Li-Chen Fu

Service-Oriented Smart-Home Architecture Based on OSGi and Mobile-Agent Technology

The architecture of a conventional smart home is usually server-centric and thus causes many problems. Mobile devices and dynamic services affect a dynamically changing environment, which can result in very difficult interaction. In addition, how to provide services efficiently and appropriately is always an important issue for a smart home. To solve the problems caused by traditional architectures, to deal with the dynamic environment, and to provide appropriate services, we propose a service-oriented architecture (SOA) for smart-home environments, based on Open Services Gateway Initiative (OSGi) and mobile-agent (MA) technology. This architecture is a peer-to-peer (P2P) model based on multiple OSGi platforms, in which service-oriented mechanisms are used for system components to interact with one another, and MA technology is applied to augment the interaction mechanisms

Robust adaptive decentralized control of robot manipulators

The authors proposes a robust adaptive decentralized control algorithm for trajectory tracking of robot manipulators. The controller is designed based on a Lyapunov method, which consists of a PD (proportional plus derivative) feedback part and a dynamic compensation part. It is shown that, without any prior knowledge of manipulator or payload parameters and possibly under deterioration of parameter variation with time or state-independent input disturbances, the tracking error is bound to converge to zero asymptotically. In particular, the algorithm does not require explicit system parameter estimation and therefore makes the controller structurally simple and computationally easy. Moreover, the controller is implemented in a decentralized manner, i.e. a subcontroller is independently and locally equipped at each joint servoloop. To illustrate the performance of the controller, a numerical simulation example is provided. >

Globally stable robust tracking of nonlinear systems using variable structure control and with an application to a robotic manipulator

The globally stable robust output tracking for a class of nonlinear systems is considered. Based only on the knowledge of the bounds on the uncertainties, a variable structure control (VSC) law is developed under the structure matching assumption. It is shown that the outputs of the closed-loop system asymptotically track given output trajectories despite the uncertainties while maintaining the boundedness of all signals inside the loop. All signals inside the loop are shown to be bounded for all time. To illustrate the efficiency of the controller, the approach is applied to the case of a two degree-of-freedom (DOF) robotic manipulator with variable payload. Numerical simulation results are also provided. >

Nonlinear adaptive control for flexible-link manipulators

As has been realized, the flexible-link manipulators have attracted more and more attention from robot control theorists and/or robot users because of its various potential advantages. But since the control degree-of-freedom is much less than that of the system, many control strategies which succeed in the conventional rigid robot control cannot be directly used in the flexible robot control problems. In this paper, a nonlinear control scheme has been proposed as a solution to these control problems. In particular, to cope with the existing model uncertainty, an adaptive version of this nonlinear control law has been proposed. Stability proof of the overall closed-loop system is then given via Lyapunov analysis. In addition, extensive experimental results are also provided to demonstrate the effectiveness of the proposed controller.

An adaptive control scheme for coordinated multimanipulator systems

The problem of adaptive coordinated control of multiple robot arms transporting an object is addressed. A stable adaptive control scheme for both trajectory tracking and internal force control is presented. Detailed analyses on tracking properties of the object position and velocity and the internal forces exerted on the object are given. It is shown that this control scheme achieves satisfactory tracking performance without using the measurement of contact forces and their derivatives. It can be shown that this scheme can be realized by decentralized implementation to reduce the computational burden. Moreover, some efficient adaptive control strategies can be incorporated to reduce the computational complexity. >

Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home

This paper presents a robust location-aware activity recognition approach for establishing ambient intelligence applications in a smart home. With observations from a variety of multimodal and unobtrusive wireless sensors seamlessly integrated into ambient-intelligence compliant objects (AICOs), the approach infers a single residents interleaved activities by utilizing a generalized and enhanced Bayesian Network fusion engine with inputs from a set of the most informative features. These features are collected by ranking their usefulness in estimating activities of interest. Additionally, each feature reckons its corresponding reliability to control its contribution in cases of possible device failure, therefore making the system more tolerant to inevitable device failure or interference commonly encountered in a wireless sensor network, and thus improving overall robustness. This work is part of an interdisciplinary Attentive Home pilot project with the goal of fulfilling real human needs by utilizing context-aware attentive services. We have also created a novel application called ldquoActivity Maprdquo to graphically display ambient-intelligence-related contextual information gathered from both humans and the environment in a more convenient and user-accessible way. All experiments were conducted in an instrumented living lab and their results demonstrate the effectiveness of the system.

