基于模糊PID的球平衡机器人控制器设计与仿真

为了提高单球机器人的姿态控制和运动控制的能力,本文在构建单球机器人机械结构的基础上,设计了一种基于模糊比例-积分-微分(PID)控制算法的线性二次型最优控制方法的控制器,应用于机器人自平衡姿态解算和运动轨迹跟踪控制.为了提高单球机器人姿态控制的实时性、准确性和可靠性,将复杂的运动学模型简化成二维倒立摆运动学模型,并利用...     

基于微分动态逻辑的铁路道口控制分析

利用微分动态逻辑对铁路道口控制进行形式化分析与建模.在火车从发送接近信号到进入道口的运动过程中,根据火车到达道口时间上的要求,将火车速度控制问题抽象成一个混成系统的安全性性质,用微分动态逻辑来描述,并使用混成系统证明工具KeYmaera对系统的安全性进行验证,以实现对火车进入道口前速度的正确控制.     

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.     

约束条件下的巡线机器人逆运动学求解

高压输电线路巡检机器人是一种多关节悬挂运动机构,要实现其运动控制就需要根据机器人的本身机构特点和悬挂系统的运动约束条件进行运动学分析.本文利用微分扭转法对巡线机器人的正向运动学进行了求解,并通过对机器人悬挂系统的力学分析,得到了机器人运动学的约束条件,并在这种约束条件下,采用了一种可用来进行实时控制的迭代循环坐标下降(CCD)算法,来进行机器人的逆运动学求解.这种迭代算法对于有运动约束系统的逆运动学求解具有较强的适用性,而且它具有较好的收敛性和有效性,适合于在线计算,便于巡线机器人的实时运动控制.     

爬行机器人的分层可控混成Petri网建模研究

飞机表面爬行机器人是一个复杂的离散事件和连续动态事件相结合的混成运动系统,为描述系统的静态特性和动态行为,提出一种分层可控混成Petri网模型.依据爬行运动机构定义了Petri网模型组成元素,建立了爬行机器人单轴和双轴分层可控混成Petri网,该模型可接收上层运动轨迹指令并输出爬行动作序列.实验表明,基于该模型实施的飞...     

多机器人系统中的机器人运动控制

针对多机器人系统中机器人运动控制的要求和特点,提出了基于Server+IPC+PLC架构的移动机器人运动控制系统方案,解决了系统互连中存在的一些问题。建立移动机器人的运动学模型,设计基于分解运动速度控制的机器人运动轨迹跟踪算法,并通过仿真研究验证算法的有效性。     

无人机集群的微分平坦性与编队控制研究

本文研究了无人机集群的微分平坦性,给出了相对运动的微分平坦映射,并以此为基础设计了分布式编队控制器.运动规划方面,通过求解受约束的优化问题,实时生成期望编队轨迹和编队构型.运动控制方面,采用微分平坦映射将运动指令映射为每架无人机的期望状态和控制输入,而后利用局部误差反馈设计分布式编队控制器跟踪期望运动轨迹.针对群体运动的稳定性问题,本文运用李雅普诺夫稳定性理论证明了闭环系统的稳定性,给出了控制参数的选取条件.最后仿真验证了编队控制方法在未知环境下的运动控制效果.     

欠驱动船舶的光滑时变指数镇定

船舶水平面运动的欠驱动特性使船舶运动控制的研究具有了新的内涵．本文在船舶水平面运动的运动学及动力学模型基础上,将其演化为二阶系统方程组．针对其欠驱动的特性,借助微分同胚变换及控制输入变换将其转化为两个子系统,分别设计状态反馈控制律,从而得到了原系统的具有指数收敛速率的时变光滑反馈镇定律,实现闭环系统所有状态全局指数收敛至平衡点．该方法可用于欠驱动船舶动力定位或自动泊位控制．最后的仿真试验验证表明本方法是有效的．     

三轮全向移动机器人的研究与设计

为了提高机器人运动控制的精度和机器人智能化的程度,基于传统机器人和移动机器人的优缺点的比较,通过对三轮全向机器人的硬件结构和运动学分析,得出了三轮全向移动机器人的运动控制模型,并结合移动机器人分布式结构的特点设计了一种三轮全向机器人的软件模型.通过对三轮全向移动机器人进行实际运动控制测试,校正了三轮移动机器人的运动学方程,有效的验证了软件模型的正确合理性,提高了移动机器人的运动效果.     

