分布式多机器人运动控制的离散事件系统方法

传统多机器人系统的运动控制主要依赖于机器人的动力学方程或运动学方程,通过求解微分方程组来获得机器人的输入控制信号.随着系统中机器人数量的增加和运行环境的复杂化,动力学方程很难描述多机器人系统的运动行为,且无法很好地解决诸如死锁等逻辑故障.本文简略综述了国内外的研究现状,重点介绍笔者所在研究组开展的关于离散事件系统方法在多机器人运动控制方面的应用性研究工作.其动机在于:1)基于离散事件系统方法的运动控制能够有效地解决系统运行过程中产生的诸如死锁等逻辑故障.首先,利用离散事件系统模型对多机器人系统的运动进行建模,从而降低计算复杂性;其次,基于所得离散事件系统模型,设计分布式安全运动控制算法,使各个机器人可以自主地、无碰撞地、无死锁地运动;设计分布式鲁棒运动控制算法,使得失效的机器人对系统的影响最小.2)基于离散事件系统方法的运动控制策略可以结合传统的基于运动学方程的运动控制方法,从而使系统不但能够避免顶层的逻辑故障,而且能够确定机器人执行器的输入信号.     

模糊认知图在智能控制中的应用研究*

介绍了智能控制和模糊认知图(FCM)的原理,首次提出基于FCM的智能控制框架解决多输入多输出变量系统的控制问题;同时利用产生式系统建模实现FCM建模阶段专家知识合成,并在实际控制问题中检验其效果。实验结果表明,FCM模型能准确表示控制系统中输入输出变量间的因果关系,基于FCM的控制框架是解决多输入多输出控制问题的一种新方法。     

一种履带式行走机器人的仿人智能控制系统

本文介绍一种参加首届全国大学生机器人电视大赛的履带式行走机器人仿人智能控制系统。首先通过对机器人跟踪直线数学模型的建立，得出该系统是一个多输入多输出非线性系统，且变量间相互耦合，用传统控制方法难以解决。根据仿人智能控制的基本思想，建立了单片机控制硬件平台，提出了机器人跟踪直线、转弯、准确定位和路线规划的仿人智能控制算法。在此基础上编制了控制软件，通过调试，整定了控制参数，经过试验和参赛的考验，证明该系统简单、实用、可靠。     

基于微控制器的现场智能控制系统设计

随着计算机技术、通信技术、控制技术(3C技术)的发展,控制领域正在发生深刻的技术变革,控制系统将向信号数字化、控制智能化、通信网络化的开放性系统方向发展.本文完成了一种基于微控制器的新型现场智能控制系统设计,其功能模块包括了数字量输入(DI)、数字量输出(DO)、模拟量输入(AI)、模拟量输出(AO)、毫伏级模拟量输入、协议转换桥等模块.在论文中,主要以数字量输入模块和模拟量输入模块为例子,介绍了功能模块的设计过程.     

不对称欠驱动水面机器人事件触发全局渐近镇定控制

本文研究了不对称欠驱动水面机器人事件触发全局渐近镇定控制问题.首先,引入坐标变换将系统全局渐近镇定控制问题转化为变换后模型欠驱动子系统的全局渐近镇定控制问题,利用周期时间函数构造时变辅助变量提出了一种时变连续的镇定控制律,并结合切换门限事件触发机制设计实际的事件触发推力与力矩控制输入使闭环系统全局渐近稳定.所提出的方法...     

基于规则的仿人智能控制方法及其应用

在总结前人研究成果的基础上,概括出基于规则仿人智能控制的基本概念、基本思想和主要特点.以仿人智能控制的原型算法为例,分析其静态特性、动态特性和稳定性,并给出基于规则的仿人智能控制器的基本设计步骤.以工作在未知环境下的机器人系统为例,研究了仿人智能控制方法在机器人接触力控制中的应用.它是在数字PID控制算法的基础上,通过仿人智能控制的特征变量的取值符号,选择相应的控制策略实现的.实验结果表明,仿人智能控制方法可以大大提高控制精度.     

基于Agent的离散事件仿真系统的控制功能框架研究

在研究基于Agent的离散事件仿真技术的基础上,提出了一个基于Agent的离散事件仿真系统的控制功能框架(ADESCF)。该框架包括了输入接口控制、输出接口控制、模型结构控制、仿真时钟控制、实验数据控制、仿真实验控制、仿真逻辑控制七个功能元素。分析了各种控制功能的工作原理与机制,并从输入输出连接关系角度分析了多Agent仿真系统层次化与分布式建模的本质,为该类仿真系统的开发提供了可参考的依据。     

单足气动跳跃机器人的基于时间事件控制方法

传统的单足跳跃机器人高度控制多采用基于状态的控制方法,这种方法由于使用位置传感器,机械系统和控制系统都较为复杂.本文提出了一种只使用触地开关的基于时间事件的控制方法,简化了系统的设计.建立了包含气动特性的动力学模型,通过仿真分析了该控制方法的特点,在机器人样机上验证了算法的有效性.仿真和实验结果表明基于时间事件控制的机器人在保证系统具有相同稳定范围、收敛速度的同时,比基于状态事件控制的机器人更简单、跳跃高度调节更灵活.     

