一种引入通信的多移动机器人编队方法*

提出以视觉跟踪为基础并引入通信进行多机器人的编队控制方法,根据需要编写了一种新的通信协议,采用闭环l-Φ实现编队算法.这种多机器人编队控制避免了视觉系统的局限,能够更好地在复杂未知环境中协作完成任务,解决了编队控制的无反馈和实时性不高的问题,使得机器人能够准确迅速地进行跟踪和通信编队,一起顺利达到目标点.试验结果证明了该方法的有效性.     

多移动机器人的领航-跟随编队避障控制

针对多移动机器人的编队控制问题,提出了一种结合Polar Histogram避障法的领航-跟随协调编队控制算法。该算法在领航-跟随l-φ编队控制结构的基础上引入虚拟跟随机器人,将编队控制转化为跟随机器人对虚拟跟随机器人的轨迹跟踪控制。结合移动机器人自身传感器技术,在简单甚至复杂的环境下为机器人提供相应的路径运动策略,实现实时导航的目的。以两轮差动Qbot移动机器人为研究对象,搭建半实物仿真平台,进行仿真实验。仿真结果表明:该方法可以有效地实现多移动机器人协调编队和避障控制。     

The stable and precise motion control for multiple mobile robots

Intelligent path planning of multiple mobile robots has been addressed in this paper. Cooperative behaviour can be achieved using several mobile robots, which require online inter-communication among themselves. In the present investigation rule-based and rule-based-neuro-fuzzy techniques are analyzed for multiple mobile robots navigation in an unknown or partially known environment. The final aims of the robots are to reach some pre-defined goals. Based upon a reference motion, direction; distances between the robots and obstacles; distances between the robots and targets; different types of rules are taken heuristically and refined later to find the steering angle. The control system combines a repelling influence related to the distance between robots and nearby obstacles and with an attracting influence between the robots and targets. Then a hybrid rule-based-neuro-fuzzy technique is analyzed to find the steering angle of the robots. Results show that the proposed rule-based-neuro-fuzzy technique can improve navigation performance in complex and unknown environments compared to this simple rule-based technique.     

一类有序化多移动机器人群集运动控制系统

群集运动控制(flocking control)是一种新型的多移动机器人运动协调控制, 目前的研究多集中于无leader模式下群集运动控制器的设计. 为此, 本文阐述了一类多移动机器人有序化群集运动系统控制方案及其性能评价方法. 首先, 在前人的研究基础上, 本文介绍了基于Agent的有序化编队控制机制; 然后, 运用非完整约束下移动机器人的动力学原理, 设计了由Agent到移动机器人的控制转化方法; 并进一步提出了基于“最小稳定时间”的群集运动分析法, 可对有序化群集运动系统进行分析; 最后, 运用仿真实例, 描述了多移动机器人有序化群集运动的控制及分析过程. 实验结果验证了此控制方案的有效性.     

Decentralized and adaptive control of multiple nonholonomic robots for sensing coverage

This work deals with decentralized control of multiple nonholonomic mobile sensors for optimal coverage of a given area for sensing purposes. We assume a density function over the region to be covered, which can be viewed as a probability density of the phenomena to be sensed. The density function is unknown but assumed to be linearly parameterized with unknown parameter weights. We consider a second‐order dynamic model for the mobile agents and derive decentralized adaptive control laws to achieve optimal coverage of the region. We then consider the case where the dynamic model of the agents are not fully known, and then develop parameter adaptation laws to achieve the optimal coverage objective. We test the derived algorithms using simulations and compare our proposed controllers with kinematics‐based controllers. We find that the feedback control design based on the dynamic model performs significantly better than controllers solely relying on kinematic models. Furthermore, for the unknown dynamics case, our controller outperforms the nonadaptive controller with poor initial parameter estimates.     

Formation control of multiple mecanum-wheeled mobile robots with physical constraints and uncertainties

Aiming at the formation control of multiple Mecanum-wheeled mobile robots (MWMRs) with physical constraints and model uncertainties, a novel robust control scheme that combines model predictive control (MPC) and extended state observer-based adaptive sliding mode control (ESO-ASMC) is proposed in this paper. First, a linear MPC strategy is proposed to address the motion constraints of MWMRs, which can transform the robot formation model based on leader-follower into a constrained quadratic programming (QP) problem. The QP problem can be solved iteratively online by a delay neural network (DNN) to obtain the optimal control velocity of the follower robot. Then, to address the input saturation constraints, model uncertainties and unknown disturbances in the dynamic model, an improved ESO-ASMC is proposed and compared with the robust adaptive terminal sliding mode control (RATSMC) and the conventional sliding mode control (SMC) to prove the effectiveness. The proposed scheme, considering the optimal control velocity obtained by the kinematics controller as the given desired velocity of the dynamics controller, can implement precise formation control, while solving various physical constraints of the robot, and eliminating the effects of model uncertainties and disturbances. Finally, through a comparative simulation case, the effectiveness and robustness of the proposed method are verified.

