Haiyan Wu

Bio: Haiyan Wu is an academic researcher from Technische Universität München. The author has contributed to research in topics: Visual servoing & Control system. The author has an hindex of 9, co-authored 21 publications receiving 318 citations. Previous affiliations of Haiyan Wu include Technical University of Denmark.

Cloud-Based Networked Visual Servo Control

Abstract: The performance of vision-based control systems, in particular, of highly dynamic vision-based motion control systems, is often limited by the low sampling rate of the visual feedback caused by the long image processing time. In order to overcome this problem, the networked visual servo control (NVSC), which integrates networked computational resources for cloud image processing, is considered in this paper. The main contributions of this paper are the following: 1) a real-time transport protocol for transmitting large-volume image data on a cloud-computing platform, which enables high-sampling-rate visual feedback; 2) a stabilizing control law for the NVSC system with time-varying feedback time delay; and 3) a sending rate scheduling strategy aiming at reducing the communication network load. The performance of the NVSC system with sending rate scheduling is validated in an object-tracking scenario on a 14-DOF dual-arm robot. Experimental results show the superior performance of our approach. In particular, the communication network load is substantially reduced by means of the scheduling strategy without performance degradation.

An FPGA implementation of insect-inspired motion detector for high-speed vision systems

Abstract: In this paper, an array of biologically inspired elementary motion detectors (EMDs) is implemented on an FPGA (field programmable gate array) platform. The well-known Reichardt-type EMD, modeling the insect's visual signal processing system, is very sensitive to motion direction and has low computational cost. A modified structure of EMD is used to detect local optical flow. Six templates of receptive fields, according to the fly's vision system, are designed for simple ego-motion estimation. The results of several typical experiments demonstrate local detection of optical flow and simple motion estimation under specific backgrounds. The performance of the real-time implementation is sufficient to deal with a video frame rate of 350 fps at 256 times 256 pixels resolution. The execution of the motion detection algorithm and the resulting time delay is only 0.25 mus. This hardware is suited for obstacle detection, motion estimation and UAV/MAV attitude control.

Dynamic manipulation: Nonprehensile ball catching

Abstract: Most industrial robots nowadays still employ strategies that neglect or minimize the effects of task dynamics. Some tasks, however, are intrinsically dynamic and can only be accomplished by considering their dynamic aspects. We address ball catching as a prominent and widely studied example for such a task. The paper follows a special approach to accomplish the task: the nonprehensile catching, which means catching without a form- or force-closure grasp. Depending on the tracked ball velocity, two different catching methods are proposed: First, catching of the ball during the initial contact. Second, catching the ball after an initial rebounce during the subsequent contact. For both approaches, the ball trajectory is predicted with a recursive least squares algorithm. The dynamic manipulability measure is used for the contact point selection. Once a permanent contact between ball and end effector is established, a balancing control based on force/torque feedback is applied. Both methods are experimentally validated using a six DoF industrial robot.

Ping-pong robotics with high-speed vision system

Abstract: The performance of vision-based control is usually limited by the low sampling rate of the visual feedback. We address Ping-Pong robotics as a widely studied example which requires high-speed vision for highly dynamic motion control. In order to detect a flying ball accurately and robustly, a multi-threshold segmentation algorithm is applied in a stereo-vision running at 150Hz. Based on the estimated 3D ball positions, a novel two-phase trajectory prediction is exploited to determine the hitting position. Benefiting from the high-speed visual feedback, the hitting position and thus the motion planning of the manipulator are updated iteratively with decreasing error. Experiments are conducted on a 7 degrees of freedom humanoid robot arm. A successful Ping-Pong playing between the robot arm and human is achieved with a high successful rate of 88%.

Hand-Eye Calibration and Inverse Kinematics of Robot Arm Using Neural Network

Abstract: Traditional technologies for solving hand-eye calibration and inverse kinematics are cumbersome and time consuming due to the high nonlinearity in the models. An alternative to the traditional approaches is the artificial neural network inspired by the remarkable abilities of the animals in different tasks. This paper describes the theory and implementation of neural networks for hand-eye calibration and inverse kinematics of a six degrees of freedom robot arm equipped with a stereo vision system. The feedforward neural network and the network training with error propagation algorithm are applied. The proposed approaches are validated in experiments. The results indicate that the hand-eye calibration with simple neural network outperforms the conventional method. Meanwhile, the neural network exhibits a promising performance in solving inverse kinematics.

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.

Data-Based Techniques Focused on Modern Industry: An Overview

Abstract: This paper provides an overview of the recent developments in data-based techniques focused on modern industrial applications. As one of the hottest research topics for complicated processes, the data-based techniques have been rapidly developed over the past two decades and widely used in numerous industrial sectors nowadays. The core of data-based techniques is to take full advantage of the huge amounts of available process data, aiming to acquire the useful information within. Compared with the well-developed model-based approaches, data-based techniques provide efficient alternative solutions for different industrial issues under various operating conditions. The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view. This paper begins with a brief evolutionary overview of data-based techniques in the last two decades. Then, the methodologies only based on process measurements and the model-data integrated techniques will be further introduced. The recent developments for modern industrial applications are, respectively, presented mainly from perspectives of monitoring and control. The new trends of data-based technique as well as potential application fields are finally discussed.

Modelling and Control of Robot Manipulators

Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.

Predictive Output Feedback Control for Networked Control Systems

Abstract: This paper studies the problem of predictive output feedback control for networked control systems (NCSs) with random communication delays A networked-predictive-control scheme is employed to compensate for the network-induced delay Furthermore, the time-varying predictive controller with mixed random delays for networked systems is introduced Then, the system is formulated as a Markovian jump system New techniques are presented to deal with the distributed delay in the discrete-time domain Based on the analysis of closed-loop NCSs, the designed predictive time-varying output feedback controller can guarantee system stability Simulation example demonstrates the compensation for random communication delays and data loss in networked systems using the proposed predictive scheme

Cooperative quadrocopter ball throwing and catching

Abstract: This paper presents a method for enabling a fleet of circularly arranged quadrocopters to throw and catch balls with a net. Based on a first-principles model of the net forces, nominal inputs for all involved vehicles are derived for arbitrary target trajectories of the net. Two algorithms that generate open-loop trajectories for throwing and catching a ball are also introduced. A set of throws and catches is demonstrated in the ETH Zurich Flying Machine Arena testbed.