多主体目标优化的动态合作博弈方法*

通过多目标优化和动态合作博弈理论,定义了联盟中多主体目标优化问题,提出了能够适应动态环境的基于合作博弈的多主体目标优化模型。该模型的组成一方面能够利用主体的协作能力,另一方面又能够充分考虑动态联盟的特征,适合大规模网络中多主体协作,避免模型中主体理性和团体理性的冲突。基于所提出的多主体目标优化模型,设计了一种联盟效用分配算法。仿真实验表明,联盟效用分配算法能够使多主体根据最优共识原则,分配各方的合作效用,从而达到多赢的帕累托最优局面。     

基于人工免疫系统的异构多机器人联盟形成

对动态环境下多机器人联盟形成问题进行了研究,提出了一种基于人工免疫系统的异构多机器人联盟形成方法.该方法在对比人工免疫系统与多机器人系统相似关系的基础上,利用人工免疫系统的隐喻机制为面向动态感知任务的异构多机器人联盟形成问题提供了一种新的思路和解决方法.进行了未知非结构化环境下的多机器人协作搜集仿真实验,仿真结果表明所提方法可以使多机器人系统自主地形成机器人联盟以完成动态感知任务,提高了多机器人系统执行任务的效率.     

基于协作协进化的多智能体机器人协作研究

协作问题一直是自主多智能体机器人系统研究的关键问题之一。基于多智能体机器人系统的CCP协作协议所生成的各智能体机器人的任务序列依赖于目标的初始顺序,因此难以得到最优解。文章提出了利用协作协进化来实现多智能体机器人之间协作的一种机制。该方法采用基于协作种群的技术来生成多智能体机器人任务执行序列,在给定的任务分解产生的所有可能解中寻找最优解,并通过交换局部知识和并行决策等手段来优化系统的性能。利用该机制,对3个智能体协作搬运8个物体进行计算机模拟,结果表明,该机制在优化任务执行序列方面作用明显,从而能有效提高多智能体机器人系统的性能。     

异构多机器人协作系统研究进展

异构多机器人系统可以发挥单一结构机器人在某个领域的优点而达到整体的最优配置,机器人的功能和接口协议对协作系统影响很大.IGRS协议是我国在信息设备协作领域中惟一的国际标准,为异构多机器人协作提供了有效的支持.对国内外相关研究进行系统地归纳和总结,找出需要解决的问题,并在课题组研制的多种机器人平台上,从3个方面阐述了基于IGRS协议的异构多机器人协作系统:异构机器人的定位、通信以及感知方案;异构机器人协商策略和分组方案;机器人的功能分类和规划,提出了细粒度可控的任务委托分配方案.     

多机器人协作无夹具焊接系统设计

与传统的单机器人系统相比,多机器人协作系统的设计存在诸如协作机器人之间的基坐标系标定、协作机器人末端位姿间的运动协调以及多机器人系统间的同步通讯控制等若干特殊性的关键问题.在初步论述多机器人协作系统设计中的若干问题的基础上,特别针对协作机器人间的基坐标系标定、工具手位姿约束分析和控制器通讯同步3个问题,进行了深入讨论,并介绍了实验室相关研究工作的结论.基于本文提供的解决方法,利用VA1400弧焊专用机器人和HP20通用型机器人基于主从式的分布控制结构组成了无夹具焊接实验系统,初步验证了提出的各问题解决方法的有效性.     

多机器人协作搬运路径规划研究

针对多机器人协作系统,提出了一种新的混合定点转动和遗传算法的方法,解决其协作路径规划问题。该方法利用遗传算法并行计算、不易陷入局部最优的优点,具备概率上寻找全局最优解的能力,同时结合了定点转动法易实现、有效减少单机器人路径浪费的优点。仿真实验结果表明,该规划方法运算速度较快,在得到有效规划路径的同时,也易于实现对单机器人的控制。     

三维环境中多机器人动态目标主动协作观测方法

动态目标的多移动机器人主动协作观测方法是指以获取较优的观测结果为目的, 对携带同构/异构观测传感器的多个机器人系统的观测数据进行有效融合并同时对其行为进行协调优化的方法. 本文主要研究了三维环境中的多机器人动态目标主动协作观测的问题. 首先, 以扩展集员估计方法(Extended set-membership filter, ESMF)为基础, 将信息融合过程与算法本身存在的集合运算环节相结合, 提出了一种高精度的多机器人观测信息融合方法. 该方法在保证较高观测精度的同时, 并没有显著增加单机器人扩展集员估计算法的计算量, 因此具有较高的实时性. 此外, 利用最优观测角度的概念, 通过引入相对速度空间(Relative velocity coordinates, RVCs), 设计了多移动机器人协调行为优化方法, 该方法可以将多机器人协调行为优化问题转化为线性规划问题, 以实现具有较高实时性的多机器人三维动态目标主动协作观测. 最后, 为了验证所研究方法的可行性与有效性, 进行了三维空间动态目标协作观测仿真实验.     

