Xuebo Zhang

Bio: Xuebo Zhang is an academic researcher from Nankai University. The author has contributed to research in topics: Mobile robot & Visual servoing. The author has an hindex of 23, co-authored 114 publications receiving 1838 citations. Previous affiliations of Xuebo Zhang include Carnegie Mellon University.

A Motion Planning-Based Adaptive Control Method for an Underactuated Crane System

Abstract: This brief proposes a motion planning-based adaptive control strategy for an underactuated overhead crane system. To improve the transportation efficiency and enhance the safety of the crane system, the trolley is required to reach the desired position fast enough, while the swing of the payload needs to be within an acceptable domain. To achieve these objectives, a novel two-step design strategy consisting of a motion planning stage and an adaptive tracking control design stage, is proposed to control such an underactuated system as an overhead crane. Specifically, a novel desired trajectory, which satisfies physical constraints of an overhead crane, is proposed for the trolley by fusing theoretical analysis results with the conventional empirical trajectory planning methods. An adaptive control law is then constructed in the second step to make the trolley track the planned trajectory, where some online update mechanism is introduced to ensure that the controller works well with different working conditions. As shown by Lyapunov techniques, the proposed adaptive controller guarantees asymptotic tracking result even in the presence of uncertainties including system parameters and various disturbance. Some experiment results demonstrate that the proposed control method achieves superior performance for the underactuated cranes.

Energy coupling output feedback control of 4-DOF underactuated cranes with saturated inputs

Abstract: We propose in the present paper an energy coupling-based output feedback (OFB) control scheme for 4 degrees-of-freedom (4-DOF) overhead cranes under control input constraints. Unlike existing crane control methods, the proposed approach can achieve superior control performance using only trolley-position/payload-swing feedback with saturated control inputs. In particular, a new concept regarding virtual payloads is introduced, together with a novel energy storage function, to successfully explore the characteristics of the crane dynamics. Based on that, an energy coupling OFB control law is proposed by taking the practical input constraints into account, which achieves both precise trolley positioning and efficient payload swing elimination. The corresponding stability analysis is guaranteed by Lyapunov techniques and LaSalle's invariance theorem. Experimental results are presented to illustrate the superior control performance of the proposed scheme.

Adaptive Active Visual Servoing of Nonholonomic Mobile Robots

Abstract: This paper presents a novel two-level scheme for adaptive active visual servoing of a mobile robot equipped with a pan camera. In the lower level, the pan platform carrying an onboard camera is controlled to keep the reference points lying around the center of the image plane. On the higher level, a switched controller is utilized to drive the mobile robot to reach the desired configuration through image feature feedback. The designed active visual servoing system presents such advantages as follows: 1) a satisfactory solution for the field-of-view problem; 2) global high servoing efficiency; and 3) free of any complex pose estimation algorithm usually required for visual servoing systems. The performance of the active visual servoing system is proven by rigorous mathematical analysis. Both simulation and experimental results are provided to validate the effectiveness of the proposed active visual servoing method.

Motion-Estimation-Based Visual Servoing of Nonholonomic Mobile Robots

Abstract: A 2-1/2-D visual servoing strategy, which is based on a novel motion-estimation technique, is presented for the stabilization of a nonholonomic mobile robot (which is also called the “parking problem”) By taking into account the planar motion constraint of mobile robots, the proposed motion-estimation technique can be applied in both planar and nonplanar scenes In addition, this approach requires no matrix estimation or decomposition, and it avoids ambiguity and degeneracy problems for the homography or fundamental matrix-based algorithms Moreover, the field-of-view (FOV) constraint of the onboard camera is largely alleviated because the presented algorithm works well with few feature points In order to incorporate the advantages of position-based visual servoing and image-based visual servoing, a composite error vector is defined that includes both image signals and the estimated rotational angle Subsequently, a smooth time-varying feedback controller is adopted to cope with the nonholonomic constraints, which yields global exponential convergent rate for the closed-loop system On the basis of the perturbed linear system theory, we show that practical exponential stability can be achieved, despite the lack of depth information, which is inherent for monocular camera systems Both simulation and experiment results are collected to investigate the feasibility of the proposed approach

Minimum-Time Trajectory Planning for Underactuated Overhead Crane Systems With State and Control Constraints

Abstract: In this paper, we propose a novel offline minimum-time trajectory planning (MTTP) approach for underactuated overhead cranes. To the best of our knowledge, it is the first optimal solution to the MTTP problem for overhead crane systems, which simultaneously takes into account various constraints, including the bounded swing angle for the payload, bounded velocity, acceleration, and even jerk for the trolley. Different from existing approaches, by means of system discretization and augmentation, the quasi-convex optimization technique is successfully adopted to find the minimum-time solution while satisfying all the aforementioned constraints. Extensive simulation and experiments with comparisons to previously published methods are conducted to show the superior performance of the proposed method. Note that the results derived in this paper also serve as promising guidance in engineering applications, since it provides a performance limit, namely, the possible highest efficiency for automatic or manual operation of overhead cranes.

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Shortcuts to adiabaticity: Concepts, methods, and applications

Abstract: Adiabatic evolution along the instantaneous eigenstate of a time-dependent Hamiltonian is used for robust and high fidelity state transfer in atomic and molecular physics. Shortcuts to adiabaticity (STA) are systematic approaches to accomplish the same final state transfer in a faster manner. This article presents an introduction to STA and reviews different theoretical approaches and applications of STA to a range of scientific and engineering tasks in quantum physics and beyond.

A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos

Abstract: Background subtraction (BS) is a crucial step in many computer vision systems, as it is first applied to detect moving objects within a video stream. Many algorithms have been designed to segment the foreground objects from the background of a sequence. In this article, we propose to use the BMC (Background Models Challenge) dataset, and to compare the 29 methods implemented in the BGSLibrary. From this large set of various BG methods, we have conducted a relevant experimental analysis to evaluate both their robustness and their practical performance in terms of processor/memory requirements.

Cooperative control of a nonuniform gantry crane with constrained tension

Abstract: In this paper, the control problem is addressed for a hybrid PDE-ODE system that describes a nonuniform gantry crane system with constrained tension. A bottom payload hangs from the top gantry by connecting a flexible cable. The flexible cable is nonuniform due to the spatiotemporally varying tension applied to the system. The control objectives are: (i) to position the payload to the desired setpoint, (ii) to regulate the transverse deflection of the flexible cable, and (iii) to keep the tension values remaining in the constrained space. Cooperative control laws are proposed and the integral-barrier Lyapunov functions are employed for stability analysis of the closed-loop system. Adaption laws are developed for handling parametric uncertainties. The bounded stability is guaranteed through rigorous analysis without any simplification of the dynamics. In the end, numerical simulations are displayed to illustrate the performance of the proposed cooperative control.

WiFi-SLAM Using G aussian Process Latent Variable Models

Abstract: WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.