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The bin picking system integrates manipulators with cameras to automatically pick randomly piled objects. It has the commercial potential to help streamline production and is evaluated by its work efficiency and purchase cost. For universal object picking, the system requires the manipulator to cover six degrees of freedom (DoFs). If the picking objects have a uniform shape, this requirement may be relaxed to achieve higher efficiency with cheaper low-DoF manipulators. On the one hand, the pose of revolution-symmetry (RS) objects is 5-DoF for rotating around their revolution axes without changing their poses. On the other hand, the widely used selective compliance assembly robot arm (SCARA) can achieve 4-DoF kinematics due to four joint actuators. Inspired by the fact that the DoF of the RS pose is exactly the same as adding one DoF to SCARA, we develop a SCARA+ system of a SCARA with an additional revolute joint and explore the possibility of integrating it with a 3-D camera to achieve bin picking of RS objects. Toward this end, we first discuss the SCARA+ kinematics with the modified Denavit–Hartenberg (DH) parameters. Then, to calibrate the additional DH and the hand–eye parameters in the kinematics, we construct an axis-point model and provide an iterative solution without singularity. Finally, comprehensive experiments verify the superiority of the SCARA+ system. When compared with the state-of-the-art systems, our system achieves a significant efficiency improvement with relatively lower costs. It has also been successfully applied in the spinning industry for practical bobbin loading.

In this article, the joint estimation of state and noise covariance for linear systems with unknown covariance of multiplicative noise is considered. The measurement likelihood is modeled as a mixture of two Gaussian distributions and a Student's t distribution, respectively. The unknown covariance of multiplicative noise is modeled as an inverse Gamma/Wishart distribution and the initial condition is formulated as the nominal covariance. By using robust design and choosing hierarchical priors, two variational Bayesian-based robust Kalman filters are proposed. The stability and convergence of the proposed filters and the covariance parameters are analyzed. The lower and upper bounds are also provided to guarantee the performance of the proposed filters. A target tracking simulation is provided to validate the effectiveness of the proposed filters.

Hand–eye calibration (HEC) aims to determine the geometrical transformation between the camera and the robot, which is essential for vision-guided robotic (VGR) systems. On the one hand, the classic pose-based and the recent pixel-based HEC methods mainly focus on multipixel research for initializing or solving. However, in these multimarker cases, additional connector assembly and wide coverage area are not user-friendly in practical data collection operations. Furthermore, both accuracy and efficiency are not ideal due to the transition calculation of multimarker poses. On the other hand, with the development of 3-D sensing technology, 3-D position data can be obtained in increasing scenes. However, HEC research on 3-D observation is relatively insufficient, and previous position-based formulations ignore the relationship among parameters. Motivated by the above shortcomings, this article thoroughly investigates the hand–eye parameter estimation based on 3-D observation of a single marker. First, a uniform single-marker formulation is proposed. This formulation is unique in the optimization sense without factor-extraction variants and covers both the eye-in-hand and eye-to-hand configurations. Then, an analytical solution and an iterative solution are derived through different rotation treatments. It is worth noting that these solutions are in a consistent and compact form due to the precalculated variables and the equivalent transformation. Meanwhile, the solvability, estimation accuracy, and computational efficiency are discussed. Finally, comprehensive simulations and real-world experiments are provided to demonstrate the advantages of the proposed method over previous methods in terms of accuracy, computational efficiency, and operational efficiency. The codes and datasets are open source at: https://github.com/MatthewJin001/Single3D and https://github.com/MatthewJin001/3Ddata, respectively.

This article considers the problem of distributed robust state estimation for sensor networks in the presence of model uncertainty and multiplicative noise. More precisely, we assume that the modeling uncertainty, i.e., the actual state space model belongs to an ambiguity set or a set of convex polytopic uncertain parameters. Several robust Kalman filters are proposed based on projection theorem, variance-constrained optimization, and robust mean square error estimation with different types of ambiguity sets. Stability analysis and simulation example verify the presented distributed robust filters.