小卫星编队飞行的自主控制与规划调度

本文针对小卫星编队飞行研究的重点问题,从功能分配的角度对小卫星编队飞行的自主控制系统进行设计,并针对小卫星编队飞行的规划和调度采用基于模型的智能推理方式进行工作这一特点,研究了小卫星编队飞行的规划和调度系统的体系结构。     

侦察卫星探测资源调度研究

侦察卫星探测资源调度是一类基于约束满足的优化问题。对卫星探测资源和探测任务的特点进行分析,在此基础上构建卫星探测资源调度的目标函数和约束条件,利用约束满足问题的建模思想对该调度问题进行建模。针对约束满足模型规模大、求解复杂的情况,结合卫星探测资源调度问题的特征,提出一种基于启发式禁忌搜索算法的模型求解方法,并通过仿真算例进行说明与分析。该调度模型和算法充分考虑了星载资源与对应任务的特点,尽量回避假设与简化条件的提出,具有较好的适用性,将为侦察与预警卫星网络任务规划与资源调度的研究奠定基础。     

基于CLIPS的卫星任务规划专家系统设计

本文提出了基于CLIPS的卫星任务规划专家系统的设计方法,详细分析了系统的结构和功能,重点讨论了中文产生式系统的BNF范式、基于上下文的推理机制和集合运算符。中文产生式系统的BNF范式基于CLIPS标准BNF范式定义,并依据BNF范式进行规则表示和规则自定义获取;推理机采用上下文限制的规则控制策略,依据不同的上下文加载相关的事实和规则,提高推理机的运行效率;利用规则中的对象逻辑子式进行了集合运算符的设计,并对极值运算符、属性差值运算符和均值运算符等三类集合运算符进行了探讨。该系统解决了卫星任务规划中知识表示和知识获取问题,提高了卫星任务规划推理效率,为卫星任务规划人员提供有效的辅助决策功能。     

基于贪心算法的科学卫星下行传输规划

本文针对多星、多站、变速率、大数据量的科学卫星数据下行传输规划问题,在开机时间约束条件下,考虑任务权重,建立了多卫星规划调度模型,提出了基于贪心算法的求解模型,并以两颗科学卫星的调度问题为例,对模型和算法进行了验证.     

基于工作流的卫星调度系统

根据当前卫星网络发展趋势和对卫星网络资源进行充分有效利用的应用需求,对卫星网络的特点进行了研究,分析了当前卫星调度系统研究的局限,并介绍了工作流模型以及根据卫星应用进行的相关改进.在此基础上,提出了一种针对由多个子任务构成的复杂任务,基于工作流模型的分层卫星调度系统.该卫星调度系统可以协同多种卫星资源,并自适应地调度卫星任务.对该卫星调度系统的体系结构、系统关键模块、调度流程以及几种优化机制进行了详细阐述.最后通过实验结果表明了该调度系统的可行性和有效性.     

航天器GN&C系统功能行为建模与仿真平台研究

在航天器近距离相对运动任务规划的研究中,为了实现验证对航天器制导、导航与控制(GN&C)系统的功能行为建模方法与仿真平台进行了研究.在分析了航天器GN&C系统的功能结构以及各子系统之间的状态约束,建立其功能行为模型的基础上,根据MVC(Model-View-Controller)设计模式搭建平台框架,综合利用Java/MATLAB/VC++,集成系统模型算法,驱动任务指令生成与调度,设计跨平台数据传输机制,完成仿真平台开发.系统仿真平台灵活开放,支持任务设计、规划功能,可实现飞行方案的仿真推演.     

卫星任务规划可视化仿真系统设计与实现

解决卫星任务规划方案正确性、可靠性问题的有效途径是建立可视化仿真分析环境,研究多星多地面站任务规划问题,设计了成像需求约束图模型,利用任务规划调度算法进行优化分析生成最优解决方案,同时研究利用基于MapX的地理信息系统功能,设计实现了卫星任务规划可视化仿真系统,实现了二维视景仿真、方案动作仿真以及可视化决策支持等功能.该系统为卫星任务规划的可视化决策、可视化仿真提供了基础,确保了其正确、可靠性.     

电子侦察卫星动态调度问题的快速启发式算法

卫星电子侦察中针对动态突发事件重新制定侦察计划是侦察任务规划所要解决的基本问题.分析导致动态调度的扰动因素,把不同扰动下的动态调度问题归结为一类复杂约束下的任务插入问题.建立混合整数规划模型,提出快速启发式任务插入算法(含三个基本算法--直接插入任务算法、移位插入任务算法和替换插入任务算法);基于初始调度方案的优良特性,极大降低动态调度复杂性.仿真实验表明:该算法能有效解决电子侦察卫星动态调度问题.     