Driver assistance system for lane detection and vehicle recognition with night vision

The objective of this research is to develop a vision-based driver assistance system to enhance the drivers safety in the nighttime. The proposed system performs both lane detection and vehicle recognition. In lane detection, three features including lane markers, brightness, slenderness and proximity are applied to detect the positions of lane markers in the image. On the other hand, vehicle recognition is achieved by using an evident feature which is extracted through three four steps: taillight standing-out process, adaptive thresholding, centroid detection, and taillight pairing algorithm. Besides, an automatic method is also provided to calculate the tilt and the pan of the camera by using the position of vanishing point which is detected in the image by applying Canny edge detection, Hough transform, major straight line extraction and vanishing point estimation. Experimental results for thousands of images are provided to demonstrate the effectiveness of the proposed approach in the nighttime. The lane detection rate is nearly 99%, and the vehicle recognition rate is about 91%. Furthermore, our system can process the image in almost real time.

Human-Centered Robot Navigation—Towards a Harmoniously Human–Robot Coexisting Environment

This paper proposes a navigation algorithm that considers the states of humans and robots in order to achieve harmonious coexistence between them. A robot navigation in the presence of humans and other robots is rarely considered in the field of robotics. When navigating through a space filled with humans and robots with different functions, a robot should not only pay attention to obstacle avoidance and goal seeking, it should also take into account whether it interferes with other people or robots. To deal with this problem, we propose several harmonious rules, which guarantee a safe and smooth navigation in a human-robot environment. Based on these rules, a practical navigation method-human-centered sensitive navigation (HCSN)-is proposed. HCSN considers the fact that both humans and robots have sensitive zones, depending on their security regions or on a humans psychological state. We model these zones as various sensitive fields with priorities, whereby robots tend to yield socially acceptable movements.

Intelligent robot deburring using adaptive fuzzy hybrid position/force control

The overwhelming complexity of the deburring process and imprecise knowledge about robot manipulators leads to a certain control problem. In the paper, a new design of hybrid position/force control of robot manipulators via an adaptive fuzzy control approach is proposed to solve these problems. The control architecture consists of an outer-loop command generator which can automatically determine the robot motion profile to yield the desired chamfering force and an inner-loop adaptive fuzzy hybrid position/force controller which can achieve the desired chamfer depth compliantly as well as the aforementioned command in real time. The proposed adaptive fuzzy controller using B-spline type membership functions can compensate the uncertainties in a much smoother and locally weighted manner and consequently guarantee global stability of closed-loop systems. To demonstrate the effectiveness of the developed work, it is applied to the control of an industrial robot arm for deburring tasks.

A Portable Vision-Based Real-Time Lane Departure Warning System: Day and Night

Lane departure warning systems (LDWS) are an important element in improving driving safety. In this paper, we propose an embedded advanced RISC machines (ARM)-based real-time LDWS. As for software development, an improved lane detection algorithm based on peak finding for feature extraction is used to successfully detect lane boundaries. Then, a spatiotemporal mechanism using the detected lane boundaries is designed to generate appropriate warning signals. As for hardware implementation, a 1-D Gaussian smoother and a global edge detector are adopted to reduce noise effects in the images. By using the developed data transfer channel (DTC) in the reconfigurable field-programmable gate array (FPGA) module, the data transfer rate among the complementary metal-oxide-semiconductor (CMOS) imager module, liquid-crystal display (LCD) display module, and central processing unit (CPU) bus is about 25 frame/s for an image size of 256 times 256. In addition, the proposed departure warning algorithm based on spatial and temporal mechanisms is successfully executed on the presented ARM-based platform. The effectiveness of our system concludes that the lane detection rate is 99.57% during the day and 98.88% at night in a highway environment. The proposed departure mechanisms effectively generate effective warning signals and avoid most false warnings.