To Örebro University

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.

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.

Current path-tracking trajectory planning methods intrinsically suffer from a heavy computational burden, hindering them from modern autonomous robotic applications requiring real-time reactivity. To achieve flexible, accurate, and efficient motions, this article proposes a real-time trajectory planning framework for time-efficient path tracking. First, a receding horizon method is designed that generates local trajectories online while considering the global kinematic constraints. Second, an analytical closed-form method is developed for calculating the local time-minimal trajectory. Third, the models and solutions are established for handling dynamic factors. Compared to existing methods, this method exhibits lower computational complexity and can deal with path changes during motion executions while maintaining tracking accuracy and time efficiency. Experiments using Franka-Emika Panda robots are conducted in handover scenarios involving unpredictable dynamic obstacles and human interventions. The results demonstrate the low computational overhead. The robot flexibly reacts to online path changes, maintaining tracking accuracy while marginally compromising time efficiency.

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.

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

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.

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.

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.

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.

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.