面向多搬运任务的柔性制造车间多载具AGV节能路径规划

自动导引搬运车（automated guided vehicle,AGV）能够灵活、准确、高效地进行物料搬运,被广泛应用于柔性制造车间。多载具AGV能同时搬运多个工件,具有较强的搬运灵活性,其路径规划问题的复杂性和多约束性更强,求解难度更大。针对柔性制造车间多载具AGV节能路径规划问题,首先建立了以能耗和搬运距离为优化目标的AGV节能路径规划模型;然后,提出了一种改进Dijkstra算法和非支配排序遗传算法（non-dominated sorting genetic algorithm-II,NSGA-II）集成的多载具AGV节能路径规划方法;最后通过案例验证了模型的节能效果和求解方法的有效性。     

模拟执行操作目标的方法研究

模拟执行的检测方法是检测需求规格说明书的一种重要手段。在目标和场景相结合的需求建模方法的基础上,提出了对操作目标集合的模拟执行方法。该方法为代理的层次划分提供了理论支持,并将操作目标集合转换成了基于规则的层次式有穷状态自动机,通过对基于规则的层次式有穷状态自动机的模拟执行,达到了对操作目标集合的模拟执行。     

Satisfying agent goals by executing different task semantics: HTN, OWL-S or plug one yourself

In this paper, we propose a plan execution architecture which supports different task semantics. This way, each goal that is identified during the deliberation cycle of an agent can be satisfied through tasks defined in different semantics. The capability of supporting different task semantics provides two main advantages. The first advantage is the reuse of legacy artifacts within agent plans. The second advantage is simplifying the adaptation of an agent architecture to different standards of a business organization. In order to integrate various task semantics within a plan execution architecture, we have used a smoothly revised version of a previously articulated workflow model into which different task semantics would be reduced before execution. We have integrated hierarchical task network and OWL-S semantics into our plan execution architecture to test the strength of it in terms of support for executing different task semantics in an agent architecture. We think that such a plan execution architecture will contribute to the industrialization of agent architectures.     

A model for an intelligent operating system for executing image understanding tasks on a reconfigurable parallel architecture

Parallel processing is one approach to achieving the large computational processing capabilities required by many real-time computing tasks. One of the problems that must be addressed in the use of reconfigurable multiprocessor systems is matching the architecture configuration to the algorithms to be executed. This paper presents a conceptual model that explores the potential of artificial intelligence tools, specifically expert systems, to design an Intelligent Operating System for multiprocessor systems. The target task is the implementation of image understanding systems on multiprocessor architectures. PASM is used as an example multiprocessor. The Intelligent Operating System concepts developed here could also be used to address other problems requiring real-time processing. An example image understanding task is presented to illustrate the concept of intelligent scheduling by the Intelligent Operating System. Also considered is the use of the conceptual model when developing an image understanding system in order to test different strategies for choosing algorithms, imposing execution order constraints, and integrating results from various algorithms.     

An iso-time scaling method for big data tasks executing on parallel computing systems

Due to the sustained and rapid growth of big data and the demand on higher accuracy solutions for application problems, the completion time of fixed-time big data tasks executing on original parallel computing systems becomes longer and longer. To meet the requirement of fixed completion time, the original parallel computing systems need to be scaled accordingly. Therefore, this paper studies an iso-time scaling method to guide the scaling of parallel computing systems. Firstly, the models of big data parallel tasks and parallel computing systems are built, and an algorithm is designed to calculate the completion time of big data parallel tasks. Secondly, according to the actual situation of the current majority computing centers, we put forward some reasonable hypotheses, make full use of backup computational nodes, and optimize the cost of scaling parallel computing systems. Then, a vertical scaling algorithm is designed to upgrade computational nodes, and a horizontal scaling algorithm is designed to add computational nodes. Furthermore, this paper compares the two scaling algorithms in the aspects of time complexity, degree of parallelism and system utilization for scaled parallel computing system. Finally, some simulation experiments are conducted. The experimental results show that our method can keep the completion time within fixed time when the increasing data parallel tasks execute on the scaled parallel computing systems and it has better effect in scaling cost than traditional methods.     

A method for efficiently executing horn clause programs using multiple processors

Previous investigations have suggested the use of multiple communicating processors for executing logic programs. However, this strategy lacks efficiency due to competition for memory and communication bandwidth, and this is a problem that has been largely neglected. In this paper we propose a realistic model for executing logic programs with low overhead on multiple processors. Our proposal does not involve shared memory or copying computation state between processors. The model organises computations over the nondeterministic proof tree so that different processors explore unique deterministic computation paths independently, in order to exploit the “OR-parallelism” present in a program. We discuss the advantages of this approach over previous ones, and suggest control strategies for making it effective in practice.     

