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Publications (10 of 21) Show all publications
Sun, D. & Liao, Q. (2024). A Framework of Robot Manipulability Learning and Control and Its Application in Telerobotics. IEEE transactions on fuzzy systems, 32(1), 266-280
Open this publication in new window or tab >>A Framework of Robot Manipulability Learning and Control and Its Application in Telerobotics
2024 (English)In: IEEE transactions on fuzzy systems, ISSN 1063-6706, E-ISSN 1941-0034, Vol. 32, no 1, p. 266-280Article in journal (Refereed) Published
Abstract [en]

Manipulability ellipsoid on the Riemannian manifold serves as an effective criterion to analyze, measure, and control the dexterous performance of robots. For asymmetric bilateral telerobotics, due to the different structures of master and slave robots, it is difficult or even impossible for the operator to manually regulate the manipulability of the remote slave robot. Thus, it is desired that the slave robot can automatically regulate its manipulability to assist the operator in remote different task executions, like humans regulating their own postures to enhance manipulability and adapt to different task scenarios. This article proposes a novel framework for manipulability transfer from human to robot. In this framework, we develop a Type-2 fuzzy model-based imitation learning method to encode and reproduce manipulability ellipsoids from demonstrations. This method can achieve high performance in accuracy and computational efficiency. In addition, it supports learning from a single demonstration. Then, we combine this method with a Riemannian manifold-based quadratic programming control algorithm such that the robot manipulability can fast track the desired manipulability profile. This framework is applied to telerobotics, in which a bilateral teleoperation controller is designed that enables the robot to follow the operator's command and simultaneously self-regulate its manipulability to perform the task adaptively. Meanwhile, the operator can receive force feedback relating to the manipulability regulation. Evaluations using comparative studies and practical experiments with a 3-DoF haptic device and 7-DoF robots are presented to show the effectiveness of the proposed framework.

Place, publisher, year, edition, pages
IEEE, 2024
Keywords
Bilateral telerobotics, imitation learning, manipulability ellipsoids, Riemannian manifold, type-2 fuzzy model
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-107171 (URN)10.1109/TFUZZ.2023.3297665 (DOI)
Note

Funding agency:

Swedish Knowledge Foundation in the TeamRob Synergy under Project 20210016

Available from: 2023-07-19 Created: 2023-07-19 Last updated: 2024-01-11Bibliographically approved
Sun, D. & Liao, Q. (2024). A Fuzzy Cluster-based Framework for Robot-Environment Collision Reaction. IEEE transactions on fuzzy systems, 32(1), 75-89
Open this publication in new window or tab >>A Fuzzy Cluster-based Framework for Robot-Environment Collision Reaction
2024 (English)In: IEEE transactions on fuzzy systems, ISSN 1063-6706, E-ISSN 1941-0034, Vol. 32, no 1, p. 75-89Article in journal (Refereed) Published
Abstract [en]

Environmental collision is a challenging issue in human-robot collaboration. This article proposes a novel fuzzy cluster-based framework for robots to have reactive responses to various environmental collision scenarios. This framework makes four contributions: First, a fuzzy cluster-based environmental collision detection algorithm is developed to efficiently classify the collision area and non-collision (free) area of the environment. Second, based on the collision detection algorithm, a p-norm approximation-based collision avoidance algorithm is proposed to enable robots to avoid environmental collisions with guaranteed stability. Third, by extending the collision avoidance algorithm, an environmental collision adaptation algorithm is proposed to allow robots to adapt to environmental collisions with intelligently regulated contact force. Fourth, a teleoperation controller is designed to strengthen haptic force rendering and enhance the operator’s perception of collisions. Going beyond existing methods, the proposed framework allows teleoperated robots to have real-time responses to collisions in quasi-static environments without suffering from local optima, where the environments can be unstructured, non-convex, and detected with noisy outliers. In addition, this framework is simple in implementation because the proposed collision avoidance and collision adaptation algorithms work as several linear Quadratic Programming (QP) constraints that can be flexibly used by Inverse Kinematics (IK) solvers. Several experiments using 7-Degree of Freedom (DoF) robots are conducted to test and compare the proposed framework with existing methods, demonstrating the effectiveness of our work.