风洞试验绳牵引并联机器人高精度控制仿真

针对一种应用于风洞试验的六自由度绳牵引并联机器人系统,研究其支撑的飞行器模型高精度运动控制问题.首先从系统运动学角度出发,针对绳与飞行器模型之间可能出现的干涉问题,基于绳系时变结构的概念,建立了一个单自由度时变运动方程.构建了驱动系统与飞行器的动力学模型,设计了基于飞行器位姿直接反馈的比例微分控制器,并进行稳定性分析.最后,分别以单自由度和多自由度运动为例,进行绳系结构非时变系统与时变系统的数值仿真.分析结果表明,本文所建立的数学模型是正确可行的,采用一种单自由度时变结构可以有效避免俯仰运动干涉.通过合理设置预紧力,可以避免运动过程中牵引绳松弛.采用PD控制率可以实现飞行器模型的高精度运动控制,从而满足风洞试验的实际应用需求.     

基于偏微分方程约束的机器人群集运动控制系统设计

为充分发挥机器人群集的协作优势,克服单机器人能力不足问题,利用偏微分方程约束理论,设计机器人群集运动控制系统;扩大机器人群集间通信网络范围,改装机器人传感器、运动控制器和驱动电机设备;在硬件设备的支持下,考虑机械结构、运动与动力工作原理,建立机器人群集数学模型;分配机器人群集运动任务,利用偏微分方程规划机器人群集编队运动路径,设置规划路径作为机器人群集运动的约束条件;从位置和姿态角两个方面计算运动控制量,通过控制指令的生成与转换,实现系统的机器人群集运动控制功能;通过系统测试实验得出结论：与传统运动控制系统相比,在优化设计系统的控制下,机器人群集的运动跟踪控制误差为13 cm,机器人群集运动过程中产生的碰撞次数平均值为15次,得到明显减少,即优化设计系统具有良好的控制效果。     

Extending adaptive fuzzy behavior hierarchies to multiple levels of composite behaviors

We propose an extended version of adaptive fuzzy behavior hierarchies, termed Multiple Composite Levels (MCL), that allows for the proper modulation of composite behaviors over multiple levels of a behavior hierarchy, and demonstrate its effectiveness for a hybrid learning/reactive control system. Controllers using adaptive fuzzy behavior hierarchies have previously been shown to provide effective control for robots tasked with multiple concurrent tasks. However, when more complex hierarchies are used to provide control for tasks of increasing complexity, low-level reactive behaviors may not be properly weighted, resulting in sub-optimal control. Through experimental evaluation in which composite behaviors that coordinate lower behaviors are learned using reinforcement learning, we demonstrate that MCL provides effective control in a complex multi-agent task, whereas the original implementation of adaptive fuzzy behavior hierarchies does not.     

一种基于效益的多机器人避碰协调策略

多移动机器人系统在完成同时定位和地图构建SLAM任务时,机器人之间常常存在相互碰撞的问题,而这种碰撞的避免又不同于一般的避障,因为避障问题中的障碍物一般是不动的。为了解决机器人之间的避碰问题,提出了一种基于效益的多机器人避碰协调策略。该策略以提高多机器人系统探索效率为主,确定机器人通过交叉路口的顺序。同时考虑了动态协调避碰的情况,给出了确定机器人通过交叉路口顺序的算法。通过机器人在交叉路口实现避碰协调算法的仿真示例,对该方法的避碰协调过程进行了说明,并对仿真结果进行了分析,同时对仿真中机器人和目标位置的空间关系给出了合理的假设。     

A formal mathematical framework for modeling probabilistic hybrid systems

The development of autonomous agents, such as mobile robots and software agents, has generated considerable research in recent years. Robotic systems, which are usually built from a mixture of continuous (analog) and discrete (digital) components, are often referred to as hybrid dynamical systems. Traditional approaches to real-time hybrid systems usually define behaviors purely in terms of determinism or sometimes non-determinism. However, this is insufficient as real-time dynamical systems very often exhibit uncertain behavior. To address this issue, we develop a semantic model, Probabilistic Constraint Nets (PCN), for probabilistic hybrid systems. PCN captures the most general structure of dynamic systems, allowing systems with discrete and continuous time/variables, synchronous as well as asynchronous event structures and uncertain dynamics to be modeled in a unitary framework. Based on a formal mathematical paradigm exploiting abstract algebra, topology and measure theory, PCN provides a rigorous formal programming semantics for the design of hybrid real-time embedded systems exhibiting uncertainty.      

Hybrid autonomous control for multi mobile robots

Reinforcement learning can be an adaptive and flexible control method for autonomous system. It does not need a priori knowledge; behaviors to accomplish given tasks are obtained automatically by repeating trial and error. However, with increasing complexity of the system, the learning costs are increased exponentially. Thus, application to complex systems, like a many redundant d.o.f. robot and multi-agent system, is very difficult. In the previous works in this field, applications were restricted to simple robots and small multi-agent systems, and because of restricted functions of the simple systems that have less redundancy, effectiveness of reinforcement learning is restricted. In our previous works, we had taken these problems into consideration and had proposed new reinforcement learning algorithm, 'Q-learning with dynamic structuring of exploration space based on GA (QDSEGA)'. Effectiveness of QDSEGA for redundant robots has been demonstrated using a 12-legged robot and a 50-link manipulator. However, previous works on QDSEGA were restricted to redundant robots and it was impossible to apply it to multi mobile robots. In this paper, we extend our previous work on QDSEGA by combining a rule-based distributed control and propose a hybrid autonomous control method for multi mobile robots. To demonstrate the effectiveness of the proposed method, simulations of a transportation task by 10 mobile robots are carried out. As a result, effective behaviors have been obtained.     

