基于行为的多机器人队形控制仿真研究

采用基于行为的控制方法,设计了多机器人队形控制的五个子行为:奔向目标、躲避障碍物、围绕障碍物、保持队形和随机扰动。在子行为设计中引入了阶梯控制区法,提高了队形控制的容错性。使用Teambots仿真平台对典型的直线队形控制进行了仿真。针对单向虚拟参照点法队形控制不稳定的情况,采用双向虚拟参考点法对控制方法进行改进,根据运行时间对改进的队形控制方法进行了评估,实验结果表明,改进的方法大幅提高了队形控制的稳定性与系统运行效率。     

多机器人任意队形分布式控制研究

本文针对多智能体协作完成特定任务时难以在全自主控制的前提下协作形成任意队形和队形向量不易确定的问题,通过由各智能体自主简单的确定自己的队形向量,从理论上扩展基于队形向量的队形控制原理以生成任意队形,改进机器人的运动方式以提高收敛速度,提出一种快速收敛的机器人部队任意队形分布式控制算法．为了解决智能体机器人之间的冲突问题,提出了一个通信协调模型．仿真实验和实际机器人实验均表明了算法的可行性和有效性．     

基于行为的机器人部队队形控制方案

提出了一种适用于实时动态环境下机器人力队的基于行为的分布式实时队形控制算法。研究了这种基于行为的方案在遇到大体积障碍物时行为,仿真过程表明该法既能使机器人动态分组、各自规划,又能机器人部队作为一个整体维持队形。该算法可使机器人形成和保持任意队形,同时集成了避障和导航的能力。最后指出了算法在控制机器人团队转向或避障运动时的问题出现的原因以及改进方法。     

基于行为的机器人自适应队形控制

针对多机器人在未知复杂环境下的队形控制问题,将leader-follower法结合到基于行为法中,提出了机器人在复杂环境下采取跟踪链的方式穿越障碍,而后再重新组队,使机器人适应环境的能力增强,避免了机器人在复杂环境下掉队的现象.在避障活动障碍时,依据障碍运动趋势有预见的主动避开,使控制行为既简单又有效,仿真结果表明该队...     

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．     

基于队形向量的机器人部队队形控制

多智能体协作完成特定任务是多智能体领域的一个基本问题 .本文结合多智能体理论和基于队形向量的队形控制算法 ,提出了一种改进的基于队形向量的控制机器人部队形成任意形状的队形的分布式队形控制算法 DFC.仿真的实验结果证明 ,该算法比现有算法功能完备 ,控制简单     

多机器人队形保持与变换仿真研究

针对多机器人的队形保持与变换任务,采用基于行为的控制方法,设计了五种子行为,即奔向目标、躲避障碍物、围绕障碍物、随机扰动和保持队形.对有障碍物环境下的队形保持进行了仿真,并针对机器人队伍无法保持队形通过的地形,采用邻居参照点法与领航参照点法相结合的策略,改进了控制方法.仿真结果表明,使用改进的控制方法,机器人队伍可以安全地通过大直径障碍物,并且提高了队形稳定性.     

一种改进的多机器人任意队形控制算法

针对快速收敛的机器人部队任意队形控制算法中存在的问题,提出一种改进的多机器人系统模型和控制算法．新方案中详细讨论了基于全局通信的机器人集合划分方式,机器人可以以多种策略选择跟踪对象,并且新的系统模型和控制算法能够用于具有不同高度的机器人、目标和障碍物的情形．理论分析表明新方案尽可能多地减少智能体机器人之间的冲突及等待时间,更接近实际应用．     

编队控制中的机器人行为与基于服务的运动行为结构设计

以基于位姿误差的虚拟目标跟踪为控制模式,提出了独立于编队控制与协作任务,但却适用于多移动机器人编队运动的基于服务的机器人运动行为结构,并对其基本运动行为与选择方法进行了设计.提出的基于服务的机器人行为结构借鉴了软件工程领域的面向服务的架构思想,具有可重用性.多机器人编队形成、改变等仿真与实验验证了本文所提方法的有效性.     

多移动机器人编队的行为选择控制系统

针对多机器人在编队行进过程中的行为选择问题进行了分析,提出一种实现多移动机器人编队的行为选择机制。通过计算机仿真和实验研究,结果表明该控制策略能很好的实现多机器人快速编队,并在编队过程中实现运动状态的平滑变化,提高了整个系统性能。     

Multiagent-Based Multi-team Formation Control for Mobile Robots

In the field of formation control, researchers generally control multiple robots in only one team, and little research focuses on multi-team formation control. In this paper, we propose an architecture, called Virtual Operator MultiAgent System (VOMAS), to perform formation control for multiple teams of mobile robots with the capabilities and advantages of scalability and autonomy. VOMAS is a hybrid architecture with two main agents. The virtual operator agent handles high level missions and team control, and the robot agent deals with low level formation control. The virtual operator uses four basic services including join, remove, split, and merge requests to perform multi-team control. A new robot can be easily added to a team by cloning a new virtual operator to control it. The robot agent uses a simple formation representation method to show formation to a large number of robots, and it uses the concept of potential field and behavior-based control to perform kinematic control to keep formation both in holonomic and nonholonomic mobile robots. In addition, we also test the stability, robustness, and uncertainty in the simulation. This research was supported by the National Science Council under grant NSC 91-2213-E-194-003.     