Place, publisher, year, edition, pages
IEEE, 2024
Keywords
Environmental collision detection and reaction, optimization control, point clouds, telerobotics
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-106703 (URN)10.1109/TFUZZ.2023.3290124 (DOI)
Funder
Knowledge Foundation, 10.1109/TFUZZ.2023.3290124
Available from: 2023-06-29 Created: 2023-06-29 Last updated: 2024-01-11Bibliographically approved
Sun, D. & Liao, Q. (2023). A Reactive Approach to Handling Multirobot Collision Based on p-Norm Approximation. IEEE Transactions on Industrial Electronics
Open this publication in new window or tab >>A Reactive Approach to Handling Multirobot Collision Based on p-Norm Approximation
2023 (English)In: IEEE Transactions on Industrial Electronics, ISSN 0278-0046, E-ISSN 1557-9948Article in journal (Refereed) Epub ahead of print
Abstract [en]

In this article, we propose a new method for multi-robot systems to have reactive responses to various collision scenarios in real time. This method contains a novel p-norm approximation-based reactive approach, which allows multiple robots to avoid mutual collisions or to adapt to the collisions with intelligently regulated force. Compared with existing approaches, the implementation of the proposed method is simpler and more convenient since our reactive approach works as several linear Quadratic Programming (QP) constraints, allowing for flexible utilization by Inverse Kinematics (IK) solvers. In addition, it requires low computational complexity, achieves high accuracy, and does not need training. In the experiments, we employ a multi-robot system to conduct comprehensive comparisons between the proposed method and state-of-the-art approaches, effectively showcasing the efficacy of our work.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Collision adaptation, collision avoidance, multirobot system
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-109628 (URN)10.1109/TIE.2023.3331141 (DOI)001119858600001 ()
Funder
Knowledge Foundation, 20210016
Available from: 2023-11-08 Created: 2023-11-08 Last updated: 2024-01-10Bibliographically approved
Liao, Q., Sun, D., Zhang, S., Loutfi, A. & Andreasson, H. (2023). Fuzzy Cluster-based Group-wise Point Set Registration with Quality Assessment. IEEE Transactions on Image Processing, 32, 550-564
Open this publication in new window or tab >>Fuzzy Cluster-based Group-wise Point Set Registration with Quality Assessment
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2023 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 32, p. 550-564Article in journal (Refereed) Published
Abstract [en]

This article studies group-wise point set registration and makes the following contributions: "FuzzyGReg", which is a new fuzzy cluster-based method to register multiple point sets jointly, and "FuzzyQA", which is the associated quality assessment to check registration accuracy automatically. Given a group of point sets, FuzzyGReg creates a model of fuzzy clusters and equally treats all the point sets as the elements of the fuzzy clusters. Then, the group-wise registration is turned into a fuzzy clustering problem. To resolve this problem, FuzzyGReg applies a fuzzy clustering algorithm to identify the parameters of the fuzzy clusters while jointly transforming all the point sets to achieve an alignment. Next, based on the identified fuzzy clusters, FuzzyQA calculates the spatial properties of the transformed point sets and then checks the alignment accuracy by comparing the similarity degrees of the spatial properties of the point sets. When a local misalignment is detected, a local re-alignment is performed to improve accuracy. The proposed method is cost-efficient and convenient to be implemented. In addition, it provides reliable quality assessments in the absence of ground truth and user intervention. In the experiments, different point sets are used to test the proposed method and make comparisons with state-of-the-art registration techniques. The experimental results demonstrate the effectiveness of our method.The code is available at https://gitsvn-nt.oru.se/qianfang.liao/FuzzyGRegWithQA