A logic-based agent that plans for extended reachability goals

Planning to reach a goal is an essential capability for rational agents. In general, a goal specifies a condition to be achieved at the end of the plan execution. In this article, we introduce nondeterministic planning for extended reachability goals (i.e., goals that also specify a condition to be preserved during the plan execution). We show that, when this kind of goal is considered, the temporal logic ctl turns out to be inadequate to formalize plan synthesis and plan validation algorithms. This is mainly due to the fact that the ctl’s semantics cannot discern among the various actions that produce state transitions. To overcome this limitation, we propose a new temporal logic called α-ctl. Then, based on this new logic, we implement a planner capable of synthesizing reliable plans for extended reachability goals, as a side effect of model checking.     

Topological optimization models for communication network with multiple reliability goals

Network reliability models for determining optimal network topology have been presented and solved by many researchers. This paper presents some new types of topological optimization model for communication network with multiple reliability goals. A stochastic simulation-based genetic algorithm is also designed for solving the proposed models. Some numerical examples are finally presented to illustrate the effectiveness of the algorithm.     

System-wide energy optimization for multiple DVS components and real-time tasks

Most dynamic voltage and frequency scaling (DVS) techniques adjust only CPU parameters; however, recent embedded systems provide multiple adjustable clocks which can be independently tuned. When considering multiple components, energy optimal frequencies depend on task set characteristics such as the number of CPU and memory access cycles. In this work, we propose a realistic energy model considering multiple components with individually adjustable frequencies such as CPUs, system bus and memory, and related task set characteristics. The model is validated on a real platform and shows less than 2% relative error compared to measured values. Based on the proposed energy model, we present an optimal static frequency assignment scheme for multiple DVS components to schedule a set of periodic real-time tasks. We simulate the energy gain of the proposed scheme compared to other DVS schemes for various task and system configurations, showing up to a 20% energy reduction. We also experimentally verify energy savings of the proposed scheme on a real hardware platform.     

Topological and dimensional synthesis of planar linkages for multiple kinematic tasks

This paper presents the combined use of two systematic methods for the synthesis of planar linkage mechanisms satisfying multiple kinematic tasks. First, a Graph Theory-based method is used to exhaustively enumerate the topological alternatives for a given problem. Then each feasible alternative is automatically dimensioned using the Precision Position Method; this computation includes space and design constraints. The existing methods to synthesize multiple tasks solve, in sequence, a decomposition of the problem into single kinematic tasks. The task decomposition and the topology selection for each task are usually performed by hand. This process leads to topologies with a repeated pattern and could lead to ignoring potentially desirable topologies. This paper analyzes a design strategy for the simultaneous solution of multiple kinematic tasks. This strategy has two advantages: (i) it eliminates the need for task decomposition, and (ii) it allows the exhaustive exploration of all non-isomorphic topologies up to a defined number of links. An example of simultaneous synthesis for a double rigid-body guidance task with application to a flap-tab mechanism is shown to illustrate the methodology.     

A multiple agent architecture for handwritten text recognition

This paper investigates the automatic reading of unconstrained omni-writer handwritten texts. It shows how to endow the reading system with learning faculties necessary to adapt the recognition to each writer's handwriting. In the first part of this paper, we explain how the recognition system can be adapted to a current handwriting by exploiting the graphical context defined by the writer's invariants. This adaptation is guaranteed by activating interaction links over the whole text between the recognition procedures of word entities and those of letter entities. In the second part, we justify the need of an open multiple-agent architecture to support the implementation of such a principle of adaptation. The proposed platform allows to plug expert treatments dedicated to handwriting analysis. We show that this platform helps to implement specific collaboration or cooperation schemes between agents which bring out new trends in the automatic reading of handwritten texts.     

Goals in conflict: semantic foundations of goals in agent programming

This paper addresses the notion of (declarative) goals as used in agent programming. Goals describe desirable states, and semantics of these goals in an agent programming context can be defined in various ways. We focus in this paper on the representation of conflicting goals. In particular, we define two semantics for goals, one for unconditional goals and one for conditional goals. The first is based on propositional logic, and the latter is based on default logic. We establish relations between and properties of these semantics. This title was inspired by the title of the PhD thesis of Harrenstein: Logic in conflict: logical explorations in strategic equilibrium [25].     

Validating an object placement planner for robotic pick-and-place tasks

This paper proposes an object placement planner for a grasped object during pick-and-place tasks. The proposed planner automatically determines the pose of an object that is stably placed near a user-assigned point on the environment surface. In our proposed method, first the polygon models of both the object and the environment are clustered, with each cluster being approximated by a planar region. The position/orientation of an object placed on the environment surface can be determined by selecting a pair of clusters: one from the object and the other from the environment. We furthermore conduct several tests to determine the position/orientation of the object, namely the Convexity Test, the Contact Test and the Stability Test. We demonstrate that, by using the polygon model of the environment that is obtained by means of conversion of the point cloud, we can determine the position/orientation of an object and can thereby realize a pick-and-place task.     

