In chemical laboratories, robot operation of instruments often relies on structured auxiliary positioning and teaching methods, which are complicated and laborious. Moreover, the single color, sparse textures, and uneven scales of instruments make the accuracy and robustness of existing visual 6-Dof pose estimation methods difficult to meet the requirement of robot operation. Therefore, we propose a novel pose estimation approach for automatic chemical laboratories using multi-level template matching to assist robots in operating instruments. This approach matches the query image with templates step by step from three levels: template, image, and pixel. During the matching processes from global to local, it achieves prediction of 2D key-point coordinates to accurately estimate instrument pose. At the same time, a template searching method based on the distribution pattern of feature points is proposed to ensure the accuracy of template searching in the real world. The experimental results show that our approach has good robustness and accuracy and meets the requirement of robot operation in automatic chemical laboratories. Note to Practitioners—This paper was motivated by the positioning problem of robot operation for instruments in automatic chemical laboratories. Structured auxiliary positioning methods are complicated and most of the pose estimation works are not suitable for practical applications at present. This paper suggests a novel template searching method and a pose estimation approach using multi-level matching for instruments. We calculate the distances, angles and point number of feature points to construct a new feature searching the best matched template with the input image. Then the predefined key pixels of the template are transformed into the input image from coarse to fine matching to ensure the accuracy and robustness. Due to the instrument CAD model, we can obtain the 2D-3D correspondences and calculate pose of the instrument by the PnP method. The experiment results validate that our method is suitable for practical robot operation in automatic chemical laboratories.

A quantum control theory of pure state in non-ideal quantum systems is proposed in this paper. A non-ideal closed quantum systems means that the controlled quantum system is in two degenerate cases: one is at least two transition frequencies between different energy levels are the same, or/and another is at least two eigenstates of the internal Hamiltonian are not directly coupled. The implicit Lyapunov control method based on the average value of an imaginary mechanical quantity is used to design the control laws. The design procedure of the imaginary mechanical quantity is derived. The relationships among the implicit Lyapunov control methods based on the state distance, the state error and the average value of an imaginary mechanical quantity are analyzed. Finally, some numerical simulation experiments are studied.

Flame detection is a key module of fire-fighting robots, especially for autonomous fire suppression. To effectively tackle the fire-fighting tasks, fire-fighting robots are usually equipped with multimodal vision systems. On the one hand, cameras of different modalities can provide complementary visual information. On the other hand, the differences in installation position and resolution between different cameras also result in weakly aligned image pairs, that is, the positions of the same object in different modal images are inconsistent. Directly fusing the image features of different modalities is difficult to meet the accuracy and false alarm requirements of fire-fighting robots. Therefore, we propose a multimodal flame detection model based on projection and attention guidance. First, we use projection to obtain the approximate position of the flame in the thermal image and employ a neighbor sampling module to detect flames around it. Second, we design an attention guidance module based on index matching, which applies the attention map generated by the thermal modality to optimize the regional feature of the color modality. Experiments on multimodal datasets collected by an actual fire-fighting robot validate that the proposed method is effective in both fire and nonfire environments.

For stochastic quantum systems with measurement feedback, this article proposes a rapid switching control scheme based on state space partition and realizes the rapid stabilization of an eigenstate of an observable operator. Meanwhile, we apply the proposed scheme to the preparation of typical entangled states in multiqubit systems. In view of the convergence obstacle caused by the symmetric structure of the state space, especially in the case with degenerate observable operators, we first partition the state space into a subset containing the target state and its complement to distinguish the target state from its antipodal points, and then design the corresponding control laws in these two subsets, respectively, by using different Lyapunov functions. The interaction Hamiltonians are also constructed to drive the system state to the desired subset first, and further to the target state. In particular, the control law designed in the undesired subset guarantees the strictly monotonic descent of the corresponding Lyapunov function, which makes the system trajectory switch between the two subsets at most twice and has the potential to speed up the convergence process. We also prove the stability of the closed-loop system with the proposed switching control law based on the stochastic Lyapunov stability theory. By applying the proposed switching control scheme to a three-qubit system, we achieve the preparation of a GHZ state and a W state.

Robotic grasping ability lags far behind human skills and poses a significant challenge in the robotics research area. According to the grasping part of an object, humans can select the appropriate grasping postures of their fingers. When humans grasp the same part of an object, different poses of the palm will cause them to select different grasping postures. Inspired by these human skills, in this article, we propose new grasping posture prediction networks (GPPNs) with multiple inputs, which acquire information from the object image and the palm pose of the dexterous hand to predict appropriate grasping postures. The GPPNs are further combined with grasping rectangle detection networks (GRDNs) to construct multilevel convolutional neural networks (ML-CNNs). In this study, a force-closure index was designed to analyze the grasping quality, and force-closure grasping postures were generated in the GraspIt! environment. Depth images of objects were captured in the Gazebo environment to construct the dataset for the GPPNs. Herein, we describe simulation experiments conducted in the GraspIt! environment, and present our study of the influences of the image input and the palm pose input on the GPPNs using a variable-controlling approach. In addition, the ML-CNNs were compared with the existing grasp detection methods. The simulation results verify that the ML-CNNs have a high grasping quality. The grasping experiments were implemented on the Shadow hand platform, and the results show that the ML-CNNs can accurately complete grasping of novel objects with good performance.

