Petri net modelling for robotic assembly and trajectory planning

A new approach to process modelling, task synthesis, motion control and trajectory planning for robotic assembly is presented. Assembly is modelled as a discrete event dynamic system using Petri nets, incorporating both discrete and continuous aspects of the process. A process monitor based on recognizing contact state transitions is presented. A discrete event controller is developed. The controller issues velocity commands that direct the system toward the next desired contact state, while maintaining currently desired contacts and avoiding unwanted transitions. A novel means of trajectory planning which incorporates the system's ability to both monitor and control the process is given. Experimental results are given for a dual peg-in-the-hole example. The experimental results not only demonstrate highly successful insertion along the desired trajectory, but also demonstrate the ability to detect, recognize, and recover from errors and unwanted situations     

Path planning for robotic manipulators with redundant degrees offreedom

The problem of path planning for robotic manipulators with redundant and nonredundant degrees of freedom is addressed. It is assumed that the motors for each joint are capable of achieving the commanded velocity within limits. Thus, the dynamic model is simplified and the main complexity is that of the kinematic relationships. Of primary interest is the problem of moving the end effector from point A to point B in an efficient manner, possibly in the presence of obstacles. A suboptimal solution is proposed and discussed. Examples are presented in order to compare the performance of the redundant and the nonredundant manipulators     

广义AND/OR图的自底向上的启发式搜索算法BFA0

本文首先根据三角模及其扩张运算概念,定义了一类新的更具普遍意义的模糊广义AND/OR图。根据新定义的启发式函数h(n,x)以及模糊广义AND/OR图的最佳解树之所有子树亦是最佳子解树的原理,提出了自底向上的启发式搜索算法BFAO·。文中证明了算法BFAO·的可采纳性。本文还提出了两类新的启发式函数的单调限制概念,并据此研究了算法BFAO·的单调限制性质,研究了两个BFAO·算法间的比较性质。     

Multiple-input and -output OR/AND circuits for VLSI GaAs IC's

OR/AND circuits with multiple input and output have been demonstrated experimentally for low-power 2K and 6K GaAs gate arrays with two levels of logic at approximately a 155-percent increase in speed and power product. The proposed multiple-logic levels process in parallel some complex logic functions with only one gate delay. Two proposed bootstrap techniques have shown an improvement of typically 12 percent in speed without an increase in power for low-power applications. In coupling these OR/AND circuits with the allowable buffered stage and the bootstrap enhancements, one can obtain good device performance over a spectrum of SSI to VLSI in the SDFL circuit family.     

Sensor data fusion using perception net for a precise assembly task

A sensor fusion method is presented for a peg-in-hole insertion task. Three kinds of sensor are fused for task execution. The vision and proximity sensors are mainly used for gross motion control, and the force/torque sensor is used for fine motion control of the robot. Covariance analysis is conducted for each sensor discussed in this paper, and the perception net is introduced to improve the usefulness of each sensor by optimally weighting its output.     

无人机电力线路巡检航线设计和任务规划中的关键技术

本文根据无人机巡检输电线路的相关需求,对无人机输电线路巡检航线和任务规划的软件的设计进行了相关探讨,同时,进一步分析了软件中所涉及到的关键技术,并且在分析与探讨的过程中充分考虑了软件的交互性、模块化和可视化.     

一种基于内容表示的图像序列运动分割算法

提出了一种MPEG－4标准所规范的基于内容表示的图像序列运动分割算法。首先进行图像序列二维运动估计并计算位移帧差,然后,将位移帧差作为将运动物体从静止背景中分离出来的标准进行分割;最后对分割结果进行形态滤波。实验结果显示,在物体存在整体运动的情况下,分割结果表现出了很好的准确度,可以成功地提取图像序列中的运动物体。     