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Quality assessment, Measurement, Three-dimensional displays, Registers, Probability distribution, Point cloud compression, Optimization, Group-wise registration, registration quality assessment, joint alignment, fuzzy clusters, 3D point sets
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-102755 (URN)10.1109/TIP.2022.3231132 (DOI)000908058200002 ()
Funder
Vinnova, 2019- 05878Swedish Research Council Formas, 2019-02264
Available from: 2022-12-16 Created: 2022-12-16 Last updated: 2023-04-03Bibliographically approved
Liao, Q., Sun, D. & Andreasson, H. (2022). FuzzyPSReg: Strategies of Fuzzy Cluster-based Point Set Registration. IEEE Transactions on robotics, 38(4), 2632-2651
Open this publication in new window or tab >>FuzzyPSReg: Strategies of Fuzzy Cluster-based Point Set Registration
2022 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 38, no 4, p. 2632-2651Article in journal (Refereed) Published
Abstract [en]

This paper studies the fuzzy cluster-based point set registration (FuzzyPSReg). First, we propose a new metric based on Gustafson-Kessel (GK) fuzzy clustering to measure the alignment of two point clouds.  Unlike the metric based on fuzzy c-means (FCM) clustering in our previous work, the GK-based metric includes orientation properties of the point clouds, thereby providing more information for registration. We then develop the registration quality assessment of the GK-based metric, which is more sensitive to small misalignments than that of the FCM-based metric. Next, by effectively combining the two metrics, we design two FuzzyPSReg strategies with global optimization: i). \textit{FuzzyPSReg-SS}, which extends our previous work and aligns two similar-sized point clouds with greatly improved efficiency; ii). \textit{FuzzyPSReg-O2S}, which aligns two point clouds with a relatively large difference in size and can be used to estimate the pose of an object in a scene. In the experiment, we use different point clouds to test and compare the proposed method with state-of-the-art registration approaches. The results demonstrate the advantages and effectiveness of our method.

Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2022
Keywords
point set registration, fuzzy clusters, registration quality assessment, 3D point clouds, object pose estimation.
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-95245 (URN)10.1109/TRO.2021.3123898 (DOI)000732336500001 ()2-s2.0-85129800930 (Scopus ID)
Funder
Vinnova, 2019-05878 2020-04483Swedish Research Council Formas, 2019-02264
Available from: 2021-10-27 Created: 2021-10-27 Last updated: 2022-09-12Bibliographically approved
Sun, D., Liao, Q. & Loutfi, A. (2022). Type-2 Fuzzy Model-based Movement Primitives for Imitation Learning. IEEE Transactions on robotics, 38(4), 2462-2480
Open this publication in new window or tab >>Type-2 Fuzzy Model-based Movement Primitives for Imitation Learning
2022 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 38, no 4, p. 2462-2480Article in journal (Refereed) Published
Abstract [en]

Imitation learning is an important direction in the area of robot skill learning. It provides a user-friendly and straightforward solution to transfer human demonstrations to robots. In this article, we integrate fuzzy theory into imitation learning to develop a novel method called Type-2 Fuzzy Model-based Movement Primitives (T2FMP).In this method, a group of data-driven Type-2 fuzzy models are used to describe the input-output relationships of demonstrations. Based on the fuzzy models, T2FMP can efficiently reproduce the trajectory without high computational costs or cumbersome parameter settings. Besides, it can well handle the variation of the demonstrations and is robust to noise. In addition, we develop extensions that endow T2FMP with trajectory modulation and superposition to achieve real-time trajectory adaptation to various scenarios. Going beyond existing imitation learning methods, we further extend T2FMP to regulate the trajectory to avoid collisions in the environment that is unstructured, non-convex, and detected with noisy outliers. Several experiments are performed to validate the effectiveness of our method.

Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2022
Keywords
Imitation learning, Movement primitives, Type-2 fuzzy model, Point cloud, Collision avoidance
National Category
Robotics
Research subject
Computer Engineering
Identifiers
urn:nbn:se:oru:diva-97569 (URN)10.1109/TRO.2022.3152685 (DOI)000767854700001 ()2-s2.0-85126290381 (Scopus ID)
Funder
Vinnova, 2020-04483
Available from: 2022-02-16 Created: 2022-02-16 Last updated: 2022-10-06Bibliographically approved
Sun, D. & Liao, Q. (2021). Asymmetric Bilateral Telerobotic System with Shared Autonomy Control. IEEE Transactions on Control Systems Technology, 29(5), 1863-1876
Open this publication in new window or tab >>Asymmetric Bilateral Telerobotic System with Shared Autonomy Control
2021 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 29, no 5, p. 1863-1876Article in journal (Refereed) Published
Abstract [en]

The asymmetry in bilateral teleoperation, i.e., the differences of mechanical structures, sizes, and number of joints between the master and slave robots, can introduce kinematicsr edundancy and workspace inequality problems. In this article, a novel shared autonomy control strategy is proposed for handling the asymmetry of bilateral teleoperation, which has two main contributions. First, to deal with kinematics redundancy, the proposed strategy provides a self-regulation algorithm of orientation that allows the operator to solely use the master position command to simultaneously control the slave’s position and orientation. Second, to deal with workspace inequality, the proposed strategy enables the slave’s workspace to be dynamically tunable to adapt to various task spaces without influencing the smoothness of the robot’s movement. The experiments on a platform consisting of a six-degree of freedom (DoF) UR10 robot and a 3-DoF haptic device are given to validate the effectiveness of the proposed control strategy.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Asymmetric bilateral teleoperation (ABT), human–machine interaction, orientation regulation, shared autonomy, workspace mapping
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-84964 (URN)10.1109/TCST.2020.3018426 (DOI)000682140300003 ()2-s2.0-85091685820 (Scopus ID)
Note

Funding agency:

AI.MEE Program: AutoDIVE

Available from: 2020-08-19 Created: 2020-08-19 Last updated: 2021-08-31Bibliographically approved
Adolfsson, D., Magnusson, M., Liao, Q., Lilienthal, A. & Andreasson, H. (2021). CorAl – Are the point clouds Correctly Aligned?. In: 10th European Conference on Mobile Robots (ECMR 2021): . Paper presented at 10th European Conference on Mobile Robots (ECMR 2021), Bonn, Germany, (Online Conference), August 31 - September 3, 2021. IEEE, 10
Open this publication in new window or tab >>CorAl – Are the point clouds Correctly Aligned?
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2021 (English)In: 10th European Conference on Mobile Robots (ECMR 2021), IEEE, 2021, Vol. 10Conference paper, Published paper (Refereed)
Abstract [en]

In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods.

Place, publisher, year, edition, pages
IEEE, 2021
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-94464 (URN)10.1109/ECMR50962.2021.9568846 (DOI)000810510000059 ()
Conference
10th European Conference on Mobile Robots (ECMR 2021), Bonn, Germany, (Online Conference), August 31 - September 3, 2021
Funder
Knowledge FoundationEU, Horizon 2020, 732737 101017274
Available from: 2021-09-22 Created: 2021-09-22 Last updated: 2024-01-02Bibliographically approved
Liao, Q., Sun, D. & Andreasson, H. (2021). Point Set Registration for 3D Range Scans Using Fuzzy Cluster-based Metric and Efficient Global Optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3229-3246
Open this publication in new window or tab >>Point Set Registration for 3D Range Scans Using Fuzzy Cluster-based Metric and Efficient Global Optimization
2021 (English)In: IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 43, no 9, p. 3229-3246Article in journal (Refereed) Published
Abstract [en]