Reinforcement learning of multiple tasks using a hierarchical CMAC architecture

A reinforcement learning approach based on modular function approximation is presented. Cerebellar Model Articulation Controller (CMAC) networks are incorporated in the Hierarchical Mixtures of Experts (HME) architecture and the resulting architecture is referred to as HME-CMAC. A computationally efficient on-line learning algorithm based on the Expectation Maximization (EM) algorithm is proposed in order to achieve fast function approximation with the HME-CMAC architecture.

The Compositional Q-Learning (CQ-L) framework establishes the relationship between the Q-values of composite tasks and those of elemental tasks in its decomposition. This framework is extended here to allow rewards in non-terminal states. An implementation of the extended CQ-L framework using the HME-CMAC architecture is used to perform task decomposition in a realistic simulation of a two-linked manipulator having non-linear dynamics. The context-dependent reinforcement learning achieved by adopting this approach has advantages over monolithic approaches in terms of speed of learning, storage requirements and the ability to cope with changing goals.     

Game strategies among multiple cloud computing platforms for non-cooperative competing assignment user tasks

The Journal of Supercomputing - With the development of cloud computing technology, more and more cloud computing platforms are emerging which provide a great deal of computing services. But every...     

Schedulability and sensitivity analysis of multiple criticality tasks with fixed-priorities

Safety-critical real-time standards define several criticality levels for the tasks. In this paper we consider the real-time systems designed under the DO-178B safety assessment process (i.e., Software Considerations in Airborne Systems and Equipment Certification). Vestal introduced a new multiple criticality task model to efficiently take into account criticality levels in the schedulability analysis of such systems. Such a task model represents a potentially very significant advance in the modeling of safety-critical real-time softwares. Baruah and Vestal continue this investigation, with a new scheduling algorithm combining fixed and dynamic priority policies. Another major design issue is to allow a system developer to determine how sensitive is the schedulability analysis to changes in execution time of various software components.     

多搬运任务下考虑碰撞避免的AGV路径规划

为提升自动导引小车在“货到人”仓库中的运行效率,针对AGV-托盘任务分配、单AGV路径规划及多AGV碰撞避免三个子问题的研究,以最小化AGV行驶距离为目标构建数学模型。首先,根据AGV与托盘的双边匹配问题特点设计改进的匈牙利算法求解匹配结果。其次,提出一种二维编码机制的改进遗传算法（improved genetic algorithm,IGA）,采用一种局部搜索算子代替原变异操作,在提高算法搜索性能的基础上使其成功应用于单AGV路径规划问题。然后,利用时空数据设计一种三维网格冲突检测方法,并根据商品SKU数量设定AGV的优先级以降低多AGV执行任务时的碰撞概率。最后,在32 m×22 m的仓库中针对不考虑碰撞与考虑碰撞两种情形进行AGV路径优化分析,给出合理的行驶距离和碰撞次数。IGA与标准遗传算法的对比结果显示,IGA能够在合理的时间内获得更高质量的解,行驶距离减少约1.74%,算法求解时间缩短约37.07%。此外,针对AGV数量灵敏度分析,在不同目标托盘规模下测试不同数量的AGV对行驶距离和碰撞次数的影响,发现14~16台AGV数量是最佳配置,验证了模型的可行性和算法的有效性。     

River: an intermediate language for stream processing

This paper presents both a calculus for stream processing, named Brooklet, and its realization as an intermediate language, named River. Because River is based on Brooklet, it has a formal semantics that enables reasoning about the correctness of source translations and optimizations. River builds on Brooklet by addressing the real‐world details that the calculus elides. We evaluated our system by implementing front‐ends for three streaming languages, and three important optimizations, and a back‐end for the System S distributed streaming runtime. Overall, we significantly lower the barrier to entry for new stream‐processing languages and thus grow the ecosystem of this crucial style of programming. Copyright © 2015 John Wiley & Sons, Ltd.     

Local communication of multiple mobile robots: Design of optimal communication area for cooperative tasks

This paper presents an optimal design for local communication between multiple mobile robots. In previous studies of local communication in multirobot systems, the area of communication was not designed using mathematical analysis, but only time-consuming simulations of multirobot communications. We analyzed the information transmission efficiency and created an optimal communication area that minimizes the information transmission time to multiple robots. This optimization comprises two steps. First, we derive the “information transmission probability” for various task models. Next, the derived information transmission probability is used to minimize the information transmission time. The optimal communication design is tested for various tasks, using system parameters. The analytical results are further verified by using computer simulations of multirobot communications and experiments with local communication. © 1998 John Wiley & Sons, Inc.