多机器人覆盖技术研究进展

系统地总结了当前覆盖问题的定义、分类和应用前景.对多机器人覆盖中关于通信、环境地图、路径规划算法及效果评价等方面的研究进展情况进行了阐述.分析并指出若干多机器人覆盖研究中的重点和难点问题:体系结构、通信技术、协商协作、地图表示、路径规划及效果评价,并对未来的研究发展方向进行了探讨.     

云决策分析方法

提出一种基于不确定性人工智能的决策分析方法.该方法利用云理论中的云变换和概念提升方法来刻画属性因素的概念云模型,并对每个因素都分配相应的云权重;使用X-条件云和极大判定法,找出每个因素的最佳评判语言云模型;让云权重参与综合评判,最终通过云计算得到基于云滴分布的综合评价结果.实验结果表明,与传统的模糊决策分析相比,它更具有易理解性,广普适性和高准确性等特点.

     

基于强化学习的未知环境多机器人协作搜集

针对多机器人协作复杂搜集任务中学习空间大,学习速度慢的问题,提出了带共享区的双层强化学习算法。该强化学习算法不仅能够实现低层状态-动作对的学习,而且能够实现高层条件-行为对的学习。高层条件-行为对的学习避免了学习空间的组合爆炸,共享区的应用强化了机器人间协作学习的能力。仿真实验结果说明所提方法加快了学习速度,满足了未知环境下多机器人复杂搜集任务的要求。     

深海采矿机器人的行走鲁棒控制

针对深海采矿机器人模型和外界环境不确定性而导致其控制问题异常复杂这一难题,首先用RBF神经网络逼近系统不确定性的上界,在状态反馈的基础上,构造了基于虚拟输入的航向控制鲁棒自适应律;然后给出了左右履带速度输入与虚拟输入之间的模糊控制推理规则,提出了采矿机器人的行走控制方法.仿真结果验证了所提出方法的可行性.

     

含不可控变迁的Petri网监控器设计

在基于Petri网建模的含不可控事件的离散事件系统监控器设计中,当给定的控制目标为Parikh矢量约束时,提出通过转换矩阵R将不可控变迁导致的非法不等式约束变换为允许约束,并求得相应监控器.构造矩阵方程求解R,通过矩阵方程的相容性判断R的存在性,并给出利用广义逆矩阵求解R的算法,得到对应的允许约束和监控器.同时提出代价函数,用于寻找控制观测代价最小的监控器.最后通过实例验证了该算法的正确性和有效性.     

力矩受限的机器人吸引域估计方法

针对力矩受限的机器人组合非线性反馈控制的局部稳定区域描述问题,研究了吸引域的估计方法.利用不变集属性和椭球性质,定义两种不同意义的最大椭球不变集来逼近吸引域,分别采用设置初始状态法和参考形状集法求解.通过带有约束的优化问题描述,所有条件均能转化为线性矩阵不等式条件,易于求解.由于采用优化技术,能够减小吸引域估计的保守性.数值算例验证了所提方法的有效性.

     

一种辨识Wiener-Hammerstein模型的新方法

针对非线性Wiener-Hammerstein模型,提出利用粒子群优化算法对非线性模型进行辨识的新方法.该方法的基本思想是将非线性系统的辨识问题转化为参数空间上的优化问题;然后采用粒子群优化算法获得该优化问题的解.为了进一步增强粒子群优化算法的辨识性能,提出利用一种混合粒子群优化算法.最后,仿真结果验证了该方法的有效性和可行性.

     

连续随机时滞系统L2-L∞滤波设计

研究了连续随机时滞系统的L2-L∞滤波器的设计问题.利用引入参数变量的方法,以线性矩阵不等式形式,给出依赖于时滞的充分条件,从而保证滤波误差方程随机渐近稳定且满足L2-L∞性能指标,且所得结果较以往结果具有更小的保守性.仿真例子表明了该方法的有效性.     