卫星对地观测任务规划问题简明综述 *

卫星对地观测任务规划是为了满足用户的遥感图像需求 ,对遥感卫星系统资源和对地观测任务进行规划与调度的过程。合理的任务规划是提高遥感卫星系统效能的重要手段。为此 ,分析了卫星对地观测任务规划问题的主要特点 ,比较了这一问题的若干常用建模方法和求解技术 ,并探讨了卫星对地观测任务规划技术的未来发展趋势。     

基于层级任务网络的卫星自主规划模型与算法

针对面向任务的卫星操作计划自动生成(即卫星的自主规划)问题，采用层级任务网络规划的任务分解思想，在分析卫星系统结构的基础上，建立了光学成像侦察卫星的规划模型，定义了2种常规任务的任务分解方法。根据模型的特点，开发了一个基于任务分解方法的导引式求解算法。通过一个实例进行了验证，结果表明模型和算法能有效解决卫星的自主规划问题。     

IPSS: A Hybrid Approach to Planning and Scheduling Integration

Recently, the areas of planning and scheduling in artificial intelligence (AI) have witnessed a big push toward their integration in order to solve complex problems. These problems require both reasoning on which actions are to be performed as well as their precedence constraints (planning) and the reasoning with respect to temporal constraints (e.g., duration, precedence, and deadline); those actions should satisfy the resources they use (scheduling). This paper describes IPSS (integrated planning and scheduling system), a domain independent solver that integrates an AI planner that synthesizes courses of actions with constraint-based techniques that reason based upon time and resources. IPSS is able to manage not only simple precedence constraints, but also more complex temporal requirements (as the Allen primitives) and multicapacity resource usage/consumption. The solver is evaluated against a set of problems characterized by the use of multiple agents (or multiple resources) that have to perform tasks with some temporal restrictions in the order of the tasks or some constraints in the availability of the resources. Experiments show how the integrated reasoning approach improves plan parallelism and gains better makespans than some state-of-the-art planners where multiple agents are represented as additional fluents in the problem operators. It also shows that IPSS is suitable for solving real domains (i.e., workflow problems) because it is able to impose temporal windows on the goals or set a maximum makespan, features that most of the planners do not yet incorporate     

多推理机实时监控软件的研究与设计

多推理机实时监控软件是一个面向实时监控的系统软件,该软件不但能够保证监控进程的可靠运行,而且能够根据系统的负载状况进程自动调度。文章引进了航天器专家诊断系统平台,把航天器专家的知识表示为推理机能够处理的知识,推理机运行时加载这些知识,并结合当前航天器运行参数状态给出诊断结果。实践证明,采用该监控软件不但可以大大提高推理机运行的稳定性,还可以优化系统资源,动态调度推理机进程,从而大大提高了航天器管理效率,节约了大量的人力物力。     

A constraint programming formulation for planning: from plan scheduling to plan generation

Planning research is recently concerned with the resolution of more realistic problems as evidenced in the many works and new extensions to the Planning Domain Definition Language (PDDL) to better approximate real problems. Researchers’ works to push planning algorithms and capture more complex domains share an essential ingredient, namely the incorporation of new types of constraints. Adding constraints seems to be the way of approximating real problems: these constraints represent the duration of tasks, temporal and resource constraints, deadlines, soft constraints, etc., i.e. features that have been traditionally associated to the area of scheduling. This desired expressiveness can be achieved by augmenting the planning reasoning capabilities, at the cost of slightly deviating the planning process from its traditional implicit purpose, that is finding the causal structure of the plan. However, the resolution of complex domains with a great variety of different constraints may involve as much planning effort as scheduling effort (and perhaps the latter being more prominent in many problems). For this reason, in this paper we present a general approach to model those problems under a constraint programming formulation which allows us to represent and handle a wide range of constraints. Our work is based on the original model of , an optimal temporal planner, and it extends the ’s formulation to deal with more expressive constraints. We will show that our general formulation can be used for planning and/or scheduling, from scheduling a given complete plan to generating the whole plan from scratch. However, our contribution is not a new planner but a constraint programming formulation for representing highly-constrained planning + scheduling problems.     

Temporal Reasoning for a Collaborative Planning Agent in a Dynamic Environment

We present a temporal reasoning mechanism for an individual agent situated in a dynamic environment such as the web and collaborating with other agents while interleaving planning and acting. Building a collaborative agent that can flexibly achieve its goals in changing environments requires a blending of real-time computing and AI technologies. Therefore, our mechanism consists of an Artificial Intelligence (AI) planning subsystem and a Real-Time (RT) scheduling subsystem. The AI planning subsystem is based on a model for collaborative planning. The AI planning subsystem generates a partial order plan dynamically. During the planning it sends the RT scheduling subsystem basic actions and time constraints. The RT scheduling subsystem receives the dynamic basic actions set with associated temporal constraints and inserts these actions into the agent's schedule of activities in such a way that the resulting schedule is feasible and satisfies the temporal constraints. Our mechanism allows the agent to construct its individual schedule independently. The mechanism handles various types of temporal constraints arising from individual activities and its collaborators. In contrast to other works on scheduling in planning systems which are either not appropriate for uncertain and dynamic environments or cannot be expanded for use in multi-agent systems, our mechanism enables the individual agent to determine the time of its activities in uncertain situations and to easily integrate its activities with the activities of other agents. We have proved that under certain conditions temporal reasoning mechanism of the AI planning subsystem is sound and complete. We show the results of several experiments on the system. The results demonstrate that interleave planning and acting in our environment is crucial.     

