To Örebro University

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

In order to generate trajectories continuously for serial manipulators with high dimensional degrees of freedom (DOFs) in a dynamic environment, a real-time trajectory planning method based on optimization and machine learning aimed at high dimensional inputs is presented. A learning optimization (LO) framework is established. Multiple criteria are defined to evaluate the performance quantitatively, and implementations with different sub-methods are discussed. In particular, a database generation method based on input space mapping is proposed for generating valid and representative samples. The methods presented are applied on a practical application-haptic interaction in virtual reality systems. The results show that the input space mapping method significantly elevates the efficiency and quality of database generation and consequently improves the performance of the LO. With the LO method, real-time trajectory generation with high dimensional inputs is achieved, which lays the foundation for robots with high dimensional DOFs to execute complex tasks in dynamic environments.

In order to let multiple robot manipulators cooperatively complete a sequence of tasks in a shared workspace under task execution uncertainty, this letter proposes a multi-robot task allocation framework for constantly assigning tasks to robots, while the interference among concurrent robot motions is account for. An online sequential task assignment method is presented, which decouples the time-extended problem into a sequence of synchronous and asynchronous instantaneous assignment sub-problems. This renders the approach capable of reacting to task execution uncertainties in real-time. A one-step-ahead simulation method is employed to reduce the idle time of robots and improve task completion efficiency. Each instantaneous assignment sub-problem is modeled as an optimal assignment problem with variable utility and solved by a branch-and-bound algorithm, with which multi-robot motion coordination is integrated. Experimental results conducted with three Franka-Emika Panda arms show that these can cooperatively complete all tasks without collision and little waiting time. Simulations with larger multi-robot systems show that the approach scales linearly with the number of robots.

In order to perform safe and natural interactions with humans, robots are required to adjust their motions quickly according to human behaviors. Performing the complex calculation and updating the trajectories in real-time is a particular challenge. In this paper, we present a real-time optimization-based trajectory planning method for serial robots. We encode the trajectory planning problem into a series of optimization problems. To solve the high-dimensional complex non-linear optimization problems in real-time, we provide a joint-decoupling method that transforms the original joint-coupled optimization problem into multiple joint-independent optimization problems, with much lower computational complexity. We implement and validate our method in a specific human-robot interaction case. Experimental results show that the computational feasibility and efficiency of optimization solution were greatly improved by the joint-decoupling transformation. Smooth, safe, and rapid motion of the robot was generated in real-time, establishing a basis for safe and reactive human-robot interactions.

To perform specific tasks in dynamic environments, robots are required to rapidly update trajectories according to changing factors. A continuous trajectory planning methodology for serial manipulators based on non-convex global optimization is presented in this paper. First, a kinematic trajectory planning model based on non-convex optimization is constructed to balance motion rapidity and safety. Then, a model transformation method for the non-convex optimization model is presented. In this way, the accurate global solution can be obtained with an iterative solver starting from arbitrary initializations, which can greatly improve the computational accuracy and efficiency. Furthermore, an efficient initialization method for the iterative solver based on multivariable-multiple regression is presented, which further speeds up the solution process. The results show that trajectory planning efficiency is significantly enhanced by model transformation and initialization improvement for the iterative solver. Consequently, real-time continuous trajectory planning for serial manipulators with many degrees of freedom can be achieved, which lays a basis for performing dynamic tasks in complex environments.

To obtain real-time haptic interactions in virtual cockpit systems (VCSs), a real-time trajectory planning method based on kinematical optimization for haptic feedback manipulators (HFMs) is presented in this paper. Firstly, the control panel area is extracted in the workspace of the HFM, in which the interacting point is located. Then a feasible interacting configuration is calculated as the objective configuration of the trajectory encoded by a parametric representation. The trajectory planning problem is formulated as a non-linear optimization problem based on kinematics, which is solved in real-time by finding a good initial solution with machine learning methods. Simulations show that trajectories with a compromise between safety and rapidity can be calculated in real-time by this method, which provides a basis for haptic interaction in VCSs.

To obtain natural space experience of haptic interaction for users in virtual cockpit systems (VCS), a haptic feedback system and a workspace analysis framework for haptic feedback manipulator (HFM) are presented in this paper. Firstly, improving the classical three-dimensional workspace obtained by the Monte Carlo method, a novel workspace representation method, oriented workspace, is presented, which can indicate both the position and the orientation of the end-effector. Then, aimed at the characters of HFMs, the oriented workspace is divided into the effective workspace and the prohibited area by extracting the control panel area. At last, the effective workspace volume and the control panel area are calculated by the double-directed extremum method, with the accuracy improved by repeatedly adding and extracting boundary points. By simulation, the area in which interactions between the manipulator and users hand performed is determined and accordingly the effective workspace along with its boundary and volume are obtained in a relative high precision, which lay a basis for haptic interaction in VCS.