This study presents a new point set registration method to align 3D range scans. In our method, fuzzy clusters are utilized to represent a scan, and the registration of two given scans is realized by minimizing a fuzzy weighted sum of the distances between their fuzzy cluster centers. This fuzzy cluster-based metric has a broad basin of convergence and is robust to noise. Moreover, this metric provides analytic gradients, allowing standard gradient-based algorithms to be applied for optimization. Based on this metric, the outlier issues are addressed. In addition, for the first time in rigid point set registration, a registration quality assessment in the absence of ground truth is provided. Furthermore, given specified rotation and translation spaces, we derive the upper and lower bounds of the fuzzy cluster-based metric and develop a branch-and-bound (BnB)-based optimization scheme, which can globally minimize the metric regardless of the initialization. This optimization scheme is performed in an efficient coarse-to-fine fashion: First, fuzzy clustering is applied to describe each of the two given scans by a small number of fuzzy clusters. Then, a global search, which integrates BnB and gradient-based algorithms, is implemented to achieve a coarse alignment for the two scans. During the global search, the registration quality assessment offers a beneficial stop criterion to detect whether a good result is obtained. Afterwards, a relatively large number of points of the two scans are directly taken as the fuzzy cluster centers, and then, the coarse solution is refined to be an exact alignment using the gradient-based local convergence. Compared to existing counterparts, this optimization scheme makes a large improvementin terms of robustness and efficiency by virtue of the fuzzy cluster-based metric and the registration quality assessment. In the experiments, the registration results of several 3D range scan pairs demonstrate the accuracy and effectiveness of the proposed method, as well as its superiority to state-of-the-art registration approaches.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Point Set Registration, Computer Vision, fuzzy clusters, registration quality assessment, 3D range scans, branch-and-bound
National Category
Computer Systems Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-80214 (URN)10.1109/TPAMI.2020.2978477 (DOI)000681124300028 ()32149624 (PubMedID)2-s2.0-85111989740 (Scopus ID)
Note

Funding agency:

Semantic Robots Research Profile - Swedish Knowledge Foundation (KKS)

Available from: 2020-02-26 Created: 2020-02-26 Last updated: 2021-08-23Bibliographically approved
Sun, D., Kiselev, A., Liao, Q., Stoyanov, T. & Loutfi, A. (2020). A New Mixed Reality - based Teleoperation System for Telepresence and Maneuverability Enhancement. IEEE Transactions on Human-Machine Systems, 50(1), 55-67
Open this publication in new window or tab >>A New Mixed Reality - based Teleoperation System for Telepresence and Maneuverability Enhancement
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2020 (English)In: IEEE Transactions on Human-Machine Systems, ISSN 2168-2305, Vol. 50, no 1, p. 55-67Article in journal (Refereed) Published
Abstract [en]

Virtual Reality (VR) is regarded as a useful tool for teleoperation system that provides operators an immersive visual feedback on the robot and the environment. However, without any haptic feedback or physical constructions, VR-based teleoperation systems normally have poor maneuverability and may cause operational faults in some fine movements. In this paper, we employ Mixed Reality (MR), which combines real and virtual worlds, to develop a novel teleoperation system. New system design and control algorithms are proposed. For the system design, a MR interface is developed based on a virtual environment augmented with real-time data from the task space with a goal to enhance the operator’s visual perception. To allow the operator to be freely decoupled from the control loop and offload the operator’s burden, a new interaction proxy is proposed to control the robot. For the control algorithms, two control modes are introduced to improve long-distance movements and fine movements of the MR-based teleoperation. In addition, a set of fuzzy logic based methods are proposed to regulate the position, velocity and force of the robot in order to enhance the system maneuverability and deal with the potential operational faults. Barrier Lyapunov Function (BLF) and back-stepping methods are leveraged to design the control laws and simultaneously guarantee the system stability under state constraints.  Experiments conducted using a 6-Degree of Freedom (DoF) robotic arm prove the feasibility of the system.

Place, publisher, year, edition, pages
IEEE, 2020
Keywords
Force control, motion regulation, telerobotics, virtual reality
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-77829 (URN)10.1109/THMS.2019.2960676 (DOI)000508380700005 ()2-s2.0-85077905008 (Scopus ID)
Funder
Knowledge Foundation
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2020-03-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8119-0843

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