多机器人对未知环境进行实时在线探测的一种方法

将多机器人协作引入了构建地图的作业任务研究中，将多个范围传感器所探测到的环境信息以概率值的方式表示，利用不确定性证据推理的原理对其进行了Dempster-Shafer数据融合。在协作的基础上，设计了避障策略。系统仿真实验，验证了该方法的可行性。     

基于势场路径规划的机器人同步定位与地图构建

针对特征点同时为障碍物的环境,提出一种基于势场路径规划的同步定位与地图构建算法,其能够分析事物的特征点、有效消除障碍物、构建良好的环境,具有十分重要的作用。这一算法经过专家学者长时间的研究,能够推算出基于势场地位的控制规律,其不仅能够对机器人同步定位与地图构建进行动态定位,还能够确保其综合性能,突破了原有格局、开创了新的发展局面。该文具体阐述SLAM系统状态,对机器人进行实时监测,另外建立系统模型,采用合理的方式进行路径规划。     

基于激光传感器的机器人地图构建方法

针对自主移动机器人局部地图构建问题,分析了几种应用较广的测距传感器的优缺点和地图构建中使用的激光传感器的数学模型及其非线性问题.在给出局部地图构建的一般模型的基础上本文提出了一种机器人局部地图构建的一种新方法,该方法针对传感器数据的不确定问题采用加权最小二乘方法对机器人在导航过程中的局部地图进行构建,具有实现容易、精度较高等特点.仿真和实验结果表明该方法在移动机器人使用激光测距传感器进行环境建模和地图构建过程中可以有效的减小直线拟合误差,进而达到有效建图的目的.     

一种工业机器人多目标轨迹优化算法

为解决工业机器人工作效率低、能耗损失严重和关节冲击磨损较大的问题,提出了一种基于布谷鸟搜索（cuckoo search,CS）算法和非支配排序遗传算法-Ⅱ(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)的混合算法（简称为CSNSGA-Ⅱ）,用于机器人的轨迹优化。采用5次非均匀有理B样条（non-uniform rational B-splines,NURBS）曲线作为工业机器人的轨迹规划曲线,同时以运动时间、能耗和冲击磨损为优化目标构建相应的多目标轨迹优化模型,并在速度、加速度和加加速度的约束下采用CSNSGA-Ⅱ进行轨迹优化。CSNSGA-Ⅱ以Tent混沌映射初始化时间序列,采用不可行度算法将解分为可行解与不可行解,并利用改进的CS算法对不可行解进行处理。利用MATLAB软件对6R勃朗特机器人进行建模仿真,并对得到的非支配解集和归一化加权迭代最优值进行对比分析。仿真结果表明,相比于NSGA-Ⅱ、多目标粒子群优化（multi-objective particle swarm optimization,MOPSO）算法,所提出的CSNS...     

一种求工业机器人工具参数的方法

本文主要介绍一种在工业机器人中通过示教获得位置点并自动为用户生成工具参数值的方法 ,从原理的角度进行剖析.首先示教知道机器人法兰末端在世界坐标系下的值,之后通过齐次变换矩阵,通过算法算出工具参数的位置值.     

一种基于区域分割的多级环境建模方法

针对现有移动机器人路径规划中各种环境建模方法存在使用范围有限、复杂问题处理能力不足和运行效率低、缺乏灵活性等问题，结合二维半描述和知识利用原理，提出了一种可以比较圆满地解决诸如建筑物内不同楼层连接、室内室外环境交替出现等实用结构性空间问题的基于区域分割的多级环境建模方法。     

基于离散小波变换的多自由度结构非平稳随机响应计算

将地面运动模拟成非平稳随机过程,基于离散小波变换得到地面运动离散小波系数的统计值,并以此作为输入,推导了多自由度结构和随机响应公式,通过对响应偏谱的计算,得到随时间变化的响应频率特性和名阶谱参数,利用Monte-Carlo法和本文方法进行了计算实例对比,验证了本文方法的正确性。     

非稳态环境激励下线性结构的模态参数辨识

假定任意随机激励信号由白噪声与非白噪声信号组成，由此导出线性结构响应之间的相关函数由两部分组成，一部分与脉冲响应具有相同的数学形式，另一部分为其它形式，利用模态分解法的基本原理，把相关函数分解为各个模态函数的叠加与余项之和，这样，第一部分信号已经分解为不同的模态函数，第二部分中的周期信号也变成了模态函数，这就把非稳态环境激励下多自由度线性结构系统的模态参数辨识问题转化为类似于已知各个单自由度系统的脉冲响应进行模态参数辨识问题，理论和模拟实验表明，本文成功地利用模态分解法进行非稳态环境激励下多自由度线性结构系统的模态参数辨识，其主要优点是，无论是白噪声激励，稳态随机激励还是非稳态随机激励，仅根据结构的响应不仅能辨识线性结构的模态参数，而且能有效地识别出环境激励中的周期成分。     