Achieving high-precision position control while maintaining positive cable tensions is the most challenging issue for the motion control of cable-driven parallel robots, which should be considered significantly. Different from the existing control schemes with online tension distribution that needs real-time computing in each control cycle, a novel dual-loop dynamic control scheme is proposed in this article, where a paralleled dual-loop tracking strategy is introduced to provide a more compatible scheme, which consists of two tracking loops: 1) the tension control loop and 2) the position control loop. In the former loop, the offline tension distribution is adopted to avoid cable hanging loosely and the real-time feasibility of the distribution method is no longer a necessary demand. In the latter loop, due to the complex dynamics characterized by the cable-driven form, the cooperative motion relation among multiple cables and inevitable external disturbances are investigated comprehensively, and the robust synchronization method is included to guarantee the high-precision position control. Afterward, the Lyapunov method is adopted to analyze the strict stability of the whole closed-loop system with both the position and tension control feedback. The experiments indicate that by synthesizing the two control loops, the proposed scheme can dramatically reduce the tracking errors in the trajectory tracking while avoiding the cable relaxation, and particularly, has a satisfactory control effect when the velocity and acceleration of the trajectory have significant oscillations. Additionally, the strong disturbance rejection ability is also validated via robustness experiments.

To achieve high-accuracy grasping of unknown objects, we present novel multilevel convolutional neural networks (CNNs) for robotic grasping with a parallel gripper or multifingered dexterous hand. The multilevel CNNs include four levels with different structures and functions. The first level is constructed to get the approximate position of the grasped object. The second level aims to obtain the preselected grasping rectangles. The third level is constructed to re-evaluate the preselected grasping rectangles and obtain substantially detailed features with quite a large network, so as to assess each preselected grasping rectangle exactly. By using a selection algorithm, the optimal grasping rectangle can be determined and unknown object grasping can be achieved with a parallel gripper. The purpose of the fourth level is to obtain the finger position distribution to complete the accurate grasping of unknown objects with a multifingered dexterous hand. The test results indicate that, compared to state-of-the-art methods, the proposed multilevel CNNs can greatly increase the precision of the grasping rectangle. Grasping experiments were implemented on a Youbot arm with five degrees of freedom and a Shadow four-fingered dexterous hand. The results show that the multilevel CNNs can determine the optimal grasping rectangle and finger position distribution, thereby achieving high-accuracy grasping of various unknown objects, even under several complex environmental conditions.Note to Practitioners—Robot grasping of objects lags far behind human experiences and poses a significant challenge in the robotics area. To solve it, we present new multilevel convolutional neural networks (CNNs) to process red green blue-depth (RGB-D) images and realize optimal grasping detection of unknown objects. Moreover, we provide details of the network structure, network training, and network testing. The testing results obtained from the open grasping data set show that the multilevel CNNs can significantly increase the accuracy of the grasping rectangle compared to state-of-the-art methods. Experiments were implemented on different robotic platforms, including a five-degrees-of-freedom Youbot arm with a parallel gripper and a UR5 robot arm with a Shadow multifingered dexterous hand. The results validate that the multilevel CNNs offer excellent generalization and robustness for handling different sizes and shapes of unknown objects, as well as background disturbances, which are key problems in robotic manipulation.

Cable-driven parallel robots (CDPRs) are a new type of parallel robots that use cables to control a mobile platform. They possess several advantages, including large workspace, low inertia, and high payload capacity. However, there are several problems in the high-precision trajectory tracking control of CDPRs. On the one hand, all the cables must remain in tension during the entire motion process. On the other hand, the controller design is subjected to model uncertainties and external disturbances. Accordingly, this article proposes a robust synchronization control (RSC) scheme in the cable length space to achieve high-precision trajectory tracking. The synchronization control ensures motion coordination among all the cables and prevents cable relaxation, whereas the robust control eliminates modeling errors and restrains external disturbances. The uniformly ultimate boundedness of the tracking and synchronization errors in the closed-loop system equation was proved using the Lyapunov theory. Simulations and experiments of the trajectory tracking control were both implemented on a three-degree-of-freedom CDPR. Compared with the adaptive robust control scheme and the augmented proportional derivative scheme on the premise of the approximate energy consumption, the proposed RSC scheme could reduce not only the tracking errors of the cables but also the synchronization errors between adjacent cables. Moreover, the RSC scheme could significantly improve the trajectory tracking accuracy of the mobile platform. The robustness of this scheme was verified using load experiments and torque-disturbance experiments.

