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     

A genetic technique for robotic trajectory planning

There are many multi-stage optimization problems that are not easily solved through any known direct method when the stages are coupled. For instance, the problem of planning a vehicle's control sequence to negotiate obstacles and reach a goal in minimum time is investigated. The vehicle has a known mass, and the controlling forces have finite limits. A genetic programming technique is developed that finds admissible control trajectories that tend to minimize the vehicle's transit time through the obstacle field. The immediate application is that of a space robot that must rapidly traverse around two or three dimensional structures via application of a rotating thruster or non-rotating on-off thrusters. (An air-bearing floor test-bed for such vehicles is located at the Marshal Space Flight Center in Huntsville, Alabama.) It appears that the developed method is applicable to a general set of optimization problems in which the cost function and the multi-dimensional multi-state system can be any non-linear functions that are continuous in the operating regions. Other applications include: the planning of optimal navigation pathways through a traversability graph, the planning of control input for underwater maneuvering vehicles which have complex control state-space relationships, the planning of control sequences for milling and manufacturing robots, the planning of control and trajectories for automated delivery vehicles, and the optimization of control for racing vehicles and athletic training in slalom sports.     

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。     

具有混合反馈的高Q和/或高fs开关电容带通滤波器

本文提出了一种减少电容分布和总电容的高Q和/或高f_s开关电容(SC)带通滤波器。在传统的双二次SC滤波器结构中,引入部分正反馈并改变前馈网络可使总电容和电容分布得到大幅度的减少。举例说明了二阶高Q(=50)SC带通滤波器设计,并比较了不同滤波器结构的性能参数。     

一种面向低轨遥感星座的路由任务规划算法研究

为了解决遥感星座数据传输时效性差的问题,利用星间链组网,提出了一种适应于断续猝发特征星间网络的路由任务规划算法。该算法以遥感卫星成像任务规划结果为驱动,结合卫星网络拓扑结构的时变性特点,改进基于快照序列的拓扑划分方法,降低路由切换频率,减少链路切换代价损失。并将卫星与地面站之间的路由看作低轨遥感星座路由的一部分,在选择星地最短路由路径的同时实现星地建链总时长最大化。仿真结果表明,该算法能够显著提升低轨遥感卫星星座的数据传输能力。     

一种为地面节点充电的多UAV任务分配与路线规划方法

以多个无人机（UAV）为大面积分布的传感器节点无线充电为应用场景,提出了一种分布式快速拍卖算法（DFAMTA）用于为多个UAV分配任务及规划航线。利用该算法,不需集中控制器,每个UAV根据自己的续航能力和获知的节点位置及剩余电量信息,建立包含多个节点的任务集进行投标;中标者的确定在单个任务层面上独立并行进行,大大节省了任务分配的收敛时间。理论证明,DFAMTA算法在最差情况下也能获得最优分配算法50%的性能。仿真验证了算法在收敛时间上的优势以及在改善无线传感器网络系统平均覆盖率方面的优越性。     

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.     

Novel matrix representation for OFDM/OQAM systems

In orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) systems, the relationship between the input of the synthesis filter bank (SFB) and the output of the analysis filter bank (AFB) is much more complicated than OFDM due to its special prototype filter. By analyzing the trans-multiplexer response characteristics, an equivalent trans-multiplexer matrix is proposed to describe the relationship between the input and the output. With the equivalent matrix, the output can be easily computed using matrix multiplication with the input. Moreover, with the inverse of the equivalent trans-multiplexer matrix, imaginary interference can be eliminated using the precoding method. The simulation results show the correctness of the equivalent trans-multiplexer matrix.     

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.     

改进Q-Learning的WRSN充电路径规划算法

针对传统无线传感器网络节点能量供应有限和网络寿命短的瓶颈问题,依据无线能量传输技术领域的最新成果,提出了一种基于改进Q-Learning的无线可充电传感器网络的充电路径规划算法。基站根据网络内各节点能耗信息进行充电任务调度,之后对路径规划问题进行数学建模和目标约束条件设置,将移动充电车抽象为一个智能体(Agent),确定其状态集和动作集,合理改进ε-greedy策略进行动作选择,并选择相关性能参数设计奖赏函数,最后通过迭代学习不断探索状态空间环境,自适应得到最优充电路径。仿真结果证明：该充电路径规划算法能够快速收敛,且与同类型经典算法相比,改进的Q-Learning充电算法在网络寿命、节点平均充电次数和能量利用率等方面具有一定优势。     

基于XNOR/OR逻辑的低功耗最佳极性搜索

 本文通过对XNOR/OR逻辑表达式、信号概率传递算法和极性转换算法的研究,提出了一种基于XNOR/OR逻辑的低功耗最佳极性搜索算法.由于算法所用的成本函数包含功耗和面积两方面因素,因此能实现功耗和面积的同时优化.通过对10个MCNC Benchmark电路的测试表明,算法对最佳极性的搜索相当有效:与极性0时的XNOR/OR电路相比,算法搜索到的最佳极性所对应的电路,功耗和面积平均节省分别达到68.4％和34.2％.