一种快速构造非支配集的方法--擂台法则

多目标进化算法是用来解决多目标优化问题的,为了提高多目标算法的效率,提出了一种快速构造非支配集的方法——擂台法则。它的时间耗费要低于Deb和Jensen提出的构造非支配集的方法。在实验中将擂台法则同Deb和Jensen的方法进行了比较,最后实验结果证明前者在运行时间上要优于后两者。     

未知环境下基于意图推理的多机器人编队控制

设计了一种未知环境下基于意图推理的多机器人编队控制方法.该方法在机器人自身移动局部坐标系中通过观察其领航机器人,采用限定记忆最小二乘多步递推策略对领航机器人的运动趋势进行预测,估计出领航机器人的运动信息及机器人之间的相关信息,结合已有的编队控制律以及切线避障策略使机器人安全躲避潜在危险的同时实现多机器人之间的协调.该方法扩展性较好,仿真验证了其可行性.     

An equilibrium for phenotypic variance in fluctuating environments owing to epigenetics

The connection between random environments and genetic and phenotypic variability has been a major focus in the population genetic literature. By providing differential access to the underlying genetic information, epigenetic variation could play an important role in the interaction between environmental and phenotypic variation. Using simulation, we model epigenetic plasticity during development by investigating the dynamics of genetic regulators of the epigenetic machinery that change the variance of the phenotype, while having no effect on the phenotype''s mean. Previous studies have found that increased phenotypic variance is selected for if the environment is fluctuating. Here, we find that when a variance-increasing allele achieves a sufficiently high frequency, it can be out-competed by a variance-reducing allele, with the consequence that the population evolves to an equilibrium phenotypic variability. This equilibrium is shown to be robust to different initial conditions, but to depend heavily on parameters of the model, such as the mutation rate, the fitness landscape and the nature of the environmental fluctuation. Indeed, if there is no mutation at the genes controlling the variance of the phenotype, reduction of this variance is favoured.     

An intelligent approach to robust multi-response process design

In order to meet strict customer demands in a global highly-complex industrial sector, it is necessary to design manufacturing processes based on a clear understanding of the customer's requirements and usage of a product, by translating this knowledge into the process parameter design. This paper presents an integrative, general and intelligent approach to the multi-response process design, based on Taguchi's method, multivariate statistical methods and artificial intelligence techniques. The proposed model considers process design in a general case where analytical relations and interdependency in a process are unknown, thus making it applicable to various types of processes, and incorporates customer demands for several (possible correlated) characteristics of a product. The implementation of the suggested approach is presented on a study that discusses the design of a thermosonic copper wire bonding process in the semiconductor industry, for assembly of microelectronic devices used in automotive applications. The results confirm the effectiveness of the approach in the presence of different types of correlated product quality characteristics.     

A consistent point‐searching algorithm for solution interpolation in unstructured meshes consisting of 4‐node bilinear quadrilateral elements

To translate and transfer solution data between two totally different meshes (i.e. mesh 1 and mesh 2), a consistent point‐searching algorithm for solution interpolation in unstructured meshes consisting of 4‐node bilinear quadrilateral elements is presented in this paper. The proposed algorithm has the following significant advantages: (1) The use of a point‐searching strategy allows a point in one mesh to be accurately related to an element (containing this point) in another mesh. Thus, to translate/transfer the solution of any particular point from mesh 2 to mesh 1, only one element in mesh 2 needs to be inversely mapped. This certainly minimizes the number of elements, to which the inverse mapping is applied. In this regard, the present algorithm is very effective and efficient. (2) Analytical solutions to the local co‐ordinates of any point in a four‐node quadrilateral element, which are derived in a rigorous mathematical manner in the context of this paper, make it possible to carry out an inverse mapping process very effectively and efficiently. (3) The use of consistent interpolation enables the interpolated solution to be compatible with an original solution and, therefore guarantees the interpolated solution of extremely high accuracy. After the mathematical formulations of the algorithm are presented, the algorithm is tested and validated through a challenging problem. The related results from the test problem have demonstrated the generality, accuracy, effectiveness, efficiency and robustness of the proposed consistent point‐searching algorithm. Copyright © 1999 John Wiley & Sons, Ltd.     

Design optimization of cold-formed steel portal frames taking into account the effect of building topology

Cold-formed steel portal frames are a popular form of construction for low-rise commercial, light industrial and agricultural buildings with spans of up to 20 m. In this article, a real-coded genetic algorithm is described that is used to minimize the cost of the main frame of such buildings. The key decision variables considered in this proposed algorithm consist of both the spacing and pitch of the frame as continuous variables, as well as the discrete section sizes. A routine taking the structural analysis and frame design for cold-formed steel sections is embedded into a genetic algorithm. The results show that the real-coded genetic algorithm handles effectively the mixture of design variables, with high robustness and consistency in achieving the optimum solution. All wind load combinations according to Australian code are considered in this research. Results for frames with knee braces are also included, for which the optimization achieved even larger savings in cost.     

