Hybrid approach to implement multi-robotic navigation system using neural network,fuzzy logic,and bio-inspired optimization methodologies

Mobile robots have been increasingly popular in a variety of industries in recent years due to their ability to move in variable situations and perform routine jobs effectively. Path planning, without a dispute, performs a crucial part in multi-robot navigation, making it one of the very foremost investigated issues in robotics. In recent times, meta-heuristic strategies have been intensively investigated to tackle path planning issues in the similar way that optimizing issues were handled, or to design the optimal path for such multi-robotics to travel from the initial point to such goal. The fundamental purpose of portable multi-robot guidance is to navigate a mobile robot across a crowded area from initial point to target position while maintaining a safe route and creating optimum length for the path. Various strategies for robot navigational path planning were investigated by scientists in this field. This work seeks to discuss bio-inspired methods that are exploited to optimize hybrid neuro-fuzzy analysis which is the combination of neural network and fuzzy logic is optimized using the particle swarm optimization technique in real-time scenarios. Several optimization approaches of bio-inspired techniques are explained briefly. Its simulation findings, which are displayed for two simulated scenarios reveal that hybridization increases multi-robot navigation accuracy in terms of navigation duration and length of the path.     

Set-based discrete particle swarm optimization and its applications: a survey

Particle swarm optimization (PSO) is one of the most popular population-based stochastic algorithms for solving complex optimization problems. While PSO is simple and effective, it is originally defined in continuous space. In order to take advantage of PSO to solve combinatorial optimization problems in discrete space, the set-based PSO (S-PSO) framework extends PSO for discrete optimization by redefining the operations in PSO utilizing the set operations. Since its proposal, S-PSO has attracted increasing research attention and has become a promising approach for discrete optimization problems. In this paper, we intend to provide a comprehensive survey on the concepts, development and applications of S-PSO. First, the classification of discrete PSO algorithms is presented. Then the S-PSO framework is given. In particular, we will give an insight into the solution construction strategies, constraint handling strategies, and alternative reinforcement strategies in S-PSO together with its different variants. Furthermore, the extensions and applications of S-PSO are also discussed systemically. Some potential directions for the research of S-PSO are also discussed in this paper.     

Cooperative control and communication of intelligent swarms: a survey

Individuals exchange information, experience and strategy based on communication. Communication is the basis for individuals to form swarms and the bridge of swarms to realize cooperative control. In this paper, the multi-robot swarm and its cooperative control and communication methods are reviewed, and we summarize these methods from the task, control, and perception levels. Based on the research, the cooperative control and communication methods of intelligent swarms are divided into the following four categories: task assignment based methods (divided into market-based methods and alliance based methods), bio-inspired methods (divided into biochemical information inspired methods, vision based methods and self-organization based methods), distributed sensor fusion and reinforcement learning based methods, and we briefly define each method and introduce its basic ideas. Based on WOS database, we divide the development of each method into several stages according to the time distribution of the literature, and outline the main research content of each stage. Finally, we discuss the communication problems of intelligent swarms and the key issues, challenges and future work of each method.     

双向反馈蚁群算法在网络负载均衡问题的研究

针对网络资源管理中的负载均衡与优化问题,提出一种双向反馈蚁群算法,用蚂蚁数量代表网络资源流量,通过蚂蚁间信息素的相互作用和动态控制来实现网络流量分担到多条可用路径。将蚁群算法扩展为双向反馈的蚁群算法,蚂蚁判断各条路径上的信息素浓度的同时,考虑可选链路的负载情况,决定选择要走路径,使得蚂蚁相对均衡地分布在可选链路上。仿真实验结果表明,双向反馈蚁群算法比原蚁群算法在缩短自适应时间,减少丢包率,提高负载均衡效率方面都具有更好的性能。     