     

Finite‐time formation control of multiple nonholonomic mobile robots

This paper considers finite‐time formation control problem for a group of nonholonomic mobile robots. The desired formation trajectory is represented by a virtual dynamic leader whose states are available to only a subset of the followers and the followers have only local interaction. First of all, a continuous distributed finite‐time observer is proposed for each follower to estimate the leader's states in a finite time. Then, a continuous distributed cooperative finite‐time tracking control law is designed for each mobile robot. Rigorous proof shows that the group of mobile robots converge to the desired geometric formation pattern in finite time. At the same time, all the robots can track the desired formation trajectory in finite time. Simulation example illustrates the effectiveness of our method. Copyright © 2012 John Wiley & Sons, Ltd.     

基于虚拟结构法的多移动机器人分布式预测控制

研究多移动机器人协同编队控制问题,提出一种基于虚拟结构的分布式预测控制算法.与现有编队控制方法相比,基于虚拟结构的方法可以使移动团队更加稳定地保持期望编队队形运动,通过将变换矩阵与虚拟结构相结合可以改善编队结构的灵活性,从而有效拓展该方法的应用范围.最后将所提出控制算法与现有编队控制方法进行对比仿真,结果验证了其有效性...     

移动机器人的确定学习与控制

利用确定学习, 提出了移动机器人的学习控制策略. 在闭环控制过程中, 该控制器可以学习到未知控制系统的动态, 并将学到的动态作为经验知识以常值网络权值的形式储存. 在下次重复相同的控制任务时, 控制器可以调用以往所学到的动态知识用于控制并获得更好的控制性能. 该策略避免了耗时的神经网络重新训练过程, 使得移动机器人具有真正意义上的从经历中获取知识, 存储知识, 并将学到的知识再利用的智能控制能力.     

Vision-based formation control of mobile robots

1Introduction Formation control of multiple vehicles,such ascooperative control of a group of mobile robots[1~4]and multiple spacecraft[5,6],has beenrecognized as a keytechnologyfor the future and studied by many researchersin recent years.The various control approaches to multiplevehicle formation reported in the literature can becategorized into three groups:leader following schemes;behavior_based methods;and virtual structure techniques.In the leader following approach[1,2,7],one of thevehi…     

Vision-based formation control of mobile robots

In this paper,a formatio n control algorithm and an obstacle avoidance control algorithm for mobile robots are developed based on a relative motion sensory system such as a pan/tilt camera vision system,without the need for global sensing and communication between robots.This is achieved by e mploying the velocity variation,instead of actual ve locities,as the control inputs.Simulation and experi mental results have demonstrated the effectiveness of the proposed control methods.     

Fuzzy behavior-based control of mobile robots

An extensive fuzzy behavior-based architecture is proposed for the control of mobile robots in a multiagent environment. The behavior-based architecture decomposes the complex multirobotic system into smaller modules of roles, behaviors and actions. Fuzzy logic is used to implement individual behaviors, to coordinate the various behaviors, to select roles for each robot and, for robot perception, decision-making, and speed control. The architecture is implemented on a team of three soccer robots performing different roles interchangeably. The robot behaviors and roles are designed to be complementary to each other, so that a coherent team of robots exhibiting good collective behavior is obtained.     

基于单应性矩阵的移动机器人编队跟随控制

本文针对以领航跟随模型为代表的移动机器人编队系统提出了一种基于单应性的编队跟随控制方案,在给定理想队形间隔距离和理想期望图像的前提下,利用单应性矩阵构造可反映理想队形中跟随机器人实时位姿的虚拟机器人,将原先的编队问题转化为对虚拟机器人的轨迹跟踪问题.编队跟随过程中,领航机器人的速度采用估计的方式,利用单应性与速度之间的关系模型以及跟随机器人的实时速度能较为准确的估计领航速度,从而避免采用局部通信的方式,节省了编队实验成本.最后本文进行的半实物仿真以及实物实验均可验证所提出的编队跟随算法包括速度估计方法的实际有效性.     