An adaptive genetic algorithm approach for the mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line that is capable of producing a variety of different product models simultaneously and continuously. The design and planning of such lines involve several long- and short-term problems. Among these problems, determining the sequence of products to be produced has received considerable attention from researchers. This problem is known as the Mixed-Model Assembly Line Sequencing Problem (MMALSP). This paper proposes an adaptive genetic algorithm approach to solve MMALSP where multiple objectives such as variation in part consumption rates, total utility work and setup costs are considered simultaneously. The proposed approach integrates an adaptive parameter control (APC) mechanism into a multi-objective genetic algorithm in order to improve the exploration and exploitation capabilities of the algorithm. The APC mechanism decides the probability of mutation and the elites that will be preserved for succeeding generations, all based on the feedback obtained during the run of the algorithm. Experimental results show that the proposed adaptive GA-based approach outperforms the non-adaptive algorithm in both solution quantity and quality.     

An approach to accidents modeling based on compounds road environments

The most common approach to study the influence of certain road features on accidents has been the consideration of uniform road segments characterized by a unique feature. However, when an accident is related to the road infrastructure, its cause is usually not a single characteristic but rather a complex combination of several characteristics. The main objective of this paper is to describe a methodology developed in order to consider the road as a complete environment by using compound road environments, overcoming the limitations inherented in considering only uniform road segments. The methodology consists of: dividing a sample of roads into segments; grouping them into quite homogeneous road environments using cluster analysis; and identifying the influence of skid resistance and texture depth on road accidents in each environment by using generalized linear models. The application of this methodology is demonstrated for eight roads. Based on real data from accidents and road characteristics, three compound road environments were established where the pavement surface properties significantly influence the occurrence of accidents. Results have showed clearly that road environments where braking maneuvers are more common or those with small radii of curvature and high speeds require higher skid resistance and texture depth as an important contribution to the accident prevention.     

An inverse problem approach to stiffness mapping for early detection of breast cancer: tissue phantom experiments

Early detection of breast cancer will continue to be crucial in improving patient survival rates. Our ultimate goal is to develop an electro-mechanical device to automate and refine the manual breast exam process, and use inverse techniques to generate a tissue stiffness map of the breast tissue. We have previously presented computational simulations of the stiffness mapping approach, which employs static indentations of the tissue and measurements of surface displacements. In this paper, we report on experimental validation of the technique with tissue phantom experiments. We tested 12 tissue phantom samples without simulated tumours and 14 tissue phantom samples with simulated tumours. Our stiffness mapping approach correctly identified all 26 samples.     

An accurate and fast regularization approach to thermodynamic topology optimization

In a series of previous works, we established a novel approach to topology optimization for compliance minimization based on thermodynamic principles known from the field of material modeling. Hamilton's principle for dissipative processes directly yields a partial differential equation (referred to as the evolution equation) as an update scheme for the spatial distribution of density mass describing the topology. Consequently, no additional mathematical minimization algorithms are needed. In this article, we introduce a regularization scheme by penalization of the gradient of the spatial distribution of mass density. The parabolic evolution equation (owing to a similar structure to the transient heat-conduction equation) is solved most efficiently by an explicit time discretization. The Laplace operator is discretized via a Taylor series expansion yielding an operator matrix that is constant for the entire optimization process. This method shares some similarities to meshless methods and allows for an accurate application also on unstructured finite element meshes. The minimal size of the structure member can directly be controlled, a priori, by a numerical parameter introduced along with the regularization, similar to classical filter radii.     

A two-stage approach for multi-objective decision making with applications to system reliability optimization

This paper proposes a two-stage approach for solving multi-objective system reliability optimization problems. In this approach, a Pareto optimal solution set is initially identified at the first stage by applying a multiple objective evolutionary algorithm (MOEA). Quite often there are a large number of Pareto optimal solutions, and it is difficult, if not impossible, to effectively choose the representative solutions for the overall problem. To overcome this challenge, an integrated multiple objective selection optimization (MOSO) method is utilized at the second stage. Specifically, a self-organizing map (SOM), with the capability of preserving the topology of the data, is applied first to classify those Pareto optimal solutions into several clusters with similar properties. Then, within each cluster, the data envelopment analysis (DEA) is performed, by comparing the relative efficiency of those solutions, to determine the final representative solutions for the overall problem. Through this sequential solution identification and pruning process, the final recommended solutions to the multi-objective system reliability optimization problem can be easily determined in a more systematic and meaningful way.     

An Integrated mining approach to discover business process models with parallel structures: towards fitness improvement

Process mining (PM) is a technique to extract a process model from an event log to represent the process behaviour recorded in that event log. A mined process model with high fitness means that it can reflect most of the process behaviour recorded in the event log. Previous studies have shown that the mined model with high fitness can be used in process improvement, such as fraud detection, continuous process improvement and benchmarking. Genetic process mining (GPM) is a famous PM approach, which can simultaneously identify several process structures from event logs. However, GPM cannot effectively discover parallel structures from event logs. This study proposes a PM approach based on integration of GPM, particle swarm optimisation and differential evolution to find process models with high fitness for event logs involving multiple parallel structures. The results show that the proposed approach does indeed lead to improvement in gaining process models with high fitness for event logs involving multiple parallel structures.