模糊多准则决策方法研究综述

模糊多准则决策是当前决策领域的一个研究热点,在实际决策中有着广泛的应用.为此,介绍了基于模糊数、直觉模糊集和Vague集的多准则决策方法和语言多准则决策方法的研究现状,定义了直觉梯形模糊数和区间直觉梯形模糊数,扩展了模糊数和直觉模糊集.最后探讨了目前模糊多准则决策要解决的问题和发展方向.     

基于一致性理论的多机器人系统队形控制

首先回顾了多机器人系统队形控制方面的成果;然后提出一个多机器人队形控制的模型．该模型可描述多机器人之间相互作用固定和动态切换两种通信拓朴结构,也能描述多机器人系统队形的分布式控制方法和基于leader的控制方法,还能表示多机器人系统奔向目标点的行为,在此基础上,利用一致性理论,对系统的稳定性条件进行分析．最后通过仿真证明了该方法的有效性．     

Cooperative multi-robot systems:: A study of vision-based 3-D mapping using information theory

Building cooperatively 3-D maps of unknown environments is one of the application fields of multi-robot systems. This article addresses that problem through a probabilistic approach based on information theory. A distributed cooperative architecture model is formulated whereby robots exhibit cooperation through efficient information sharing. A probabilistic model of a 3-D map and a statistical sensor model are used to update the map upon range measurements, with an explicit representation of uncertainty through the definition of the map’s entropy. Each robot is able to build a 3-D map upon measurements from its own range sensor and is committed to cooperate with other robots by sharing useful measurements. An entropy-based measure of information utility is used to define a cooperation strategy for sharing useful information, without overwhelming communication resources with redundant or unnecessary information. Each robot reduces the map’s uncertainty by exploring maximum information viewpoints, by using its current map to drive its sensor to frontier regions having maximum entropy gradient. The proposed framework is validated through experiments with mobile robots equipped with stereo-vision sensors.     

Cooperative localization method for multi-robot based on PF-EKF

A method of cooperative localization for multi-robot in an unknown environment is described. They share information and perform localization by using relative observations and necessary communication. At initial time, robots do not know their positions. Once the robot that can obtain the absolute position information has its position, other robots use particle filter to fuse relative observations and maintain a set of samples respectively representing their positions. When the particles are close to s Gsussian distribution after a number of steps, we switch to an EKF to track the pose of the robots. Simulation results and real experiment show that PF-EKF method combines the robustness of PF and the efficiency of EKF. Robots can share the absolute position information and effectively localize themselves in an unknown environment.     

Robust multi-robot coordination in pick-and-place tasks based on part-dispatching rules

This paper addresses the problem of realizing multi-robot coordination that is robust against pattern variation in a pick-and-place task. To improve productivity and reduce the number of parts remaining on the conveyor, a robust and appropriate part flow and multi-robot coordinate strategy are needed. We therefore propose combining part-dispatching rules to coordinate robots, by integrating a greedy randomized adaptive search procedure (GRASP) and a Monte Carlo strategy (MCS). GRASP is used to search for the appropriate combination of part-dispatching rules, and MCS is used to estimate the minimum-maximal part flow for one combination of part-dispatching rules. The part-dispatching rule of first-in–first-out is used to control the final robot in the multi-robot system to pick up parts left by other robots, and the part-dispatching rule of shortest processing time is used to make the other robots pick up as many parts as possible. By comparing it with non-cooperative game theory, we verify that the appropriate combination of part-dispatching rules is effective in improving the productivity of a multi-robot system. By comparing it with a genetic algorithm, we also verify that MCS is effective in estimating minimum-maximal part flow. The task-completion success rate derived via the proposed method reached 99.4% for 10,000 patterns.     