基于产生式系统的模糊认知图建模方法及其在控制中的应用

介绍了模糊认知图的基本原理,首次在FCM（fuzzy cognitive map）建模阶段引入产生式系统提出基于产生式系统的FCM建模方法,然后结合智能控制理论提出了基于FCM的控制框架以解决多输入变量系统的控制问题,并以混液池密度控制问题为例对其进行了检验。研究结果表明：FCM与产生式系统的结合弥补了传统FCM建模方法在专家知识整合上的缺陷、基于FCM的控制框架是解决多输入多输出控制问题的一条新途径。     

机器人作业的离散事件建模与控制方法

针对传统机器人控制中规划器开环的不足, 首先设计了底层的非时间参考的连续路径规划与控制方法, 使规划器的规划值基于系统的状态之上, 避免了基于时间规划的系统由于意外事件造成的损害和任务的重新规划; 在此基础上, 又建立了整个作业任务的离散事件模型, 设计了离散事件路径规划方法及控制综合方法, 实现了事件反馈的闭环, 提高了系统处理不确定事件的能力和作业的自动化程度. 最后两台PUMA560机械手搬运被识别工件的作业验证了本文算法的有效性.     

Path-based approach to integrated planning and control for robotic systems: A path-based approach provides an analytical integration of robot planning and control. As a result, the task level control can be achieved, and the system is capable of coping with unexpected or uncertain events

Our newly developed event-based planning and control theory is applied to robotic systems. It Introduces a suitable action or motion reference variable other than time, but directly related to the desired and measurable systems output, called event. Here the event is the length of the path tracked by a robot. It enables the construction of an integrated planning and control system where planning becomes a real-time closed-loop process. The path-based integration planning and control scheme is exemplified by a single-arm tracking problem. Time and energy optimal motion plans combined with nonlinear feedback control are derived in closed form. To the best of our knowledge, this closed-form solution was not obtained before. The equivalence of path-based and time-based representations of nonlinear feedback control is shown, and an overall system stability criterion has also been obtained. The application of event-based integrated planning and control provides the robotic systems the capability to cope with unexpected and uncertain events in real time, without the need for replanning. The theoretical results are illustrated and verified by experiments.     

一个面向复杂任务的多机器人分布式协调系统

基于多智能体系统理论, 研究在非结构、不确定环境下面向复杂任务的多机器人分布式协调系统的实现原理、方法和技术. 提出的递阶混合式协调结构、基于网络的通讯模式和基于有限状态机的规划与控制集成方法, 充分考虑了复杂任务和真实自然环境的特点. 通过构建一个全实物的多移动机器人实验平台, 对规划、控制、传感、通讯、协调与合作的各关键技术进行了开发和集成, 使多机器人分布式协调技术的研究直接面向实际应用, 编队和物料搬运的演示实验结果展示了多机器人协调技术的广阔应用前景.     

Data-based dynamical modeling of externally observed actuators-only robots

This paper describes the derivation of dynamical models for mobile robots using system-identification methods and shows how such models can be used in the control of autonomous devices. Unlike traditional approaches that are based on the system physics alone, in this paper, an autoregressive model structure is derived from the physics of the process, and its parameters are estimated using real input and output data. From the resulting model, efficient control can be obtained for centralized and remotely controlled robots (typically externally observed) with little or no local "intelligence," thus overcoming many of the severe shortcomings presented by such systems. System dead time is one example of a serious problem that can be efficaciously minimized by using the model as an online predictor. Several experimental results using small differential-driven robots are included to demonstrate the applicability and robustness of the suggested procedure in actual real-world scenarios.     

Robot Teams for Intervention in Confined and Structured Environments

Safety, security, and rescue robotics can be extremely useful in emergency scenarios such as mining accidents or tunnel collapses where robot teams can be used to carry out cooperative exploration, intervention, or logistic missions. Deploying a multirobot team in such confined environments poses multiple challenges that involve task planning, motion planning, localization and mapping, safe navigation, coordination, and communications among all the robots. To complete their mission, robots have to be able to move in the environment with full autonomy while at the same time maintaining communication among themselves and with their human operators to accomplish team collaboration. Guaranteeing connectivity enables robots to explicitly exchange information needed in the execution of collaborative tasks and allows operators to monitor and teleoperate the robots and receive information about the environment. In this work, we present a system that integrates several research aspects to achieve a real exploration exercise in a tunnel using a robot team. These aspects are as follows: deployment planning, semantic feature recognition, multirobot navigation, localization, map building, and real‐time communications. Two experimental scenarios have been used for the assessment of the system. The first is the Spanish Santa Marta mine, a large mazelike environment selected for its complexity for all the tasks involved. The second is the Spanish‐French Somport tunnel, an old railway between Spain and France through the Central Pyrenees, used to carry out the real‐world experiments. The latter is a simpler scenario, but it serves to highlight the real communication issues.     

