Control of a nonholonomic mobile robot using neural networks

A control structure that makes possible the integration of a kinematic controller and a neural network (NN) computed-torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and stability is guaranteed by Lyapunov theory. This control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. Moreover, the NN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. Online NN weight tuning algorithms do not require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.     

Robust adaptive control for a nonholonomic mobile robot with unknown parameters

A robust adaptive controller for a nonholonomic mobile robot with unknown kinematic and dynamic parameters is proposed. A kinematic controller whose output is the input of the relevant dynamic controller is provided by using the concept of backstepping. An adaptive algorithm is developed in the kinematic controller to approximate the unknown kinematic parameters, and a simple single-layer neural network is used to express the highly nonlinear robot dynamics in terms of the known and unknown parameters. In order to attenuate the effects of the uncertainties and disturbances on tracking performance, a sliding mode control term is added to the dynamic controller. In the deterministic design of feedback controllers for the uncertain dynamic systems, upper bounds on the norm of the uncertainties are an important clue to guarantee the stability of the closed-loop system. However, sometimes these upper bounds may not be easily obtained because of the complexity of the structure of the uncertainties. Thereby, simple adaptation laws are proposed to approximate upper bounds on the norm of the uncertainties to address this problem. The stability of the proposed control system is shown through the Lyapunov method. Lastly, a design example for a mobile robot with two actuated wheels is provided and the feasibility of the controller is demonstrated by numerical simulations.     

Saturated stabilization and tracking of a nonholonomic mobile robot

This paper presents a framework to deal with the problem of global stabilization and global tracking control for the kinematic model of a wheeled mobile robot in the presence of input saturations. A model-based control design strategy is developed via a simple application of passivity and normalization. Saturated, Lipschitz continuous, time-varying feedback laws are obtained and illustrated in a number of compelling simulations.     

非完整移动机器人鲁棒运动规划

非完整移动机器人利用传感器可以解决不确定性模型和未知环境中的许多问题. 利用移动机器人上配备的传感器的信息组合提出了一种在线视点寻求算法, 结合移动机器人的运动方程和传感器的量测方程采用扩展Kalman估计来对移动机器人的位置进行修正, 以降低运动的不确定性, 从而得到一种鲁棒的规划算法, 仿真的结果证明了上述方法是行之有效的.     

Adaptive output feedback tracking control of a nonholonomic mobile robot

An adaptive output feedback tracking controller for nonholonomic mobile robots is proposed to guarantee that the tracking errors are confined to an arbitrarily small ball. The major difficulties are caused by simultaneous existence of nonholonomic constraints, unknown system parameters and a quadratic term of unmeasurable states in the mobile robot dynamic system as well as their couplings. To overcome these difficulties, we propose a new adaptive control scheme including designing a new adaptive state feedback controller and two high-gain observers to estimate the unknown linear and angular velocities respectively. It is shown that the closed loop adaptive system is stable and the tracking errors are guaranteed to be within the pre-specified bounds which can be arbitrarily small. Simulation results also verify the effectiveness of the proposed scheme.     

Control of a nonholonomic mobile robot using an RBF network

This article deals with handling unknown factors, such as external disturbance and unknown dynamics, for mobile robot control. We propose a radial-basis function (RBF) network-based controller to compensate for these. The stability of the proposed controller is proven using the Lyapunov function. To show the effectiveness of the proposed controller, several simulation results are presented. Through the simulations, we show that the proposed controller can overcome the modelling uncertainty and the disturbances. The proposed RBF controller also outperforms previous work from the viewpoint of computation time, which is a crucial fact for real-time applications.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

基于传感器的非完整移动机器人运动规划

针对非完整移动机器人在未知室内环境中提出了一种路径规划方法, 通过利用传感器对周围环境的探测和实时处理传感器数据, 以及所设计的目标寻找函数, 可以有效地完成其运动规划. 该方法能够确保移动机器人在无障碍物区或障碍物对机器人不构成危险时加速前进, 在障碍物区能够慢速绕过, 从而使得移动机器人快速且安全地到达目标位置, 仿真的结果证明了该方法的有效性.     

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.     

非完整移动机器人群体的优化蜂拥控制

考虑到非完整移动机器人群体蜂拥运动过程中保持位置拓扑全局连通的性能,提出一类基于局部信息交互的优化蜂拥控制算法.利用趋向局部最小外接圆圆心位置的控制方式维持群体位置拓扑在运动过程中的全局连通性,保证群体位置的聚合;结合角度控制和贝塞尔曲线规划个体的运动路径,在路径长度阈值的限定下,通过粒子群算法求取个体的优化目标方向角,保障群体运动方向的匹配;最后给出了可行的避碰方案.     