多项目多任务选择计划模型及其免疫遗传算法

针对多资源受限下项目群选择计划及项目流程调度问题,依据项目的权重和承继约束,将项目群按权值从大到小进行排序,各项目内部的任务分别采用网络计划图进行拓扑排序,并以资源约束和合同期等因素为项目选择标准,建立多项目多任务选择计划的资源配置数学模型。进而,在遗传算法中引入免疫系统的记忆性和多样性功能,设计免疫遗传算法求解所获模型的最佳决策方案。比较性的数值实验结果说明了模型的合理性和算法的有效性。     

Realization of multi-agent planning system for autonomous spacecraft

To achieve fully autonomy of deep space spacecraft, onboard planning is a crucial technology. Though, lots of successful applications about it in spacecraft operations have recently been used in real or experimental flight, the inherent distribution and concurrency of the spacecraft subsystem was not taken into consideration. To improve the efficiency of planning system, a multi-agent planning system (MAPS) of autonomous spacecraft is proposed in this paper. MAPS subdivides the search space into some small ones and every PA manages and operates in its own space. With the help of planning manager agent (PMA), all of the planning agents communicate and cooperate with each other to produce partial plan which satisfied all the constraints of the system. To be capable of describing simultaneous activity, continue time, resource and temporal constraints in MAPS, a new planning formal model is given firstly. As the application of this architecture, an MAPS prototype system for deep spacecraft, which is realized with Java language, is designed.     

战场环境中多无人机动态任务调度

采用了基于合同网的分布式规划方法，研究了战场环境中多无人机动态任务调度问题，并建立了数学模型，提出了分布式的任务调度体系结构，设计了一种基于代价变换的概率路标图路径规划算法，该算法能够在任务调度阶段，快速预估无人机执行不同任务的飞行航路，扩展了合同网协议，可在一次拍卖中“并发”进行多次交易，提高了任务调度的效率。通过多种合同类型的综合，解决了复杂战场态势下的任务调度问题。     

A CSP model for simple non-reversible and parallel repair plans

This work presents a constraint satisfaction problem (CSP) model for the planning and scheduling of disassembly and assembly tasks when repairing or substituting faulty parts. The problem involves not only the ordering of assembly and disassembly tasks, but also the selection of them from a set of alternatives. The goal of the plan is the minimization of the total repairing time, and the model considers, apart from the durations and resources used for the assembly and disassembly tasks, the necessary delays due to the change of configuration in the machines, and to the transportation of intermediate subassemblies between different machines. The problem considers that sub-assemblies that do not contain the faulty part are nor further disassembled, but allows non-reversible and parallel repair plans. The set of all feasible repair plans are represented by an extended And/Or graph. This extended representation embodies all of the constraints of the problem, such as temporal and resource constraints and those related to the selection of tasks for obtaining a correct plan.     

软件人群体智能自律协调模型研究及应用

在智能自律分散系统的构建中引入面向服务的协调机制, 提出一种面向服务的软件人群体智能自律协调(SoftMan group intelligent autonomous coordination, SMGIAC)模型. 把服务共同体思想和全局规划有机结合起来, 考虑基于角色的服务发现/绑定策略, 构建面向服务的元组空间, 为群体协作提供统一的服务接口. 针对数字气田管网调度实际需求, 给出SMGIAC数学建模方法、分解协调引理及其子服务(流)构造算法. 实验对比结果说明, 比较现有案例匹配、专家推理两种方法, SMGIAC总体调度准确度有较大提高, 并克服了ReO软件使用不便、调度方案生成时间长的缺点.     

Group planning with time constraints

Embedding planning systems in real-world domains has led to the necessity of Distributed Continual Planning (DCP) systems where planning activities are distributed across multiple agents and plan generation may occur concurrently with plan execution. A key challenge in DCP systems is how to coordinate activities for a group of planning agents. This problem is compounded when these agents are situated in a real-world dynamic domain where the agents often encounter differing, incomplete, and possibly inconsistent views of their environment. To date, DCP systems have only focused on cases where agents’ behavior is designed to optimize a global plan. In contrast, this paper presents a temporal reasoning mechanism for self-interested planning agents. To do so, we model agents’ behavior based on the Belief-Desire-Intention (BDI) theoretical model of cooperation, while modeling dynamic joint plans with group time constraints through creating hierarchical abstraction plans integrated with temporal constraints network. The contribution of this paper is threefold: (i) the BDI model specifies a behavior for self interested agents working in a group, permitting an individual agent to schedule its activities in an autonomous fashion, while taking into consideration temporal constraints of its group members; (ii) abstract plans allow the group to plan a joint action without explicitly describing all possible states in advance, making it possible to reduce the number of states which need to be considered in a BDI-based approach; and (iii) a temporal constraints network enables each agent to reason by itself about the best time for scheduling activities, making it possible to reduce coordination messages among a group. The mechanism ensures temporal consistency of a cooperative plan, enables the interleaving of planning and execution at both individual and group levels. We report on how the mechanism was implemented within a commercial training and simulation application, and present empirical evidence of its effectiveness in real-life scenarios and in reducing communication to coordinate group members’ activities.