A pure or nearly pure quantum state can be described as a low-rank density matrix, which is a positive semidefinite and unit-trace Hermitian. We consider the problem of recovering such a low-rank density matrix contaminated by sparse components, from a small set of linear measurements. This quantum state estimation task can be formulated as a robust principal component analysis (RPCA) problem subject to positive semidefinite and unit-trace Hermitian constraints. We propose an efficient and fast inexact alternating direction method of multipliers (I-ADMM), in which the subproblems are solved inexactly and hence have closed-form solutions. We prove global convergence of the proposed I-ADMM, and the theoretical result provides a guideline for parameter setting. Numerical experiments show that the proposed I-ADMM can recover state density matrices of 5 qubits on a laptop in 0.69 s, with 6 × 10-4accuracy (99.38% fidelity) using 30% compressive sensing measurements, which outperforms existing algorithms.

The Greenberger-Horne-Zeilinger (GHZ) entangled states are a typical class of entangled states in multiparticle systems and play an important role in the applications of quantum communication and quantum computation. For a general quantum system of qubits, degenerate measurement operators are often met, which cause the convergence obstacle in the state preparation or stabilization problem. This paper first generalizes the traditional quantum state continuous reduction theory to the case of a degenerate measurement operator and chooses a measurement operator for an arbitrarily given target GHZ entangled state, then presents a state stabilization control strategy based on the Lyapunov method and achieves the feedback preparation of the target GHZ state. In our stabilization strategy, we separate the target GHZ state and all the other GHZ states that often form the equilibrium points of the closed-loop system by dividing the state space into several different regions; and formally design a switching control law between the regions, which contains the control Hamiltonians to be constructed. By analyzing the stability of the closed-loop system in the different regions, we propose a systematic method for constructing the control Hamiltonians and solve the convergence problem caused by the degenerate measurement operator. The global stability of the whole closed-loop stochastic system is strictly proved. Also, we perform some simulation experiments on a three-qubit system and prepare a three-qubit GHZ entangled state. At the same time, the simulation results show the effectiveness of the switching control law and the construction method for the control Hamiltonians proposed in this paper.

This paper addresses the dynamic trajectory planning of a spatial cable-suspended parallel robot with three cables and three-degree-of-freedom. A new s - s̈ plane (a is the path parameter) method is presented to devise dynamically feasible point-to-point trajectories and periodic trajectories that are not fully located in the static workspace (SW) of the robot. First, the unilateral cable tension constraints are explicitly converted into geometry constraints in the s - s̈ plane. Then, a set of reachable workspaces is defined, which can be obtained analytically and the volumes of which are all much larger than the volume of the SW. By designing dynamic point-to-point trajectories directly in the s - s̈ plane, any points in the reachable workspaces can be reached in sequence via some intermediate points in the SW. The s - s̈ plane method also offers insights into planning periodic circular trajectory and transition trajectory for oscillations along a straight line, which is considered to be more efficient than the algebraic method provided in the literature. The proposed method always guarantees positive and continuous cable tensions and yields analytical results. The performance of the method is evaluated through numerical simulations and experiments.

In this paper, we consider the recovery of piecewise sparse signals from incomplete noisy measurements via a greedy algorithm. Here piecewise sparse means that the signal can be approximated in certain domain with known number of nonzero entries in each piece/segment. This paper makes a two-fold contribution to this problem: 1) formulating a piecewise sparse model in the framework of compressed sensing and providing the theoretical analysis of corresponding sensing matrices; 2) developing a greedy algorithm called piecewise orthogonal matching pursuit (POMP) for the recovery of piecewise sparse signals. Experimental simulations verify the effectiveness of the proposed algorithms.

This paper presents a task space coordination controller for the parallel manipulators with actuation redundancy to improve the motion relation between multiple kinematic chains. According to the mechanism characteristic of multiple kinematic chains, two different types of synchronization error are developed in the joint space of active joints and in the task space of end-effector, respectively. The coordination controller is designed by using the synchronization error, and it is proved to guarantee asymptotic convergence to zero of both tracking error and synchronization error with the Barbalat's Lemma. The trajectory tracking experiments are carried out on an actual parallel manipulator with actuation redundancy, and the superiority of the coordination controller over the traditional augmented PD (APD) controller is studied.

According to the principle of mechanics, a new adaptive inertia weight strategy is proposed. The strategy depends on particle's search states including its location and velocity instead of iteration times. Based on the proposed strategy, an inertia weight function is designed, which is continuous in real domain, thus it's easy to be implemented and the computation cost is low. Experiments on three benchmark functions, comparison between convergence speed, the ability to search the global solution of the linear decreasing strategy (LPOS) and the proposed strategy are done. The experimental results are also analyzed in detail.