起重吊装多机器人系统协作避障规划研究

针对起重吊装多机器人系统实际作业情况,进行了该机器人系统协作避障规划研究.首先,基于栅格法提出了优化姿态转角--动态栅格法的路径规划改进算法;其次,设计了基于传感器技术的自适应协作系统.该协作避障方法具备了寻找全局最优路线的能力,同时实现了多机器人系统内部的稳定协调合作;最后,建立了Matlab与LabVIEW联合仿真系统,仿真结果表明该系统是可行且高效的.     

协作多机器人系统研究进展综述

协作多机器人系统是近年来机器人研究的热点,具有良好的应用前景.针对开展相关研究的迫切需要,总结了协作多机器人系统研究的国内外发展现状,并分别对无意识协作的仿生多机器人系统和有意识协作的异构多机器人系统研究进行论述.重点从系统构建和优化控制的宏观角度出发,对有意识协作异构多机器人系统的体系结构、环境感知以及优化控制3个方面的研究进展分别展开论述;对典型的多机器人验证任务和软硬件实验平台进行了总结;最后对协作多机器人系统的研究方向进行了展望.     

双机器人协作管管焊接的运动规划

针对中小管径管管相贯的马鞍形曲线焊缝,以双机器人协作为基础,提出一种应用于双机器人协作的焊接过程中机器人运动规划的方法。该方法基于焊缝、焊枪的数学模型,充分利用双机器人系统的冗余特性,提出了融合机器人操作度、关节位置等的多指标目标函数,以机器人关节极限为规划约束条件,采用遗传算法规划出最优机器人运动路径。在三维仿真环境中进行的仿真结果初步验证了该方法的可行性和有效性。     

一种适用于多机器人搜索动态目标的改进粒子群算法

粒子群算法引导机器人搜索跟踪动态目标时,在迭代后期易出现收敛停滞现象。为了改善上述情况,本文提出了结合牛顿法的改进粒子群算法。为了结合粒子群算法与牛顿法,我们在算法中引入了马尔科夫链,这使得机器人在每一次迭代时以一定的概率随机选择牛顿法或粒子群算法搜索跟踪目标。为了模拟机器人搜索动态目标的真实环境,本文还利用了通信项使机器人以一定的方式努力与基站保持通信,用来实时更新目标信息。仿真结果表明,改进的粒子群算法能有效的寻找并跟踪动态目标。     

An efficient approach for type II robotic assembly line balancing problems

In the past decades, robots have been extensively applied in assembly systems as called robotic assembly lines. When changes in the production process of a product take place, the line needs to be reconfigured in order to improve its productivity. This study presents a type II robotic assembly line balancing (rALB-II) problem, in which the assembly tasks have to be assigned to workstations, and each workstation needs to select one of the available robots to process the assigned tasks with the objective of minimum cycle time. An innovative genetic algorithm (GA) hybridized with local search is proposed for the problem. The genetic algorithm uses a partial representation technique, where only part of the decision information about a candidate solution is expressed in the chromosome and the rest is computed via a heuristic method. Based on different neighborhood structures, five local search procedures are developed to enhance the search ability of GA. The coordination between these procedures is well considered in order to escape from local optima and to reduce computation time. The performance of the hybrid genetic algorithm (hGA) is tested on 32 rALB-II problems and the obtained results are compared with those by other methods.     

湍流烟羽环境下多机器人主动嗅觉实现方法研究

给出了一种用于实现主动嗅觉(也称气味/气体源定位或化学烟羽跟踪)的多机器人协同搜索策略. 将蚁群算法与逆风搜索相结合用于协调多机器人的运动方向. 蚁群算法可有效调动机器人朝信息素高的区域运动且保证机器人之间的距离不会过大; 逆风搜索可降低算法过早地陷入局部最优的概率. 为正确判断转移方向, 蚁群算法中还增加了对历史信息的考虑. 在源头确认方面, 本文提出了气味/气体浓度持久性判断结合机器人旋转计算流体质量通量散度的方法. 仿真表明, 本文的主动嗅觉搜索策略可适用于湍流烟羽环境, 且可有效地逃脱浓度局部最优和风场的漩涡, 另外可最终确认源头位置.     

A PSO multi-robot exploration approach over unreliable MANETs

This paper proposes two extensions of Particle Swarm Optimization (PSO) and Darwinian Particle Swarm Optimization (DPSO), respectively denoted as RPSO (Robotic PSO) and RDPSO (Robotic DPSO), so as to adapt these promising biologically inspired techniques to the multi-robot systems domain, by considering obstacle avoidance and communication constraints. The concepts of social exclusion and social inclusion are used in the RDPSO algorithm as a ‘punish–reward’ mechanism, thus enhancing the ability to escape from local optima. Experimental results obtained in a simulated environment shows the superiority of the RDPSO evidencing that sociobiological inspiration can be useful to meet the challenges of robotic applications that can be described as optimization problems (e.g. search and rescue). Moreover, the performance of the RDPSO is further evaluated within a population of up to 12 physical robots under communication constraints. Experimental results with real platforms show that only 4 robots are needed to accomplish the herein proposed mission and, independently on the number of robots and maximum communication distance, the global optimum is achieved in approximately 90% of the experiments.     