A decentralized control system for cooperative transportation by multiple non-holonomic mobile robots

In this paper, we propose a decentralized control system for transporting a single object by multiple non-holonomic mobile robots. Each agent used in the proposed system has two arms, which can steer around a joint offset from the centre point between two driving wheels. One of these mobile robots acts as a leader, who is assumed to be able to plan and to manipulate the omnidirectional motion of the object by using a resolved velocity control. Other robots, referred to as followers, cooperatively transport the object by keeping a constant relative position with the object using a simple PI control. Different from conventional leader–follower type systems that transport an object by multiple robots in coordination, the present followers can plan an action based on their local coordinate and need no absolute positional information. In addition, as a special case, a system consisting of only two robots is introduced, in which the follower robot not only has an arm length controller to follow the leader but also has a fuzzy controller as an avoidance controller to avoid obstacles or a posture controller to keep a desired posture of the object. Simulation results are given to demonstrate the good performance of the proposed systems.     

Finite-time tracking control for multiple non-holonomic mobile robots based on visual servoing

This paper investigates finite-time tracking control problem of multiple non-holonomic mobile robots via visual servoing. It is assumed that the pinhole camera is fixed to the ceiling, and camera parameters are unknown. The desired reference trajectory is represented by a virtual leader whose states are available to only a subset of the followers, and the followers have only interaction. First, the camera-objective visual kinematic model is introduced by utilising the pinhole camera model for each mobile robot. Second, a unified tracking error system between camera-objective visual servoing model and desired reference trajectory is introduced. Third, based on the neighbour rule and by using finite-time control method, continuous distributed cooperative finite-time tracking control laws are designed for each mobile robot with unknown camera parameters, where the communication topology among the multiple mobile robots is assumed to be a directed graph. Rigorous proof shows that the group of mobile robots converges to the desired reference trajectory in finite time. Simulation example illustrates the effectiveness of our method.     

Cooperative collision avoidance between multiple mobile robots

This paper presents a new collision avoidance technique, called cooperative collision avoidance, for multiple mobile robots. The detection of the danger of collision between two mobile robots is discussed with respect to the geometric aspects of their paths as are cooperative collision avoidance behaviors. The direction control command and the velocity control command for the cooperative collision avoidance are then proposed. The avoidance technique is extended to cases in which the number of mobile robots is more than two. Furthermore, the conditions for collision avoidance are considered with respect to the navigation parameters and guidelines of designing the navigation parameters are obtained. The effectiveness of the proposed technique is demonstrated by means of numerical simulation and navigation experiments using two real mobile robots named Pioneer‐1. ©2000 John Wiley & Sons, Inc.     

Dynamic adaptive trajectory tracking control of nonholonomic mobile robots using multiple models approach

This paper presents an approach for the trajectory tracking control of nonholonomic wheeled mobile robots (WMR) by combining one of the existing adaptive control methods and multiple identification models. The overall system includes two types of controllers in the control scheme. A kinematic controller developed by using kinematic model produces the required linear and angular velocities of the robot for tracking a reference trajectory. These required velocities are used to calculate the torques using an adaptive dynamic controller with multiple models. The proposed method uses the multiple models of the WMR for the identification of the dynamic parameters and performs switching between the given models. The models used in the identification are identical, except for the initial estimates of the parameters. By using an adaptive dynamic controller with multiple models of the WMR, enhancement in transient response is obtained. Stability analysis of the overall system is given, and simulation results are presented to demonstrate the effective performance of the adaptive control by using multiple models approach.     

Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

This paper proposes a sliding‐mode control (SMC) method to achieve practical cooperative consensus tracking for a network of multiple nonholonomic wheeled mobile robots (MNWMRs) with input disturbances. A novel SMC surface under the nonholonomic constraints is first formulated to characterize the network communication interactions among the networked robots under the framework of polar coordinates. A unified distributed consensus tracking strategy is then proposed by systematically combining a position controller and a direction controller. Furthermore, a simple yet general criterion is derived to achieve the desired practical consensus of trajectory tracking and posture stabilization for MNWMRs. In particular, for a specific common consensus trajectory, the complete asymptotic tracking in heading direction can be fully guaranteed when the perfect asymptotic position‐tracking errors are realized. Accordingly, the developed consensus tracking strategy for MNWMRs demonstrates some advantages of control performance including stability, robustness, and effectiveness over the existing control method proposed for their single‐robot counterparts. Some comparative simulation results are given to confirm the effectiveness of the proposed cooperative consensus control method.