多机器人路径规划的安全性验证

近些年来,伴随着人工智能领域的浪潮,机器人越来越多的出现在我们的日常生活中,例如足球机器人、无人机、无人车等.如何保证这些自治机器人尤其是多个机器人在移动过程中的安全成了人们一直很关心的问题.混成通信顺序进程（Hybrid Communicating Sequential Process,HCSP）是一个针对混成系统的形式化建模语言,在通信顺序进程（Communicating Sequential Process,CSP）的基础上引入了微分方程以描述混成系统中的连续行为和控制逻辑,可以方便高效地对大型控制系统尤其是在有通信事件发生时的情形进行形式化建模.本文就是用HCSP建模多机器人的路径控制算法,并用定理证明工具HProver进行形式化验证.结果证明了在满足一定初始条件下,机器人团队在整个运行途中不会发生碰撞.     

Vision-based hybrid control scheme for autonomous parking of a mobile robot

This paper presents a novel vision-based hybrid controller for parking of mobile robots. Parking or docking is an essential behavioral unit for autonomous robots. The proposed hybrid controller comprises a discrete event controller to change the direction of travel and a pixel error-driven proportional controller to generate motion commands to achieve the continuous motion. At the velocity control level, the robot is driven using a built-in PID control system. The feedback system uses image plane measurements in pixel units to perform image-based visual servoing (IBVS). The constraints imposed due to the non-holonomic nature of the robot and the limited field of view of the camera are taken into account in designing the IBVS-based controller. The controller continuously compares the current view of the parking station against the reference view until the desired parking condition is achieved. A comprehensive analysis is provided to prove the convergence of the proposed method. Once the parking behavior is invoked, the robot has the ability to start from any arbitrary position to achieve successful parking given that initially the parking station is in the robot's field of view. As the method is purely based on vision the hybrid controller does not require any position information (or localization) of the robot. Using the Pioneer 3AT robot, several experiments are carried out to authenticate the method. The experimental system has the ability to achieve the parking state and align laterally within ±0.5 cm of the target pose.     

Accurate dynamic modeling and control parameters design of an industrial hybrid spray-painting robot

To obtain higher performance for hybrid robots subject to nonlinear dynamics and friction, feedforward compensations have been ubiquitously utilized in the industrial robotic field to attenuate these disturbances. However, due to the complex friction model and the coupling and time-varying dynamic of hybrid robots, there is no effective approach to realize accurate feedforward compensations in industrial control systems. This paper investigated an accurate dynamic modeling and control parameters design method to address these issues all at once. Taking the friction of each joint into account, the accurate dynamic model of the hybrid robot is developed and verified by experiments. With the accurate dynamic model, an exact control parameter design method is proposed based on the mapping relationship between the dynamic model and the feedforward compensations. Additionally, the control system designed by the method proposed in this paper is compared with the one tuned by an experienced engineer. Particularly, the robot's position and motion accuracy are also tested by a third-party inspection agency. The experimental and test results show that the position and velocity accuracy of the robot is improved significantly when the control system is designed by using the method proposed in this paper, which proves the effectiveness of the proposed method.     

基于DH标定的机器人正向运动学形式化验证

DH坐标系在机器人运动学分析中发挥着重要的作用。在基于DH坐标系构建的机器人控制系统中,机器人结构的复杂性使得构建安全的控制系统成为一个难题,仅仅依靠人工方法可能导致系统漏洞和安全风险,从而危及机器人的安全。形式化方法通过演绎推理与代码抽取实现了对软硬件系统的设计、开发及验证。基于此,本文设计了基于DH标定的机器人正向运动学的形式化验证框架。在Coq中构建了机器人运动理论的形式化证明,并验证控制算法的正确性以确保机器人的运动安全。首先,对DH坐标系进行形式化建模,构建相邻坐标系间转换矩阵的形式化定义,并验证了该转换矩阵与复合螺旋运动的等价性;其次,构建了机械臂正向运动学的形式化定义,并对机械臂运动的可分解性进行形式化验证;再次,本文对工业机器人中常见连杆结构及机器人进行形式化建模,并完成了正向运动学的形式化验证;最后,本文实现了Coq到OCaml的代码抽取,并对抽取的代码进行分析与验证。