BioDKM: Bio-inspired domain knowledge modeling method for humanoid delivery robots’ planning

A bio-inspired human domain knowledge modeling method, BioDKM, is proposed and developed to make delivery robots think more humanly and act more effectively. This presented method focused on feasible fusion between artificial intelligent and bionics in the field of tasks planning or scheduling in delivery robots. BioDKM is designed and implemented with several components, in terms of human knowledge, workflow (WF), hierarchical task network (HTN), and planner. In detail, WF is utilized as the human domain knowledge modeling tool, because of its convenient applications, friendly user interface and explicit representation. Moreover, WF can effectively complement conventional HTN planning with great convenience to formalize human domain knowledge. Translation from WF to HTN is also considered and established to make task planning smooth. Finally, examples and simulations are carried out to validate the effectiveness of this proposed bio-inspired domain knowledge modeling method.     

一种带有摄动的导向性蚁群算法

针对加入导向性局部搜索(Guided Local Search,GLS)的蚁群算法(Ant Colony Optimization,ACO)容易过早收敛的问题,提出一种带有摄动的导向性蚁群算法(Perturbation Guided Ant Colony Optimization,PGACO),该算法在当前解表现出过早收敛的趋势时,采用摄动(Perturbation)方式干扰解构建过程,使当前解移动到其邻域空间,从而产生一个新的可行解来避免算法过早收敛,提高算法求解的精度。实验结果表明,PGACO能有效地改善过早收敛问题,获得更优的可行解和执行速度,同时具有更强的全局搜索能力,能进一步提高算法的性能。     

A Linear Affect–Expression Space Model and Control Points for Mascot-Type Facial Robots

A robot's face is its symbolic feature, and its facial expressions are the best method for interacting with people with emotional information. Moreover, a robot's facial expressions play an important role in human-robot emotional interactions. This paper proposes a general rule for the design and realization of expressions when some mascot-type facial robots are developed. Mascot-type facial robots are developed to enable friendly human feelings. The number and type of control points for six basic expressions or emotions were determined through a questionnaire. A linear affect-expression space model is provided to realize continuous and various expressions effectively, and the effects of the proposed method are shown through experiments using a simulator and an actual robot system.     

Formation control of mobile robots with obstacle avoidance based on GOACM using onboard sensors

This paper deals with the problem of formation control for nonholonomic mobile robots under a cluttered environment. When the obstacles are not detected, the follower robot calculates its waypoint to track, based on the leader robot’s state. The proposed geometric obstacle avoidance control method (GOACM) guarantees that the robot avoids the static and dynamic obstacles using onboard sensors. Due to the difficulty for the robot to simultaneously get overall safe boundary of an obstacle in practice, a safe line, which is perpendicular to the obstacle surface, is used instead of the safe boundary. Since GOACM is executed to find a safe waypoint for the robot, GOACM can effectively cooperate with the formation control method. Moreover, the adaptive controllers guarantee that the trajectory and velocity tracking errors converge to zero with the consideration of the parametric uncertainties of both kinematic and dynamic models. Simulation and experiment results present that the robots effectively form and maintain formation avoiding the obstacles.     

Improved subspace modal identification of industrial robots

Industrial robots have become key components for manufacturing automations due to their larger workspaces and flexibility. However, low stiffness and high compliance of industrial robots may inevitably lead to vibration by self-excitation or periodic force dependent on workspace configuration. Therefore, the knowledge of the robot's modal properties should be accurately required to enhance the operation accuracy of industrial robots. To improve the identification accuracy of experimental modal parameters of field industrial robots, an improved subspace identification method is proposed to perform nonlinear iterative optimization for updating the state parameters of industrial robots. Experimental response measurement of a six-degrees-of-freedom industrial robot is carried out to obtain modal parameters under various poses. The identification results of the improved subspace modal method are preferable to that of the traditional method. Moreover, the reconstructed three-dimension working frequency space is presented to exactly characterize experimental modal frequencies throughout its workspace. The proposed method effectively improves the identification accuracy of modal parameters when compared with the traditional algorithms and the influence of robots' pose change on modal parameters is also investigated by experimental modal measurements.     

一种簇结构下的多移动机器人通信方法

针对集中式通信方式的不足,提出一种基于簇的多移动机器人通信方法.通过周期性地发送识别消息,建立簇结构并形成机器人通信网络.采用层次结构管理机器人,可以有效地降低通信网络的负载.在多移动机器人团队上进行了簇结构重构和数据传输实验,结果证明该通信方法具有良好的实用性.