一种新的移动机器人组的多目标控制仿真实验

为了实现具有编队运动和避障能力的分布式自治系统,针对协同的轮式移动机器人组提出了一种非线性控制方法,使得其在动态变化环境中能编队运动,避开静态和动态障碍物,并有效地跟踪虚拟领航机器人。首先,建立了多目标控制问题模型,然后为环境中的每一个物体生成人工势场,在此基础上设计生成综合控制量。此方法系统地综合考虑了协同运动、虚拟领航者跟踪、避障和振荡抑制等多目标的实现。采用李亚普洛夫方法对所需的基本条件和主要特性进行了严格地证明和推导。所提出的控制基于势场法,提高了机器人组的瞬态性能,并解决了其固有的振荡问题。仿真研究表明,该方法保证了移动机器人组在动态环境中能有效地实现协同运动、虚拟领航者跟踪、避障和振荡抑制等多目标任务。     

A General Framework for Planning Landmark-Based Motions for Mobile Robots

In this paper, we present a novel generic approach for planning landmark-based motion. The method consists in selecting automatically the most relevant landmarks along a preplanned geometric path. It proposes a strategy to correct the trajectory and to smoothly switch among the landmarks of the environment. Experimental results highlight the relevance of the proposed formalism.     

Feedback Control of an Omnidirectional Autonomous Platform for Mobile Service Robots

This paper proposes a feedback control scheme for an omnidirectional holonomic autonomous platform, which is equipped with three lateral orthogonal-wheel assemblies. Firstly, the dynamic properties of the platform are studied, and a dynamic model suitable for the application of control is derived. The control scheme constructed is of the resolved-acceleration type, with PI and PD feedback. The control scheme was experimentally applied to an actual mobile robotic platform. The results obtained show that full omnidirectionality can be achieved with decoupled rotational and translational motions. Omnidirectionality is one of the principal requirements for mobile robots designed for health-care and other general-hospital services.     

A Novel Robust Leader-Following Control Design for Mobile Robots

To extend the utility of multiple mobile robots (MMRs) in formation on a larger scale, the algorithms developed for control of such robots must be robust. The majority of the developed controllers in the literature lack incorporating dynamics of the MMRs or lack robustness in their design. The very few recently developed robust controllers that consider dynamics, either rely on conservative assumptions to obtain robustness, or are sliding-mode based which suffer from the chattering problem. In this paper, (1) we consider both kinematics and dynamics as well as the actuator dynamics and their uncertainties in formulating the formation of non-holonomic MMRs, (2) we develop a novel robust control technique that can effectively handle the unknown parameters and uncertainties in the system, (3) unlike other papers, we relax the conservative assumptions to arrive at control design, and present rigorous mathematical analyses for the development of robust control and prove the system stability based on the Lyapunov theorem. Simulation results proved the effectiveness of the developed robust control method. It was concluded that the proposed control method, while not being conservative, is easy to use and can be readily adopted in real-time experiments.     

Multisensor-Based Human Detection and Tracking for Mobile Service Robots

One of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In this paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based leg detection using the onboard laser range finder (LRF). The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to also be very discriminative in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera, and the information is fused to the legs' position using a sequential implementation of unscented Kalman filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments.     

移动机器人行为协同开发框架与应用研究

移动机器人在生产生活当中日益发挥重要作用，但移动机器人开发应用却比较困难，主要存在两个问题：一是软件模块化和重用性，二是行为协同控制模型的理论支撑。针对上述问题，融合了已在学术界和工业界取得广泛应用的开源机器人软件平台--机器人操作系统（Robot Operaing System，ROS），以及多主体理论中，支撑主体内部行为协同机制的反应式包容模型（Subsumption Architecture），设计并实现了一个移动机器人行为协同开发框架。基于机器人操作系统底层机制，把包容模型的基本控制元件封装成API，在功能单元内部提供基于有限状态机的开发模板，在功能单元之间提供松散耦合的通信机制，并且提供了基于框架的应用开发的模型设计方法学和应用实现方法学。用户使用本文框架开发移动机器人，因为框架把包容模型中的关键控制元件封装成API，使得用户不用了解复杂的控制模型理论以及底层模块执行机制和通信机制，并且可以重用机器人操作系统提供的开源软件包，几乎没有额外的学习代价，十分地方便高效。通过框架研究并且开发了具有三个行为层的自主漫游机器人以及具有六个行为层的安保巡逻机器人，在仿真器和实际场景中设置不同地图环境，进行机器人行为实验，通过实验结果分析，进一步验证了框架的易用性、可靠性和鲁棒性。     

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.     

一种混合结构的移动机器人导航控制策略

研究了未知环境下移动机器人实时的导航控制问题.采用分布式系统将反射式行为、反应式行为与慎思规划相结合,设计了移动机器人导航控制策略.根据激光雷达传感器信息设计了基于栅格的实时避障算法和解锁策略.通过慎思规划解决了复杂环境下的局部势能陷阱问题.通过自行研制的移动机器人IMR01的实验验证了导航策略的有效性.     

