Discrete-event modeling for internet multi-robotics

Internet multi-robotics is a typical discrete-event system. In order to describe joint activities between multiple operators and multiple robots, a 4-level discrete-event model is proposed in this paper based on the controlled condition/event Petri nets (CCEP). On the first or mission level, the task splitting of the system is defined; on the second or multi-operator level, a precedence graph is introduced for every operator to plan his or her robotic actions; on the third or coordination level, the above precedence graphs are translated and integrated into the corresponding CCEPs in terms of specific rules; and on the last or multi-robot level, operators can select their control range by setting the corresponding control marks of the obtained CCEPs. As a consequence, a clear mechanism of operator-robot collaboration is obtained to conduct the development of the system.     

基于Petri网的并行任务规划器

本文具体讨论了基于Ｐｅｔｒｉ网理论的并行任务规划器的设计与实现，提出将Ｐｅｔｒｉ网结合人工智能技术构造出基于网的智能系统来描述计算机集成制造系统（ＣＩＭＳ）的思路．该并行任务规划器已在ＳＵＮＳＰＡＲＣ机器上用Ｃ语言实现，达到预期设计目的     

基于适应度的多机器人任务分配策略

为提高多机器人任务分配方法的通用性和实用性，提出了一种分布式多机器人协作任务分配策略.根据多机器人任务特点，提出了多机器人任务分配必须满足的4个基本目标，并由目标制定相应的4条任务选择策略.按形式化思想为子任务建立了一个通用模型，模型包含了与任务选择相关的子任务基本属性和运行状态.结合子任务模型和任务选择策略，建立了一个子任务适应度模型，包含静态适应度、进度适应度、资源适应度和外部适应度4个分量，机器人根据适应度大小来选择子任务.仿真实验表明，应用该任务分配策略的协作异构多机器人系统对一类搬运任务具有很好的通用性，当任务发生变化和机器人发生故障时，机器人能够正确、及时地调整子任务，系统具有很好的实时性、灵活性和鲁棒性.系统能够实现机器人到子任务的最优映射，与其他任务分配方法相比明显缩短了任务执行时间.     

基于Petri网的Internet多机器人协调控制器设计

基于Petri网库所不变量理论提出Internet多机器人协调控制模型,并利用此模型解决系统操作中的碰撞检测问题．通过对两个机器人协作空间进行网格划分,引入隐式、显式基本操作概念简化Petri网模型,从而增强了系统的灵活性;对操作中的异常情况进行分类,建立了故障恢复Petri网;利用库所不变量理论设计机器人协调控制器,规划多机器人协调动作．仿真实验表明,所提控制模型可以有效满足系统的安全性和实时性要求．     

基于虚拟向导的多操作者多机器人遥操作系统

网络传输时延的不确定性和机器人之间的有效协调是基于Internet的多操作者多机器人遥操作系统研究的关键问题.采用Java和Java3D技术构建了基于Web的虚拟操作环境,改变了传统上基于视频信息作为反馈的操作模式,克服了网络传输时延对系统操作性能的影响.提出了基于虚拟向导的概念并加以实现,处于异端的操作者能够准确地预测其他操作者的操作意图并实现有效的协调动作.最后,通过两个典型操作实例的分析,验证了系统的合理性与可行性.     

Internet based design of distributed virtual environment for multi-robot teleoperation

Recently,withtheapplicationoftheInternetand thedevelopmentoftele robotics,manyrobotshavebeenlinkedtotheInternetandhaveshowntheirvalueinthefieldsofdevicesharing,remotetraining,distrib utedcooperation,remotesurgeries,andentertainment.Comparedwithconventiona…     

Collision avoidance planning in multi-robot system based on improved artificial potential field and rules

For real-time and distributed features of multi-robot system,the strategy of combining the improved artificial potential field method and the rules based on priority is proposed to study the collision avoidance planning in multi-robot systems. The improved artificial potential field based on simulated annealing algorithm satisfactorily overcomes the drawbacks of traditional artificial potential field method,so that robots can find a local collision-free path in the complex environment. According to the movement vector trail of robots,collisions between robots can be detected,thereby the collision avoidance rules can be obtained. Coordination between robots by the priority based rules improves the real-time property of multi-robot system. The combination of these two methods can help a robot to find a collision-free path from a starting point to the goal quickly in an environment with many obstacles. The feasibility of the proposed method is validated in the VC-based simulated environment.     

