.
Chi W, Liu J, Abdelaziz MEMK, et al., 2019, Trajectory Optimization of Robot-Assisted Endovascular Catheterization with Reinforcement Learning, 25th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 3875-3881, ISSN: 2153-0858
Zhang D, Guo Y, Chen J, et al., 2019, A Handheld Master Controller for Robot-Assisted Microsurgery, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 394-400, ISSN: 2153-0858
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- Citations: 8
Singh RK, Varghese RJ, Liu J, et al., 2019, A multi-sensor fusion approach for intention detection, 4th International Conference on NeuroRehabilitation (ICNR), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 454-458, ISSN: 2195-3562
For assistive devices to seamlessly and promptly assist users with activities of daily living (ADL), it is important to understand the user’s intention. Current assistive systems are mostly driven by unimodal sensory input which hinders their accuracy and responses. In this paper, we propose a context-aware sensor fusion framework to detect intention for assistive robotic devices which fuses information from a wearable video camera and wearable inertial measurement unit (IMU) sensors. A Naive Bayes classifier is used to predict the intent to move from IMU data and the object classification results from the video data. The proposed approach can achieve an accuracy of 85.2% in detecting movement intention.
Bernstein A, Varghese RJ, Liu J, et al., 2018, An assistive ankle joint exoskeleton for gait impairment, 4th International Conference on NeuroRehabilitation (ICNR), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 658-662, ISSN: 2195-3562
Motor rehabilitation and assistance post-stroke are becoming a major concern for healthcare services with an increasingly aging population. Wearable robots can be a technological solution to support gait rehabilitation and to provide assistance to enable users to carry out activities of daily living independently. To address the need for long-term assistance for stroke survivors suffering from drop foot, this paper proposes a low-cost, assistive ankle joint exoskeleton for gait assistance. The proposed exoskeleton is designed to provide ankle foot support thus enabling normal walking gait. Baseline gait reading was recorded from two force sensors attached to a custom-built shoe insole of the exoskeleton. From our experiments, the average maximum force during heel-strike (63.95 N) and toe-off (54.84 N) were found, in addition to the average period of a gait cycle (1.45 s). The timing and force data were used to control the actuation of tendons of the exoskeleton to prevent the foot from preemptively hitting the ground during swing phase.
Triantafyllou P, Wisanuvej P, Giannarou S, et al., 2018, A Framework for Sensorless Tissue Motion Tracking in Robotic Endomicroscopy Scanning, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 2694-2699, ISSN: 1050-4729
Davila-Chacon J, Liu J, Wermter S, 2018, Enhanced robot speech recognition using biomimetic binaural sound source localisation, IEEE Transactions on Neural Networks and Learning Systems, Vol: 30, Pages: 138-150, ISSN: 2162-2388
Inspired by the behaviour of humans talkingin noisy environments, we propose an embodied embeddedcognition approach to improve automatic speech recogni-tion (ASR) systems for robots in complex situation, suchas with ego-noise, using binaural sound source localisation(SSL). The approach is verified by measuring the impactof SSL with a humanoid robot head on the performanceof an ASR system. More specifically, a robot orients itselftowards the angle where the signal-to-noise ratio (SNR)of speech is maximised for one microphone before doingan ASR task. First, a spiking neural network inspired bythe midbrain auditory system based on our previous workis applied to calculate the sound signal angle. Then, afeedforward neural network is used to handle high levelsof ego-noise and reverberation in the signal. Finally, thesound signal is fed into an ASR system. For ASR, weuse a system developed by our group and compare itsperformance with and without support from SSL. We testour SSL and ASR systems on two humanoid platformswith different structural and material properties. With ourapproach we halve the sentence error rate with respect tothe common downmixing of both channels. Surprisingly,the ASR performance is more than two times better whenthe angle between the humanoid head and the sound sourceallows sound waves to be reflected most intensely from thepinna to the ear microphone, rather than when soundwaves arrive perpendicularly to the membrane.