任意初始位置机器人领航跟随型迭代学习编队

针对移动机器人编队形成与队形保持问题,提出了一种适用于任意初始位置条件下的迭代学习编队控制算法。采用领航-跟随型编队法,仅利用领航者的运动轨迹和期望的编队队形推导出跟随者的参考航迹,引入迭代学习控制（Iterative Learning Control,ILC）方法,设计跟随者的控制律,使跟随者随着每次迭代调节自身的线速度和角速度,与领航者一起以期望编队队形工作;引入对初始位置的学习,即同时进行编队队形的学习和编队初始位置的学习。解决了任意初始位置的多移动机器人形成并保持期望编队队形的问题。并在理论上分析了控制算法的可行性,仿真结果验证了控制算法的有效性。     

多移动机器人组成任意队形的控制研究

本文将多移动机器人组成队形的整体行为分解为机器人个体分别向对应目标点运动的子行为加以研究．提出了基于两层监控模式的分布式控制框架,监控模块作为统一的指挥协调中枢,确定各机器人合理的对应目标点,避免机器人在目标区域内绕路,使得多移动机器人能够快速流畅地组成任意队形．实验证明了控制方法的有效性．     

A Flexible Control Architecture for Mobile Robots: An Application for a Walking Robot

To get the best features of both deliberative and reactive controllers, present mobile robot control architectures are designed to accommodate both types of controller. However, these architectures are still very rigidly structured thus deliberative modules are always assigned to the same role as a high-level planner or sequencer while low-level reactive modules are still the ones directly interacting with the robot environment. Furthermore, within these architectures communication and interface between modules are if not strongly established, they are very complex thus making them unsuitable for simple robotic systems. Our idea in this paper is to present a control architecture that is flexible in the sense that it can easily integrate both reactive and deliberative modules but not necessarily restricting the role of each type of controller. Communication between modules is through simple arbitration schemes while interface is by connecting a common communication line between modules and simple read and/or write access of data objects. On top of these features, the proposed control architecture is scalable and exhibits graceful degradation when some of the modules fail, similar to the present mobile robot architectures. Our idea has enabled our four-legged robot to walk autonomously in a structured uneven terrain.     

A Multiple Models Approach for Adaptation and Learning in Mobile Robots Control

The paper proposes a multiple models based control methodology for the solution of the tracking problem for mobile robots. The proposed method utilizes multiple models of the robot for its identification in an adaptive and learning control framework. Radial Basis Function Networks (RBFNs) are considered for the multiple models in order to exploit the non-linear approximation capabilities of the nets for modeling the kinematic behaviour of the vehicle and for reducing unmodelled tracking errors contributions. The training of the nets and the control performance analysis have been done in a real experimental setup. The experimental results are satisfactory in terms of tracking errors and computational efforts and show the improvement in the tracking performance when the proposed methodology is used for tracking tasks in dynamical uncertain environments.     

一种多移动机器人避障的改进算法

为了使多机器人在有障碍物的环境中可靠地运行,针对多机器人的避障问题,融合沿墙行为的避障模式,构造出一类具有自适应特性l-ψ闭环控制律下的多机器人避障算法,以作为基于行为的控制策略的有益补充。仿真结果表明,该算法可以成功地解决机器人因融合参数不当而形成的避障"死锁"问题,使多机器人在有障碍物的环境下,在障碍物区能够顺利地通过障碍物,在离开障碍物后,快速恢复至稳定。     

A Fuzzy Behavior-Based Control for Mobile Robots Using Adaptive Fusion Units

It is known that a behavior-based control approach is effective for acquiring an intelligent control system of robots. However, further improvements are required for making any behavior-based control system robust against changes in the environments. A module learning method has been applied in the framework of fuzzy behavior-based control to have an adaptive behavioral fusion. In this paper, an adaptive fusion strategy is proposed to adaptively select a cooperative fusion unit or competitive fusion unit, depending on the external sensor information. Some simulations are given to illustrate that the present control systems are flexible against the change of environments or untrained environments, compared to those with a conventional priority-based fusion unit.     

Neural Network Force Control for Industrial Robots

In this paper, we present a hierarchical force control framework consisting of a high level control system based on neural network and the existing motion control system of a manipulator in the low level. Inputs of the neural network are the contact force error and estimated stiffness of the contacted environment. The output of the neural network is the position command for the position controller of industrial robots. A MITSUBISHI MELFA RV-M1 industrial robot equipped with a BL Force/Torque sensor is utilized for implementing the hierarchical neural network force control system. Successful experiments for various contact motions are carried out. Additionally, the proposed neural network force controller together with the master/slave control method are used in dual-industrial robot systems. Successful experiments are carried out for the dual-robot system handling an object.     

不确定环境下多机器人的动态编队控制

提出了一种不确定环境下多机器人的动态编队控制方法．通过队形参数矩阵确立多机器人之间的相对位置关系,将全局队形控制问题转化为跟随机器人离轴点对虚机器人（与领航机器人运动方向一致,且对领航机器人保持期望的相对距离和观测方位角）离轴点的跟踪．基于建立的跟随机器人和领航机器人之间的误差跟踪系统模型设计相应控制律实现队形保持,并提出了防止机器人与障碍物及其它机器人碰撞的避障策略．仿真结果表明了所提方法的可行性和有效性．