从集群智能角度分析协同驾驶:综述和展望

协同驾驶是集群智能技术的典型应用之一.和集群机器人相比,协同驾驶既有集群智能技术的共性问题,也有特性问题.对协同驾驶与集群机器人对比研究,有助于更好理解协同驾驶问题的内在难点,也有助于推动集群智能技术的发展,完善复杂系统科学.本文从集群智能研究的角度出发,对协同驾驶进行反思综述.本文首先介绍协同驾驶的研究背景和意义,然后从集群智能的角度进一步分析协同驾驶和集群机器人的区别,接着介绍协同驾驶的关键技术,并着重强调协同规划在协同驾驶中的特殊性与重要性,最后分析了世界各国协同驾驶发展现状,并针对现阶段我国协同驾驶发展的难点提出一些建议,同时对协同驾驶的未来进行展望.     

多智能体强化学习在城市交通网络信号控制方法中应用综述

交通信号控制系统在物理位置和控制逻辑上分散于动态变化的网络交通环境,将每个路口的交通信号控制器看作一个异质的智能体,非常适合采用“无模型、自学习、数据驱动”的多智能体强化学习（MARL）方法建模与描述。为了解该方法的研究现状、存在问题及发展前景,系统跟踪了多智能体强化学习在国内外交通控制领域的具体应用,包括交通信号MARL控制概念模型、完全孤立的MARL控制、部分状态合作的MARL和动作联动的MARL控制,分析其技术特征和代际差异,讨论了多智体强化学习方法在交通信号控制中的研究动向,提出了发展网络交通信号多智能体强化学习集成控制的关键问题在于强化学习控制机理、联动协调性、交通状态特征抽取和多模式整合控制。     

集对分析在人工智能中的应用与进展

人工智能是我国发展战略,集对分析从自主原创角度为人工智能提供一种基础性思路,具有重要意义。集对分析把确定的数学计算与不确定性系统分析有机结合,已在人工智能基础、模式识别、不确定性推理、智能决策、知识生态学、自然语言理解、专家系统、神经网络、智能工程、智能社会网络社区划分与演化等研究中得到应用。本文在概述集对分析的原理和联系数之后,综述集对分析在人工智能中的应用和进展,以期推动集对分析在人工智能中的进一步应用。     

Loosely coordinated coscheduling in the context of other approaches for dynamic job scheduling: a survey

Loosely coordinated (implicit/dynamic) coscheduling is a time‐sharing approach that originates from network of workstations environments of mixed parallel/serial workloads and limitedsoftware support. It is meant to be an easy‐to‐implement and scalable approach. Considering that the percentage of clusters in parallel computing is increasing and easily portable software is needed, loosely coordinated coscheduling becomes an attractive approach for dedicated machines. Loose coordination offers attractive features as a dynamic approach. Static approaches for local job scheduling assign resources exclusively and non‐preemptively. Such approaches still remain beyond the desirable resource utilization and average response times. Conversely, approaches for dynamic scheduling of jobs can preempt resources and/or adapt their allocation. They typically provide better resource utilization and response times. Existing dynamic approaches are full preemption with checkpointing, dynamic adaptation of node/CPU allocation, and time sharing via gang or loosely coordinated coscheduling. This survey presents and compares the different approaches, while particularly focusing on the less well‐explored loosely coordinated time sharing. The discussion particularly focuses on the implementation problems, in terms of modification of standard operating systems, the runtime system and the communication libraries. Copyright © 2005 John Wiley & Sons, Ltd.     

一个直实化工厂蒸汽网络系统的建模与优化

对蒸汽网络系统进行建模与优化,可以在系统配置和操作条件满足公用工程要求的前提下,以最小产汽量为目标,实现化工厂的节能运行.本文利用在一个真实化工厂现场调研得到的实际数据,对该化工厂的蒸汽网络系统进行建模,得到LP(线性规划)和MINLP(混合整数非线性规划)模型,用线性规划算法和改进文化差分算法分别对模型进行求解,并与现场实时数据和LINGO计算结果进行对比.计算结果显示,MINLP模型更好的描述了该蒸汽网络系统,并达到了可观的优化效果,可在满足生产需求的条件下,减小锅炉产汽量和放空量,达到降低成本、提高经济效益的效果.     