Chi W, Liu J, Rafii-Tari H, et al., 2018, Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization, INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, Vol: 13, Pages: 855-864, ISSN: 1861-6410
PurposeEndovascular intervention is limited by two-dimensional intraoperative imaging and prolonged procedure times in the presence of complex anatomies. Robotic catheter technology could offer benefits such as reduced radiation exposure to the clinician and improved intravascular navigation. Incorporating three-dimensional preoperative imaging into a semiautonomous robotic catheterization platform has the potential for safer and more precise navigation. This paper discusses a semiautonomous robotic catheter platform based on previous work (Rafii-Tari et al., in: MICCAI2013, pp 369–377. https://doi.org/10.1007/978-3-642-40763-5_46, 2013) by proposing a method to address anatomical variability among aortic arches. It incorporates anatomical information in the process of catheter trajectories optimization, hence can adapt to the scale and orientation differences among patient-specific anatomies.MethodsStatistical modeling is implemented to encode the catheter motions of both proximal and distal sites based on cannulation data obtained from a single phantom by an expert operator. Non-rigid registration is applied to obtain a warping function to map catheter tip trajectories into other anatomically similar but shape/scale/orientation different models. The remapped trajectories were used to generate robot trajectories to conduct a collaborative cannulation task under flow simulations. Cross-validations were performed to test the performance of the non-rigid registration. Success rates of the cannulation task executed by the robotic platform were measured. The quality of the catheterization was also assessed using performance metrics for manual and robotic approaches. Furthermore, the contact forces between the instruments and the phantoms were measured and compared for both approaches.ResultsThe success rate for semiautomatic cannulation is 98.1% under dry simulation and 94.4% under continuous flow simulation. The proposed robotic approach achieved smoother cathete
Wisanuvej P, Gras GG, Leibrandt KL, et al., 2017, Master manipulator designed for highly articulated robotic instruments in single access surgery, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 209-214
The performance of a master-slave robotic system depends significantly on the ergonomics and the capability of its master device to correctly interface the user with the slave robot. Master manipulators generating commands in task space represent a commonly adopted solution for controlling a range of slave robots while retaining an ergonomic design. However, these devices present several drawbacks, such as requiring the use of clutching mechanics to compensate for the mismatch between slave and master workspaces, and the lack of capability to intuitively transmit important information such as specific joint limits to the user. In this paper, a novel joint-space master manipulator is presented. This manipulator emulates the kinematic structure of highly flexible surgical instruments which it is designed to control. This system uses 6 active degrees of freedom to compensate for its own weight, as well as to provide force feedback corresponding to the slave robot's joint limits. A force/torque sensor integrated at the end effector is used to relay user-generated forces and torques directly to specific joints. This is performed to counteract the friction stemming from structural constraints imposed by the kinematic design of the instruments. Finally, a usability study is carried out to test the validity of the system, proving that the instruments can be intuitively controlled even at the extremities of the workspace.
Wisanuvej P, Giataganas PG, Leibrandt KL, et al., 2017, Three-dimensional robotic-assisted endomicroscopy with a force adaptive robotic arm, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE
Effective in situ, in vivo tumour margin assessment is an important, yet unmet, clinical demand in surgical oncology. Recent advances in probe-based optical imaging tools such as confocal endomicroscopy is making inroads in clinical applications. In practice, maintaining consistent tissue contact whilst ensuring large area surveillance is crucial for its practical adoption and for this reason there is a great demand for robotic assistance so that high-speed endomicroscopes can be combined with autonomous scanning, thus simplifying its incorporation in routine surgical workflows. In this paper, a cooperatively controlled robotic manipulator is developed, which provides a stable mechatronically-enhanced platform for micro-scanning tools to perform local high resolution mosaics over 3D undulating moving surfaces. Detailed kinematic and overall system performance analyses are provided and the results demonstrate the adaptability in terms of both contact force and orientation control of the system, and thus its simplicity in practical deployment and value for clinical adoption.
Chi W, Rafii-Tari H, Payne CJ, et al., 2017, A learning based training and skill assessment platform with haptic guidance for endovascular catheterization, 2017 IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 2357-2363, ISSN: 1050-4729
Increasing demands in endovascular intervention have motivated technical skill training and competency-based measures of performance. However, there are no well-established online metrics for technical skill assessment; few studies have explored operator behavioral patterns from catheter motion and operator hand motions. This paper proposes a platform for active online training and objective assessment of endovascular skills, through learning optimum catheter motions from multiple demonstrations. An ungrounded hand-held haptic device for providing intuitive haptic guidance to novice users based on this learnt information is also proposed. Statistical models are implemented to extract the underlying catheter motion patterns, and utilize them for performance evaluation and haptic guidance. The results show significant improvements in endovascular navigation for inexperienced operators. Finer catheter motions were achieved with the provided haptic guidance. The results suggest that the proposed platform can be integrated into current clinical training setups, and motivate the improvement of endovascular training platforms with better realism.