三维多无人机系统协同任务规划关键问题综述

随着无人机技术的迅猛发展,人们对无人机技术的需求越来越广泛,已从单机简单任务向多机协同执行多个复杂任务、且自主协调、无人干预、群体合作的方向发展.而多无人机系统的协同任务规划技术,是无人机自主导航飞行和无人机之间自主协调配合共同完成任务的关键.这一任务规划决定了无人机各类资源的协调和执行目标的合理分配,无人机协调可飞飞行航迹的规划,以及在飞行过程中,取代人为干预,实时协调化解多机飞行时可能产生的各种冲突和问题.因此,对多无人机系统协同任务规划的关键问题,如目标分配、航迹规划、在线重规划等深入研究,是提高多机系统执行任务能力的重要环节.近年来,该领域的研究日益广泛,但还存在诸多问题需要解决,尤其迫切需要研究在三维战场环境下的多机协同任务规划方法.     

Solving a generalized constrained optimization problem with bothlogic AND and OR relationships by a mathematical transformation and itsapplication to robot motion planning

The logic relationship among the equality and inequality constraints in a standard constrained optimization problem (SCOP) is the logical AND. Various efficient, convergent and robust algorithms have been developed for such a SCOP. Motivated by a practical application, a more general constrained optimization problem (GCOP) with not only logic AND but also OR relationships is proposed in this paper. In order to solve such a generalized problem, a mathematical transformation which can transfer a set of inequalities with logic OR into one inequality is developed. This transformation provides a necessary and sufficient condition which enables us to use the algorithms developed for SCOPs to solve the generalized optimization problems. The research is motivated by the requirements of developing an efficient, robust, and reliable navigation algorithm for a mobile robot such as an autonomous underwater vehicle (AUV). The original contributions of the paper include threefold: first, from the viewpoint of optimization theory, this paper, to the authors' best knowledge, is the first one to propose such a GCOP. Second, a method is developed to solve such a GCOP. Third, from the viewpoint of robot path planning, this paper presents a new way of using classical optimization approach to solve robot path planning     

Self-learning-based post-processing for image/video deblocking via sparse representation

Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate coding. Various post-processing techniques have been proposed to reduce blocking artifacts, but they usually introduce excessive blurring or ringing effects. This paper proposes a self-learning-based post-processing framework for image/video deblocking by properly formulating deblocking as an MCA (morphological component analysis)-based image decomposition problem via sparse representation. Without the need of any prior knowledge (e.g., the positions where blocking artifacts occur, the algorithm used for compression, or the characteristics of image to be processed) about the blocking artifacts to be removed, the proposed framework can automatically learn two dictionaries for decomposing an input decoded image into its “blocking component” and “non-blocking component.” More specifically, the proposed method first decomposes a frame into the low-frequency and high-frequency parts by applying BM3D (block-matching and 3D filtering) algorithm. The high-frequency part is then decomposed into a blocking component and a non-blocking component by performing dictionary learning and sparse coding based on MCA. As a result, the blocking component can be removed from the image/video frame successfully while preserving most original visual details. Experimental results demonstrate the efficacy of the proposed algorithm.     

Variable voltage task scheduling algorithms for minimizing energy/power

In this paper, we propose variable voltage task scheduling algorithms that minimize energy or minimize peak power for the case when the task arrival times, deadline times, execution times, periods, and switching activities are given. We consider aperiodic (earliest due date, earliest deadline first), as well as periodic (rate monotonic, earliest deadline first) scheduling algorithms. We use the Lagrange multiplier method to theoretically determine the relation between the task voltages such that the energy or peak power is minimum, and then develop an iterative algorithm that satisfies the relation. The asymptotic complexity of the existing scheduling algorithms change very mildly with the application of the proposed algorithms. We show experimentally (random experiments as well as real-life cases), that the voltage assignment obtained by the proposed low-complexity algorithm is very close to that of the optimal energy (0.1% error) and optimal peak power (1% error) assignment.     