Achieving developability of a polygonal surface by minimum deformation: a study of global and local optimization approaches

Surface developability is required in a variety of applications in product design, such as clothing, ship hulls, automobile parts, etc. However, most current geometric modeling systems using polygonal surfaces ignore this important intrinsic geometric property. This paper investigates the problem of how to minimally deform a polygonal surface to attain developability, or the so-called developability-by-deformation problem. In our study, this problem is first formulated as a global constrained optimization problem and a penalty-function-based numerical solution is proposed for solving this global optimization problem. Next, as an alternative to the global optimization approach, which usually requires lengthy computing time, we present an iterative solution based on a local optimization criterion that achieves near real-time computing speed.     

Avatars of the tortoise: life,longevity and simulation

Abstract

We present a new way of dynamically growing and breeding structures in 3D space through swarming agents. Different agent types and the way they evolve over time is specified by a swarm grammar similar to Lindenmayer systems. We expand common L-system string interpretation from a single turtle to a multitude of turtles which behave like a swarm. By describing swarm agents within the framework of formal grammars, we build a bridge from symbolic production systems (rewrite systems) to three-dimensional real-time construction procedures that are executed by reactive and interacting agents which move in simulated physical 3D spaces. We introduce constructor agents, their formal representation in swarm grammars and demonstrate by examples how (1) the swarm rules, (2) the agent parameters and (3) the environ ment can influence the actual construction and growth processes that are initiated and directed by the swarms. In order to facilitate exploration of a large variety of swarm grammars, we apply interactive evolutionary design methods to create swarm grammar sculptures and 3D structures.     

Cognitive mechanism related to line drawings and its applications in intelligent process of visual media: a survey

Line drawings, as a concise form, can be recognized by infants and even chimpanzees. Recently, how the visual system processes line-drawings attracts more and more attention from psychology, cognitive science and computer science. The neuroscientific studies revealed that line drawings generate similar neural actions as color photographs, which give insights on how to efficiently process big media data. In this paper, we present a comprehensive survey on line drawing studies, including cognitive mechanism of visual perception, computational models in computer vision and intelligent process in diverse media applications. Major debates, challenges and solutions that have been addressed over the years are discussed. Finally some of the ensuing challenges in line drawing studies are outlined.     

Digital twin-based designing of the configuration,motion, control,and optimization model of a flow-type smart manufacturing system

Digital twins can achieve hardware-in-the-loop simulation of both physical equipment and cyber model, which could be used to avoid the considerable cost of manufacturing system reconfiguration if the design deficiencies are found in the deployment process of the traditional irreversible design approach. Based on the digital twin technology, a quad-play CMCO (i.e., Configuration design-Motion planning-Control development-Optimization decoupling) design architecture is put forward for the design of the flow-type smart manufacturing system in the Industry 4.0 context. The iteration logic of the CMCO design model is expounded. Two key enabling technologies for enabling the customized and software-defined design of flow-type smart manufacturing systems are presented, including the generalized encapsulation of the quad-play CMCO model and the digital twin technique. A prototype of a digital twin-based manufacturing system design platform, named Digital Twin System, is presented based on the CMCO model. The digital twin-based design platform is verified with a case study of the hollow glass smart manufacturing system. The result shows that the Digital Twin System-based design approach is feasible and efficient.     

On the pitfalls of bibliographic database searching: comparing successful and less successful users

This article investigates individual factors that determine success and failure in bibliographic database searching. In line with previous research, we hypothesised that the low usability of advanced bibliographic database interfaces (e.g. OvidSP?) would hinder students in implementing their information-seeking knowledge. Furthermore, we assumed high conscientiousness and need for cognitive closure to drive students towards rigidly using such advanced interfaces, even if they overtax them. In a study with 116 psychology undergraduates, 3 types of searchers were identified by means of a cluster analysis: successful database users, unsuccessful database users, and no-database users (e.g. Google Scholar? searchers). Multiple regression revealed the relationship between information-seeking knowledge and search success to be positive for no-database users and negative for unsuccessful database users: unsuccessful database users do not only have trouble putting their information-seeking knowledge into practice; high information-seeking knowledge even impairs their search success, presumably because it makes them use advanced functions that they are not yet able to master. T-tests revealed that unsuccessful users differ from successful users in terms of higher conscientiousness and need for cognitive closure. In conclusion, our results highlight the importance of enhancing database interface usability and underline the crucial role of practical database instruction.     