Freer DR, Liu J, Yang G-Z, 2017, Optimization of EMG Movement Recognization for Use in an Upper Limb Wearable Robot, 14th Annual IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 202-205, ISSN: 2376-8886
Wang W, Liu J, Xie G, et al., 2017, A bio-inspired electrocommunication system for small underwater robots., Bioinspir Biomim, Vol: 12, Pages: 036002-036002
Weakly electric fishes (Gymnotid and Mormyrid) use an electric field to communicate efficiently (termed electrocommunication) in the turbid waters of confined spaces where other communication modalities fail. Inspired by this biological phenomenon, we design an artificial electrocommunication system for small underwater robots and explore the capabilities of such an underwater robotic communication system. An analytical model for electrocommunication is derived to predict the effect of the key parameters such as electrode distance and emitter current of the system on the communication performance. According to this model, a low-dissipation, and small-sized electrocommunication system is proposed and integrated into a small robotic fish. We characterize the communication performance of the robot in still water, flowing water, water with obstacles and natural water conditions. The results show that underwater robots are able to communicate electrically at a speed of around 1 k baud within about 3 m with a low power consumption (less than 1 W). In addition, we demonstrate that two leader-follower robots successfully achieve motion synchronization through electrocommunication in the three-dimensional underwater space, indicating that this bio-inspired electrocommunication system is a promising setup for the interaction of small underwater robots.
Shang J, Leibrandt K, Giataganas P, et al., 2017, A Single-Port Robotic System for Transanal Microsurgery—Design and Validation, IEEE Robotics and Automation Letters, Vol: 2, Pages: 1510-1517, ISSN: 2377-3766
This letter introduces a single-port robotic platform for transanal endoscopic microsurgery (TEMS). Two robotically controlled articulated surgical instruments are inserted via a transanal approach to perform submucosal or full-thickness dissection. This system is intended to replace the conventional TEMS approach that uses manual laparoscopic instruments. The new system is based on master-slave robotically controlled tele-manipulation. The slave robot comprises a support arm that is mounted on the operating table, supporting a surgical port and a robotic platform that drives the surgical instruments. The master console includes a pair of haptic devices, as well as a three-dimensional display showing the live video stream of a stereo endoscope inserted through the surgical port. The surgical instrumentation consists of energy delivery devices, graspers, and needle drivers allowing a full TEMS procedure to be performed. Results from benchtop tests, ex vivo animal tissue evaluation, and in vivo studies demonstrate the clinical advantage of the proposed system.
Wisanuvej P, Leibrandt KL, Liu JL, et al., 2016, Hands-on reconfigurable robotic surgical instrument holder arm, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, ISSN: 2153-0866
Abstract:The use of conventional surgical tool holders requires an assistant during positioning and adjustment due to the lack of weight compensation. In this paper, we introduce a robotic arm system with hands-on control approach. The robot incorporates a force sensor at the end effector which realises tool weight compensation as well as hands-on manipulation. On the operating table, the required workspace can be tight due to a number of instruments required. There are situations where the surgical tool is at the desired location but the holder arm pose is not ideal due to space constraints or obstacles. Although the arm is a non-redundant robot because of the limited degrees of freedom, the pseudo-null-space inverse kinematics can be used to constrain a particular joint of the robot to a specific angle while the other joints compensate in order to minimise the tool movement. This allows operator to adjust the arm configuration conveniently together with the weight compensation. Experimental results demonstrated that our robotic arm can maintain the tool position during reconfiguration significantly more stably than a conventional one.
Huen D, Liu J, Lo B, 2016, An integrated wearable robot for tremor suppression with context aware sensing, 13th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 312-317, ISSN: 2376-8886
Abstract:Tremor is a neurological disorder which can significantly impede the daily functions of patients. The available treatments for patients with tremor are mainly pharmacotherapy and neurosurgery, but these treatments often have side effects. A wearable exoskeleton can potentially provide the assistance needed for patients with Parkinsonian or essential tremor to carry out daily activities and enable independent living. This paper presents the design and development of a 3D printed lightweight tremor suppression wearable exoskeleton. One of the major technical challenges for wearable robot is to maintain long battery life meanwhile miniature in size for practical use. This paper proposes an integrated approach where context aware Body Sensor Networks (BSN) sensors are incorporated to characterize voluntary and tremor movement, and detect activities of daily life (ADL). With the contextual information, the system can determine the intention of the user, optimize its control and minimize its power consumption by providing the necessary suppression only when needed. The preliminary result has shown that the wearable robot prototype can reduce the amplitude of simulated tremor by around 77%, and accurately identify different ADL with accuracy above 70%.