MRI/fMRI-compatible robotic system with force feedback for interaction with human motion

This paper presents a robotic system that is compatible with anatomical magnetic resonance imaging (MRI) as well as with the more sensitive functional MRI (fMRI), and can safely and smoothly interact with human motion during the imaging. The system takes advantage of the electromagnetic shield that encloses the MR room by placing the interfering or sensitive components outside the shield, in the control room. This eliminates the need for extensive compatibility testing before each use. The concept is based on a conventional actuator placed outside the scanner room and a hydrostatic connection to transmit force and motion to an MR-compatible slave placed next to or inside the MR scanner. A force sensor, based on reflected light intensity measurement over optical fibers, measures interaction forces with the human subject. A robotic interface for wrist motion demonstrates the MR compatibility of this concept and the possibility to interact with various dynamic environments during functional imaging. This technology provides a basis for applications such as assistive devices for interventional MRI and haptic interfaces for neuroscience investigations.     

Exponential effective SINR approximations for OFDM/OFDMA-based cellular system planning

Classical single carrier cell planning is extended to multi-carrier systems operating over frequency selective channels. Extension is achieved by expressing the link outage probability in terms of the statistics of the effective SINR. Two approximations for the link outage probability are obtained by considering log-normal and Gaussian assumptions for the derivation of the statistics of the exponential effective SINR. A simplified planning procedure is presented and applied to the calculation of the network parameters for the typical two cells interference scenario. Results having accuracy within one dB are obtained for a number of sub-carriers greater than six and for an outage target of 5%.     

A genetic algorithm for reliability-oriented task assignment with k/spl tilde/ duplications in distributed systems

A distributed system is a collection of processor-memory pairs connected by communication links. The reliability of a distributed system can be expressed using the distributed program reliability, and distributed system reliability analysis. The computing reliability of a distributed system is an NP-hard problem. The distribution of programs & data-files can affect the system reliability. The reliability-oriented task assignment problem, which is NP-hard, is to find a task distribution such that the program reliability or system reliability is maximized. For example, efficient allocation of channels to the different cells can greatly improve the overall network throughput, in terms of the number of calls successfully supported. This paper presents a genetic algorithm-based reliability-oriented task assignment methodology (GAROTA) for computing the k/spl tilde/-DTA reliability problem. The proposed algorithm uses a genetic algorithm to select a program & file assignment set that is maximal, or nearly maximal, with respect to system reliability. Our numerical results show that the proposed algorithm may obtain the exact solution in most cases, and the computation time seems to be significantly shorter than that needed for the exhaustive method. When the proposed method fails to give an exact solution, the deviation from the exact solution is very small. The technique presented in this paper would be helpful for readers to understand the correlation between task assignment reliability, and distributed system topology.     

Design of all-optical AND,OR, and XOR logic gates using photonic crystals for switching applications

Photonic Network Communications - We propose a photonic crystal-based all-optical AND, OR, and XOR logic gates using square lattice silicon rods with air background. The design of proposed logic...     

Channel estimation using complementary sequence pairs for UWB/OFDM systems

It is well known that time domain channel estimation can achieve better performance than frequency domain channel estimation with a time-multiplexed preamble in common OFDM systems. When it is applied to UWB/OFDM systems, however, its high complexity becomes the main obstacle because of the large number of resolvable paths. To solve this problem, a low-complexity channel estimation scheme is presented by exploiting a special construction of complementary sequence pairs.     

Contingency/disaster recovery planning for transmission systems ofthe Defense Information System Network

The paper provides recommendations for the enhancement and protection of the Defense Information System Network (DISN) for disaster recovery and contingencies. The DISN is a worldwide Defense Department network designed to serve consolidated and integrated communication requirements of the defense services and agencies. The ability to recover from disasters and allow for contingencies must be inherent in the design of this network. Basic design aspects of the DISN are described, along with several complementary programs for disaster recovery planning being implemented by the Office of the Manager, National Communications System (OMNCS). Both hostile and nonhostile threats are examined to set the stage for recommendations that follow. Techniques for disaster and contingency preparedness are described that will improve DISN survivability and disaster recovery. Specific technologies are considered for provision of independent orderwires and backup transmission systems. An interface device designed for transmission recovery is recommended for both satellite and terrestrial media. Other specific recommendations are made for network management within the various hierarchical levels of DISN management