移动小车的轨迹跟踪控制

对由运动学模型描述的二自由度移动小车的跟踪问题进行研究。利用终端滑动模态技术设计控制律,使得移动小车能在有限时间内完全跟踪转动速度不为零的期望轨迹。仿真结果表明了该方法的有效性。     

轮式移动机器人预见预测运动控制

针对移动机器人的运动控制问题,该文采用预见预测控制方法加以解决。利用三阶Bezier曲线作为路径生成器生成目标轨迹,并据此设计了最优预见控制器作为系统的前馈补偿;使用扩展卡尔曼滤波器作为预测模型,基于广义预测控制(GPC)实现了PPC运动控制器的设计。仿真实验结果证明了该方法的有效性。     

多移动机器人避障编队控制

研究多移动机器人避障优化设计,针对多移动机器人在障碍物环境下的编队控制问题,为了保持整体合理避障和控制系统的稳定性和安全性,提出一种多机器人避障编队控制策略.首先获得多移动机器人编队的队形结构模型,结合多机器人完成避障编队任务的问题描述;在此基础上引入导航函数采用一种避障编队控制算法,使移动机器人能以设定的队形运动到目标点,可保证编队运动过程中未与障碍物发生碰撞.进行仿真的结果证明,所提算法解决了多机器人编队与避障问题,并保证了闭环系统的稳定性与安全性,验证了设计方法的有效性.     

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.     

多移动机器人避障编队控制

研究了非完整移动机器人群的避障编队问题. 在次优化控制基础上, 通过对每个交互机器人求解指标函数存在耦合的优化问题提出了两种算法. 在终端惩罚项中加入了势场函数并且构造出相应的终端约束集. 关于系统稳定性及安全性进行了讨论. 仿真实例说明了所提算法的可行性.     

非时间参考的移动机器人路径跟踪控制

基于非时间参考的思想提出了一种移动机器人路径跟踪控制方法.首先选择移动机器人实际路径在某参考系下的x轴投影作为非时间参考量,并针对一类几何路径的跟踪建立移动机器人非时间参考的运动学模 型,据此设计以恒定速度跟踪期望路径的控制律,然后在此基础上给出跟踪任意几何路径的分段控制策略.此跟踪 控制系统所采用的参考量为非时间量􀁯,摆脱了时间因素的影响,因此能提高移动机器人在不确定环境中的跟踪能 力.仿真和物理实验表明了控制方法的有效性.􀁱     

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.     

不确定非完整移动机器人的轨迹跟踪控制

具有未校准视觉参数的非完整移动机器人的运动学系统具有参数不确定性,较一般的运动学系统更加复杂.基于视觉反馈、Barbalat's定理和Lyapunov直接方法,研究了具有未标定摄像机参数的非完整移动机器人的轨迹跟踪问题.首先,利用固定在天花板上的针孔摄像机透视投影模型,提出了一种新的基于视觉伺服的移动机器人运动学跟踪误差模型;基于这个模型,提出了一种新的与未知视觉参数无关的动态反馈跟踪控制器.该控制器不仅保证系统的状态渐近跟踪给定参考轨迹,而且控制器是全局的,通过Lyapunov方法严格证明了闭环系统的稳定性.在惯性系和图像坐标系下讨论跟踪问题,使问题变的简单且设计的控制器更加有用.最后,仿真结果证实了所提出的控制器的有效性.     

Adaptive Robust Control of an Omnidirectional Mobile Platform for Autonomous Service Robots in Polar Coordinates

This paper presents an adaptive robust control method for trajectory tracking and path following of an omni-directional wheeled mobile platform with actuators’ uncertainties. The polar-space kinematic model of the platform with three independent driving omnidirectional wheels equally spaced at 120 from one another is briefly introduced, and the dynamic models of the three uncertain servomotors mounted on the driving wheels are also described. With the platform’s kinematic model and the motors’ dynamic model associated two unknown parameters, the adaptive robust controller is synthesized via the integral backstepping approach. Computer simulations and experimental results are conducted to show the effectiveness and merits of the proposed control method in comparison with a conventional PI feedback control method.     

多移动机器人的队形控制

针对步行机器人建模困难和传感信息有限的条件下对其进行的队形控制研究。通过建立机器人的队形位置信息知识库,制定有优先级的组队参考机器人选择规则,结合有限的传感交互信息,提出了基于主从知识联想的平行四边形法来确定机器人的运动向量,使跟随机器人在虚构的平行四边形中分析出其下一步的偏转角和速度,并在偏转角和速度的分析中考虑了时延和适当的控制周期。在机器人队形控制过程中的避障问题则采用模糊控制理论,依据人为经验制定的模糊避障控制规则使机器人灵活的避开障碍,仿真实验证明了算法的有效性。     

移动机器人视觉控制系统

机器人由图像采集系统,模式识别系统,控制系统,传感器和各种执行单元构成。我们采用工控机,配合图形采集卡,进行图像的采集和处理。控制系统主要由单片机构成。识别系统将处理结果通过编码发送到控制系统,由控制系统融合触觉,接近觉等其它传感器信息,控制各种功能执行单元。     

基于UKF的移动机器人主动建模及模型自适应控制方法

利用基于无色卡尔曼滤波（Unscented Kalman Filter, UKF）的状态和参数联合估计方法对移动机器人进行在线主动建模,基于该主动模型的逆动力学控制方法,实现了移动机器人对其自身不确定因素的自主性. 在针对全方位移动机器人的仿真实验中,验证了UKF对时变的状态和参数的收敛性和跟踪能力,并给出了不确定界. 基于主动建模的逆动力学控制方法与常值PID控制方法的比较结果,验证了该方法的有效性.