基于人工神经网络的多机器人协作学习研究

机器人足球比赛是一个有趣并且复杂的新兴的人工智能研究领域，它是一个典型的多智能体系统。文中主要研究机器人足球比赛中的协作行为的学习问题，采用人工神经网络算法实现了两个足球机器人的传球学习，实验结果表明了该方法的有效性。最后讨论了对BP算法的诸多改进方法。     

Selective Smooth Fictitious Play: An approach based on game theory for patrolling infrastructures with a multi-robot system

The multi-robot patrolling problem is defined as the activity of traversing a given environment. In this activity, a fleet of robots visits some places at irregular intervals of time for security purpose. To date, this problem has been solved with different approaches. However, the approaches that obtain the best results are unfeasible for security applications because they are centralized and deterministic. To overcome the disadvantages of previous work, this paper presents a new distributed and non-deterministic approach based on a model from game theory called Smooth Fictitious Play. To this end, the multi-robot patrolling problem is formulated by using concepts of graph theory to represent an environment. In this formulation, several normal-form games are defined at each node of the graph. This approach is validated by comparison with best suited literature approaches by using a patrolling simulator. The results for the proposed approach turn out to be better than previous literature approaches in as many as 88% of the cases of study. Moreover, the novel approach presented in this work has many advantages over other approaches of the literature such distribution, robustness, scalability, and dynamism. The achievements obtained in this work validate the potential of game theory to protect infrastructures.     

基于改进细菌觅食算法的测试用例生成方法

针对测试用例自动化生成技术中效率较低的问题,尝试引入新的细菌觅食算法,并结合测试用例生成问题提出了一种基于细菌觅食算法的改进算法（IM-BFOA）。IM-BFOA首先采用Kent映射来增加细菌的初始种群和全局搜索的多样性,其次针对算法中趋化阶段的步长进行自适应设计,使其在细菌趋化过程中更加合理化,并通过实验仿真验证其合理性,最后根据被测程序构造适应度函数来加速测试数据的优化。实验结果表明,与遗传算法（GA）、粒子群优化（PSO）算法和标准细菌觅食优化算法（BFOA）相比,该算法在保证覆盖率的前提下,在迭代次数和运行时间方面都是较优的,可有效提高生成测试用例的效率。     

基于菌群优化算法的非线性系统模型参数辨识

非线性系统模型参数估计一直是自动控制领域的研究热点。针对非线性系统,结合菌群优化(BSFO)算法的特点,提出了一种新型的非线性系统模型参数辨识方法。通过将待辨识参数设置为群体细菌在参数空间的位置,并模拟细菌群体觅食的动态行为来实现对系统参数的辨识,有效地提高了参数辨识的精度和效率。通过对重油热解三集总模型进行了仿真研究,得到了较为精确的过程模型,模型输出与实际输出基本一致。仿真结果表明,菌群优化算法为非线性系统模型参数估计提供了一种有效的途径。     

Operation allocation and part type selection in E-manufacturing: An auction based heuristic supported by agent technology

This paper enlightens some of the key issues involved in developing real schedule generation architecture in E-manufacturing environment. The high cost, long cycle time of development of shop floor control systems and the lack of robust system integration capabilities are some of the major deterrents in the development of the underlying architecture. We conceptualize a robust framework, capable of providing flexibility to the system, communication among various entities and making intelligent decisions. Owing to the fast communication, distributed control and autonomous character, agent-oriented architecture has been preferred here to address the scheduling problem in E-manufacturing. An integer programming based model with dual objectives of minimizing the makespan and increasing the system throughput has been formulated for determining the optimal part type sequence from the part type pool. It is very difficult to appraise all possible combinations of the operation-machine allocations in order to accomplish the above objectives. A combinatorial auction-based heuristic has been proposed to minimize large search spaces and to obtain optimal or near-optimal solutions of operation-machine allocations of given part types with tool slots and available machine time as constraint. We have further shown the effects of exceeding the planning horizon due to urgency of part types or over time given to complete the part type processing on shop floor and observed the significant increase in system throughput.