自主移动机器人足球比赛视觉定位方法综述

综述了RoboCup足球赛中全自主移动机器人基于视觉的定位技术,包括机器人自定位和多机器人协作物体定位.介绍了定位技术的发展情况与分类.从机器人环境构建形式的不同以及先验位姿和概率方法的应用与否等方面,系统地分析和比较了各种自定位方法.对于多机器人协作物体定位,阐述了静态方法和动态跟踪方法.总结了定位过程中需要重点研究的传感器模型构建、图像处理、特征匹配以及协作过程涉及的相关问题.最后就视觉定位存在的问题和技术发展趋势进行了讨论.     

Position estimation of a mobile robot using images of a moving target in intelligent space with distributed sensors

Latest advances in hardware technology and state-of-the-art of mobile robots and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. A mobile service robot requires the perception of its present position to co-exist with humans and support humans effectively in populated environments. To realize this, a robot needs to keep track of relevant changes in the environment. This paper proposes localization of a mobile robot using images recognized by distributed intelligent networked devices in intelligent space (ISpace) in order to achieve these goals. This scheme combines data from the observed position, using dead-reckoning sensors, and the estimated position, using images of moving objects, such as a walking human captured by a camera system, to determine the location of a mobile robot. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the ISpace. Using the a priori known path of a moving object and a perspective camera model, the geometric constraint equations that represent the relation between image frame coordinates for a moving object and the estimated robot's position are derived. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot, and the Kalman filtering scheme is used for the estimation of the mobile robot location. The proposed approach is applied for a mobile robot in ISpace to show the reduction of uncertainty in determining the location of a mobile robot, and its performance is verified by computer simulation and experiment.     

非完整移动机器人目标环绕动态反馈线性化控制

本文针对多个非完整移动机器人对静止或运动目标的环绕追踪问题进行研究.每个机器人仅通过自身和其相邻的机器人的位置与方向信息以及所追踪的目标的位置信息来协调其运动.首先,提出了一种基于动态反馈线性化方法的分布式控制策略,并引入一个控制机器人之间相对角间距的非线性函数,控制机器人间的相对角间距.使多个机器人能够以期望的与目标之间的相对距离、环绕速度和机器人之间的相对角间距对目标进行追踪.然后,利用Lyapunov工具对控制算法进行了渐近稳定性和收敛性分析.最后构建了多移动机器人实验平台,进行了数值仿真和实验验证,仿真和实验的运行结果表明了所提出算法的有效性.     

Finite‐time tracking control of a nonholonomic mobile robot

In this paper, the finite‐time tracking problem is investigated for a nonholonomic wheeled mobile robot in a fifth‐order dynamic model. We consider the whole tracking error system as a cascaded system. Two continuous global finite‐time stabilizing controllers are designed for a second‐order subsystem and a third‐order subsystem respectively. Then finite‐time stability results for cascaded systems are employed to prove that the closed‐loop system satisfies the finite‐time stability. Thus the closed‐loop system can track the reference trajectory in finite‐time when the desired velocities satisfy some conditions. In particular, we discuss the control gains selection for the third‐order finite‐time controller and give sufficient conditions by using Lyapunov and backstepping techniques. Simulation results demonstrate the effectiveness of our method. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

3D environmental extremum seeking navigation of a nonholonomic mobile robot

A nonholonomic under-actuated robot with bounded control travels in a 3D region. A single sensor provides the value of an unknown scalar field at the current location of the robot. We present a new kinematic control paradigm to drive the robot to the maximizer of the field, which is different from conventionally trying to align the velocity vector with the field gradient. The proposed strategy does not employ gradient estimation and is non-demanding with respect to both computation and motion. Its mathematically rigorous analysis and justification are provided. Simulation results confirm the applicability and performance of the proposed guidance approach.     