Pettitt C, Liu J, Kwasnicki R, et al., 2015, A pilot study to determine whether using a lightweight, wearable micro-camera improves dietary assessment accuracy and offers information on macronutrients and eating rate., British Journal of Nutrition, Vol: 115, Pages: 160-167, ISSN: 1475-2662
A major limitation in nutritional science is the lack of understanding of the nutritional intake of free living people. There is an inverse relationship between accuracy of reporting of energy intake by all current nutritional methodologies and body weight. In this pilot study we aim to explore whether using a novel lightweight, wearable micro-camera improves accuracy of dietary intake assessment. Doubly-labelled water (DLW) was used to estimate energy expenditure and intake over a 14-day period over which time participants (n = 6) completed a food diary and wore a micro-camera on 2 of the days. Comparisons were made between the estimated energy intake from reported food diary alone and together with the images from the micro-camera recordings. There was an average daily deficit of 3912kJ using food diaries to estimate energy intake compared to estimated energy expenditure from DLW (p=0.0118) representing an under-reporting rate of 34%. Analysis of food diaries alone showed a significant deficit in estimated daily energy intake compared to estimated intake from food diary analysis with images from the micro-camera recordings (405kJ). Use of the micro-camera images in conjunction with food diaries improves the accuracy of dietary assessment and provides valuable information on macronutrient intake and eating rate. There is a need to develop this recording technique to remove user and assessor bias.
King HK, Shang JS, Liu JL, et al., 2015, Micro-IGES Robot for Transanal Robotic Microsurgery., In The Hamlyn Symposium on Medical Robotics.
Rafii-Tari H, Payne C, Liu J, et al., 2015, Towards Automated Surgical Skill Evaluation of Endovascular Catheterization Tasks based on Force and Motion Signatures, IEEE International Conference on Robotics and Automation (ICRA), Pages: 1789-1794
Ryuh Y-S, Yang G-H, Liu J, et al., 2015, A School of Robotic Fish for Mariculture Monitoring in the Sea Coast, Journal of Bionic Engineering, Vol: 12, Pages: 37-46, ISSN: 1672-6529
Vicente A, Liu J, Yang G-Z, 2015, Surface classification based on vibration on omni-wheel mobile base, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 916-921, ISSN: 2153-0858
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- Citations: 18
Liu J, Yang GZ, 2014, Robust speech recognition in reverberant environments by using an optimal synthetic room impulse response model, Speech Communication, Pages: 65-77, ISSN: 0193-6700
Liu J, Yang GZ, 2014, Robust speech recognition in reverberant environments by using an optimal synthetic room impulse response model, Speech Communication, Vol: 67, Pages: 65-77, ISSN: 1872-7182
This paper presents a practical technique for Automatic speech recognition (ASR) in multiple reverberant environment selection. Multiple ASR models are trained with artificial synthetic room impulse responses (IRs), i.e. simulated room IRs, with different reverberation time (T60Models) and tested on real room IRs with varying T60Rooms. To apply our method, the biggest challenge is to choose a proper artificial room IR model for training ASR models. In this paper, a generalised statistical IR model with attenuated reverberation after an early reflection period, named attenuated IR model, has been adopted based on three time-domain statistical IR models. Its optimal values of the reverberation-attenuation factor and the early reflection period on the recognition rate have been searched and determined. Extensive testing has been performed over four real room IR sets (63 IRs in total) with variant T60Rooms and speaker microphone distances (SMDs). The optimised attenuated IR model had the best performance in terms of recognition rate over others. Specific considerations of the practical use of the method have been taken into account including: (i) the maximal training step of T60Model in order to get the minimal number of models with acceptable performance; (ii) the impact of selection errors on the ASR caused by the estimation error of T60Room; and (iii) the performance over SMD and direct-to-reverberation energy Ratio (DRR). It is shown that recognition rates of over 80∼∼90% are achieved in most cases. One important advantage of the method is that T60Room can be estimated either from reverberant sound directly ( Takeda et al., 2009, Falk and Chan, 2010 and Löllmann et al., 2010) or from an IR measured from any point of the room as it remains constant in the same room ( Kuttruff, 2000), thus it is particularly suited to mobile applications. Compared to many classical dereverberation methods, the proposed method is more suited to ASR tasks in multiple reverb
Wisanuvej PW, Liu JL, Chen CMC, et al., 2014, Blind collision detection and obstacle characterisation using a compliant robotic arm, 2014 IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, ISSN: 1050-4729
This paper presents a novel blind collision detection and material characterisation scheme for a compliant robotic arm. By the incorporation of a simple MEMS accelerometer at each joint, the robot is able to detect collision, identify the material of an obstacle, and create a map of the environment. Detailed hardware design is provided, illustrating its value for building a compact and economical robot platform. The proposed method does not require the additional use of vision sensor for mapping the environment, and hence is termed as `blind' collision detection and environment mapping. Based on the shock wave and vibration signals, the proposed algorithm is able to classify a range of materials encountered. Detailed laboratory evaluation was performed with controlled obstacle collision from different orientation and locations with varying force and materials. The proposed method has achieved 98% detection sensitivity while maintaining 77% specificity. Furthermore, by using sound feature extraction and machine learning techniques, the classifier produces an accuracy of 98% for classifying four different impact materials. In this paper, we also demonstrate its use for detailed environment mapping by using the proposed method.
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