Novel MPC‐Based Fault Tolerant Tracking Control Against Sensor Faults

The problem of active fault‐tolerant tracking control with control input and system output constraints is studied for a class of discrete‐time systems subject to sensor faults. A time‐varying fault‐tolerant observer is first developed to estimate the real system state from the faulty sensor output and control input signals. Then by using the estimated state at each time step, a model predictive control (MPC)‐based fault‐tolerant tracking control scheme is presented to guarantee the desired tracking performance and the given input and output constraints on the faulty system. In comparison with many existing fault‐tolerant MPC methods, its main contribution is that the proposed state estimator is designed by the simple and online numerical computation to tolerate the possible sensor faults, so that the regular MPC algorithm without fault information can be adopted for the online calculation of fault‐tolerant control signal. The potential recursive infeasibility and computational complexity due to the faults are avoided in the scheme. Additionally, the closed‐loop stability of the post‐fault system is discussed. Simulative results of an electric throttle control system verify the effectiveness of the proposed method.     

Computing dynamic output feedback laws

The pole placement problem asks to find laws to feed the output of a plant governed by a linear system of differential equations back to the input of the plant so that the resulting closed-loop system has a desired set of eigenvalues. Converting this problem into a question of enumerative geometry, efficient numerical homotopy algorithms to solve this problem for general multiple-input-multiple-output systems have been proposed recently. Despite the wider application range of dynamic feedback laws, the realization of the output of the numerical homotopies as a machine to control the plant in the time domain has not been addressed before. In this note, we present symbolic-numeric algorithms to turn the solution to the question of enumerative geometry into a useful control feedback machine. We report on numerical experiments with our publicly available software PHCpack and illustrate its application on various control problems from the literature.     

机器人鲁棒控制器参数演化设计

论文将讨论具有控制输入幅值限制的机器人轨迹跟踪控制问题。将利用基于信号补偿的鲁棒控制方法设计机器人的子关节系统控制器。该控制器由标称控制器和鲁棒补偿器组成。标称控制器对于一标称受控对象实现所希望的轨迹跟踪特性,鲁棒补偿器则用于减小实际受控对象和标称受控对象之间的差异对跟踪特性的影响。当输入存在饱和约束的情况下,对鲁棒补偿器进行了修改,并且基于演化寻优的方法求取鲁棒补偿器参数。     

Parametric Planning for Multiple Cooperative Robots

A novel planning strategy, parametric planning, is proposed to negotiate the task-oriented object manipulation of multiple coordinated robots. The approach provides an advantage to improve flexibility of robotic cooperation, in which the desired trajectories in Cartesian space derived from task requirements are converted into the trajectories of robots in joint space for a fixed-coordinated multi-robot system. For this purpose, a parametric cooperative index matrix is introduced to handle the relationship of the input desired Cartesian trajectories and the position of robots. A case study of 2-dimension object-motion trajectory tracking using four robots is presented in the end. It proved that the proposed approach effectively delivers trajectory task requirements to the joint trajectories of robots.     

Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence

In this paper, a practically viable approach for conflict free, coordinated motion planning of multiple robots is proposed. The presented approach is a two phase decoupled method that can provide the desired coordination among the participating robots in offline mode. In the first phase, the collision free path with respect to stationary obstacles for each robot is obtained by employing an A* algorithm. In the second phase, the coordination among multiple robots is achieved by resolving conflicts based on a path modification approach. The paths of conflicting robots are modified based on their position in a dynamically computed path modification sequence (PMS). To assess the effectiveness of the developed methodology, the coordination among robots is also achieved by different strategies such as fixed priority sequence allotment for motion of each robot, reduction in the velocities of joints of the robot, and introduction of delay in starting of each robot. The performance is assessed in terms of the length of path traversed by each robot, time taken by the robot to realize the task and computational time. The effectiveness of the proposed approach for multi-robot motion planning is demonstrated with two case studies that considered the tasks with three and four robots. The results obtained from realistic simulation of multi-robot environment demonstrate that the proposed approach assures rapid, concurrent and conflict free coordinated path planning for multiple robots.     

基于OpenHaptics的六自由度串联机器人实时控制

随着串联机器人技术的发展,通过计算机编程实现的多种人机交互的机器人控制方法成为一个新的研究热点.对触觉输入输出设备、六自由度串联机器人的物理结构、通讯方式及编程接口进行研究,使用Geomagic公司的触觉输入输出设备Geomagic Touch为人机交互信息采集设备,利用设备配套的OpenHaptics编程套件,通过UR10机器人及其控制脚本URScript,构建了一个六自由度串联机器人的实时控制系统.