非完整移动机器人的神经网络鲁棒自适应控制

在非完整移动机器人轨迹跟踪问题中,针对机器人运动学与动力学模型的参数和非参数不确定性,提出了一种混合神经网络鲁棒自适应轨迹跟踪控制器,该控制器由运动学控制器和动力学控制器两部分组成;其中,采用了参数自适应的径向基神经网络对运动学模型的未知部分进行了建模,并采用权值在线调整的单层神经网络和自适应鲁棒控制项构成了动力学控制器;基于Lyapunov方法的设计过程保证了系统的稳定性和收敛性,仿真结果证明了算法的有效性。     

Feedback control strategies for a nonholonomic mobile robot using a nonlinear oscillator

Among control problems for mobile robots, point‐to‐point stabilization is the most challenging since it does not admit designs with smooth static state feedback laws. Stabilization strategies for mobile robots, and nonholonomic systems generally, are smooth, time‐varying or nonsmooth, time‐invariant. Time‐varying control strategies are designed with umdamped linear oscillators but their fixed structure offer limited flexibility in control design. The central theme of this paper lies in use of nonlinear oscillators for mobile robot control. Large numbers of qualitatively different control strategies can be designed using nonlinear oscillators since stiffness and damping can be functions of robot states. We demonstrate by designing two fundamentally different controllers for two‐wheeled mobile robot using two variants of a particular nonlinear oscillator. First controller is dynamic and generates smooth control action. Second controller is almost‐smooth and time‐invariant. While first controller guarantees global asymptotic stability for any desired posture of robot, second controller is stable, and converges robot from almost any posture to desired posture. The only gap in posture space is unstable equilibrium manifold of measure zero. For both control strategies we mathematically establish stability and convergence of mobile robot to desired posture. Simulation results support theoretical claims. ©1999 John Wiley & Sons, Inc.     

Vision-based tracking control of quadrotor for a moving target with nonlinear angular velocity observer

In this article, an automatic tracking process for unmanned aerial vehicle (UAV) system to follow the target with maneuverability is developed, and a vision-based control method is proposed. At first, the image-based visual servoing (IBVS) position control technology is applied without any prior target information for the algorithm practicability. Besides, consider the situation with unavailable angular velocity measurement, a thorough nonlinear attitude controller with an observer based on contraction is proposed. Finally, the whole UAV system stability is demonstrated to be uniformly ultimately bounded, and the bound can be arbitrarily small. Simulation and experiment results are presented to show effectiveness of the designed controller.     

Probabilistic state estimation of dynamic objects with a moving mobile robot

Mobile service robots are designed to operate in dynamic and populated environments. To plan their missions and to perform them successfully, mobile robots need to keep track of relevant changes in the environment. For example, office delivery or cleaning robots must be able to estimate the state of doors or the position of waste-baskets in order to deal with the dynamics of the environment. In this paper we present a probabilistic technique for estimating the state of dynamic objects in the environment of a mobile robot. Our method matches real sensor measurements against expected measurements obtained by a sensor simulation to efficiently and accurately identify the most likely state of each object even if the robot is in motion. The probabilistic approach allows us to incorporate the robot’s uncertainty in its position into the state estimation process. The method has been implemented and tested on a real robot. We present different examples illustrating the efficiency and robustness of our approach.     

基于模糊CMAC的移动机器人轨迹跟踪控制

针对受非完整约束的移动机器人的轨迹跟踪问题,提出了一种基于模糊CMAC的轨迹跟踪控制策略。该策略利用模糊CMAC神经网络逼近移动机器人动力学模型的非线性和不确定,同时与速度误差结合起来构成力矩控制器,并用滑模项来补偿不确定性扰动对系统的影响。李亚普诺夫稳定性定理保证了系统的稳定性和跟踪误差的渐近收敛,仿真结果进一步验证了所提方法的有效性。     

An on-line robot planning strategy for target interception

On-line planning of gross robot motion for moving-object interception is considered in this article within the context of an active prediction planning, and execution (APPE) strategy. The objective is to find an optimal interception point such that the robot end-effector and the object arrive simultaneously at this target pregrasping point. In this approach, the optimality of the selected rendezvous point on the target trajectory is directly dependent on the robot-trajectory planning technique. Thus, for the solution of the general interception problem, three issues must be addressed: (i) optimal rendezvous-point selection, (ii) optimal robot-trajectory planning, and (iii) replanning in response to gross changes in the predicted target trajectory. The effect of uncertainties in the target-trajectory prediction must be considered at each planning stage. Herein, solutions to the first two problems are briefly reviewed as background to the proposed rendezvous-point replanning strategy. This strategy determines when replanning is necessary, modifies the rendezvous point, and iteratively replans robot “patch trajectories” to new interception points. Simulation results using two different on-line robot-motion-generation strategies are also presented. © 1998 John Wiley & Sons, Inc. 15: 97–114, 1998