Enterprise social networking usage as a moderator of the relationship between work stressors and employee creativity: A multilevel study

Enterprise social networking (ESN) techniques have been widely adopted by organizations to provide a platform for public communication among employees. Based on the job demand–control–support model and communication visibility theory, this study investigates how the relationships between the perceived work stressors of employees (i.e., challenge and hindrance stressors) and their creativity are moderated by team task- and relationship-oriented ESN usage. We used the hierarchical linear model to test this multilevel model. Results of a survey of 260 employees in 72 groups indicate that two ESN usage types differentially moderate the relationship between work stressors and employee creativity. Specifically, task-oriented ESN usage positively moderates the effects of the two types of stressors on employee creativity, whereas relationship-oriented ESN usage negatively moderates the relationship between hindrance stressors and employee creativity. Theoretical and practical implications are also discussed.     

On the concept of density control and its application to a hybrid optimization framework: Investigation into cutting problems

The Generate-and-Solve (GS) methodology is a hybrid approach that combines a metaheuristic component with an exact solver. GS has been recently introduced in the literature in order to solve cutting and packing problems, showing promising results. The GS framework includes a metaheuristic engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver. In this paper, we present an extended version of GS, focusing primarily on the concept of a new Density Control Operator (DCO). The role of this operator is to adaptively control the dimension of the reduced instances in such a way as to allow a much steadier progress towards a better solution, thereby avoiding premature convergence. In order to assess the potentials of this novel version of the GS methodology, we conducted computational experiments on a set of difficult benchmark instances of the constrained non-guillotine cutting problem. The results achieved are quantitatively and qualitatively discussed in terms of effectiveness and efficiency, showing that the proposed variant of the GS hybridization framework is highly suitable when effectiveness is a major requirement.     

Local and global optima in decision-making: a sheaf-theoretical analysis of the difference between classical and behavioral approaches

One of the main differences between the traditional and the behavioral approaches to decision-making is that the latter has not yet been captured in a unifying framework. This hampers in a certain way the whole research program and raises the question of whether this competing approach can provide an encompassing alternative to the classical one. We analyze this issue in light of the problem of reconstructing global choices of an agent up from the solutions found for local problems. We show that a representation based on category theory of the conditions for such reconstruction is general and robust enough to represent both the case in which problems are non-contextual and local as well as that, typical in the literature on behavioral decision-making, in which such properties do not hold. In the first case, we show how a sheaf-theoretical representation provides an abstract characterization of the global solution. In the latter case, we show how locality and contextuality generate obstructions toward the reconstruction of global solutions, yielding a possible clue for the intrinsic difference between behavioral and classical decision theory.     

Prior knowledge in learning from a non-linear electronic document: Disorientation and coherence of the reading sequences

A study was carried out to investigate the effects of prior knowledge on learning with a non-linear electronic document including an interactive conceptual map. Cognitive Load Theory was used as theoretical framework to investigate effects on cognitive load and disorientation in learning from non-linear documents. Forty-four future high school biology teachers were required to learn the multiplication cycle of a virus from either a hierarchical structure (organisational links) or a network structure (relational links). For the low prior knowledge learners, the results showed that the hierarchical structure supported better free recall performance and reduced feelings of disorientation. In contrast, the high prior knowledge learners performed better and followed more coherent reading sequences in the network structure. However, no interaction effect between prior knowledge and the type of structure was observed on mental effort and disorientation ratings. The results and the construct of disorientation are discussed in light of the processing demands in non-linear documents.