密集观测场景下的敏捷成像卫星任务规划方法

针对密集观测场景下敏捷成像卫星任务规划问题求解空间大、输入任务序列较长的特点,综合考虑时间窗口约束、任务转移时卫星姿态调整时间、存储约束和电量约束,对敏捷成像卫星任务规划问题进行建模. 提出融合IndRNN和Pointer Networks的算法模型（Ind-PN）对敏捷成像卫星任务规划问题进行求解,使用多层的IndRNN结构作为算法模型的解码器. 基于Pointer Networks机制对输入任务序列进行选择,使用Mask向量考虑敏捷成像卫星任务规划问题中的各类约束. 基于Actor Critic强化学习算法对算法模型进行训练,以获得最大的观测收益率. 实验结果表明,对于密集观测场景下的任务规划,Ind-PN算法的收敛速度更快,可以获得更高的观测收益率.     

基于蚁群算法的改进装配序列规划方法

机械产品的装配序列直接影响到产品质量和成本,装配序列规划是产品设计的重要内容.为快速实现产品装配序列规划,根据零部件间的装配优先关系,提出了一种改进的装配序列规划方法,并基于蚁群算法予以实现.该方法充分考虑装配过程中的几何约束关系,并引入稳定性、装配经验等因素的影响,以稳定性、装配经验、装配工具和方向的改变次数为影响因子构建优化目标评价体系,使所得解更具有实际指导意义.在求解过程中,根据几何约束关系运用动态候选集合策略限制蚁群的搜索空间,提高了搜索效率;考虑稳定性因素的影响改进了蚁群的状态转移规则,同时引入局部-全局信息素更新规则,保证了蚁群获得最优或近优解.最后,通过实例研究验证了算法的可行性和高效性.     

基于改进指针网络的卫星对地观测任务规划方法

针对卫星观测任务规划问题约束复杂、求解空间大和输入任务序列长度不固定的特点,使用深度强化学习（DRL）方法对卫星观测任务规划问题进行求解. 综合考虑时间窗口约束、任务间转移机动时间和卫星电量、存储约束,对卫星观测任务规划问题进行建模. 基于指针网络（PN）的运行机制建立序列决策算法模型,使用Mask向量来考虑卫星观测任务规划问题中的各类约束,并通过Actor Critic强化学习算法对模型进行训练,以获得最大的收益率. 借鉴多头注意力（MHA）机制的思想对PN进行改进,提出多头注意力指针网络（MHA-PN）算法. 根据实验结果可以看出,MHA-PN算法显著提高了模型的训练速度和泛化性能,训练好的MHA-PN算法模型可以直接对输入序列进行端到端的推理,避免传统启发式算法迭代求解的过程,具有较高的求解效率.     

基于合同网的多agent任务分配分布式优化算法

针对合同网下的多agent系统,基于集合覆盖理论提出了一种解决子任务分配的严格启发式搜索算法;并分析了该算法的收敛性及渐进时间复杂度;证明了其搜索结果的上确界．该算法具有分布性,搜索空间缩减快,适合于中小型的多agent系统的子任务分配．     

Global optimal path planning for mobile robot based on improved Dijkstra algorithm and ant system algorithm

A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps： the first step is adopting the MAK-LINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.     

Timeline based autonomous mission planning system for deep space exploration

In order to realize the explorer autonomy, the software architecture of autonomous mission management system (AMMS) is given for the deep space explorer, and the autonomous mission planning system, the kernel part of this architecture, is designed in detail. In order to describe the parallel activity, the state timeline is introduced to build the formal model of the planning system and based on this model, the temporal constraint satisfaction planning algorithm is proposed to produce the explorer‘ s activity sequence. With some key subsystems of the deep space explorer as examples, the autonomous mission planning simulation system is designed.The results show that this system can calculate the executable activity sequence with the given mission goals and initial state of the explorer.     

Comprehensive assessment method for environmental impact of railway based on geographic information system

By integrating the merits of the map overlay method and the geographic information system (GIS), a GIS based map overlay method was developed to analyze comprehensively the environmental vulnerability around railway and its impact on the environment, which is adapted for the comprehensive assessment of railway environmental impact and the optimization of railway alignments. The assessment process of the GIS based map overlay method was presented, which includes deciding the system structure and weights of assessment factors, making environmental vulnerability grade maps, and evaluating the alternative alignments comprehensively to obtain the best one. With the GIS functions of spatial analysis, such as overlay analysis and buffer analysis, and functions of handling attribute data, the GIS based map overlay method overcomes the shortcomings of the existing map overlay method and the conclusion is more reasonable. In the end, a detailed case study was illustrated to verify the efficiency of the method.