移动机器人自适应视觉伺服镇定控制

对有单目视觉的移动机器人系统,提出了一种自适应视觉伺服镇定控制算法;在缺乏深度信息传感器并且摄像机外参数未知的情况下,该算法利用视觉反馈实现了移动机器人位置和姿态的渐近稳定.由于机器人坐标系与摄像机坐标系之间的平移外参数(手眼参数)是未知的,本文利用静态特征点的位姿变化特性,建立移动机器人在摄像机坐标系下的运动学模型.然后,利用单应矩阵分解的方法得到了可测的角度误差信号,并结合2维图像误差信号,通过一组坐标变换,得到了系统的开环误差方程.在此基础之上,基于Lyapunov稳定性理论设计了一种自适应镇定控制算法.理论分析、仿真与实验结果均证明了本文所设计的单目视觉控制器在摄像机外参数未知的情况下,可以使移动机器人渐近稳定到期望的位姿.     

基于神经网络的不确定移动机器人鲁棒自适应跟踪控制

针对含运动学未知参数以及动力学模型不确定的非完整轮式移动机器人轨迹跟踪问题,基于Radical Basis Function(径向基函数)神经网络,提出了一种鲁棒自适应控制器.首先,考虑移动机器人运动学参数未知的情况,提出了一种含自适应参数的运动学控制器,用以补偿参数不确定性导致的系统误差;其次,利用神经网络控制技术,对于机器人在移动中动力学模型不确定问题,提出了一种具有鲁棒性的动力学控制器,使得移动机器人可以在不知道具体动力学模型的情况下跟踪到目标轨迹;最后利用Lyapunov稳定性理论证明了整个系统的稳定性.通过数值仿真验证了所设计的控制器的可行性.     

基于自适应神经网络的AUVs路径跟踪控制

为解决由于随时间变化水动力阻尼引起的参数变化和不确定性的问题,提出了基于径向基函数神经网络的未知评估算法,引入自适应算法以保证神经网络权值的最优评估.基于Lyapunov稳定性理论,设计一种自适应神经网络控制器以保证路径跟踪系统中所有误差状态都趋于稳定.为了验证该控制器的可行性,对系统施加如位置误差、方向误差等虚拟干扰,证明该控制器可将误差消减为零.另一方面,机器人在以恒定的速度行驶时,每个航点被指定一个适合半径的圆弧可以保证其有较高的精度.为了评估路径跟踪控制器的性能,提出直线型和直线加圆弧型路径方案.仿真结果表明,该控制器可以有效地消除机器人非线性和模型不确定性造成的干扰.     

基于扰动观测器的机器人自适应神经网络跟踪控制研究

为解决机器人动力学模型未知问题并提升系统鲁棒性,本文基于扰动观测器,考虑动力学模型未知的情况,设计了一种自适应神经网络（Neural network,NN）跟踪控制器.首先分析了机器人运动学和动力学模型,针对模型已知的情况,提出了刚体机械臂通用模型跟踪控制策略;在考虑动力学模型未知的情况下,利用径向基函数（Radial basis function,RBF）神经网络设计基于全状态反馈的自适应神经网络跟踪控制器,并通过设计扰动观测器补偿系统中的未知扰动.利用李雅普诺夫理论证明所提出的控制策略可以使闭环系统误差信号半全局一致有界（Semi-globally uniformly bounded,SGUB）,并通过选择合适的增益参数可以将跟踪误差收敛到零域.仿真结果证明所提出算法的有效性并且所提出的控制器在Baxter机器人平台上得到了实验验证.     

带有未知参数和有界干扰的移动机器人轨迹跟踪控

针对模型参数未知和存在有界干扰的非完整移动机器人的轨迹跟踪控制问题,本文提出了一种鲁棒自适应轨迹跟踪控制器方法.非完整移动机器人的控制难点在于它的运动学系统是欠驱动的.针对这一难点,本文利用横截函数的思想,引入新的辅助控制器,使得非完整移动机器人系统不再是一个欠驱动系统,缩减了控制器设计的难度,进而利用非线性自适应算法和参数映射方法构造李雅谱诺夫函数.通过李雅普诺夫方法设计控制器和参数自适应器,从而使得非完整移动机器人的跟随误差任意小,即可以任意小的误差来跟随任意给定的参考轨迹.仿真结果证明了方法的有效性.     

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

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。     

一种用于移动机器人状态和参数估计的自适应UKF算法

For improving the estimation accuracy and the convergence speed of the unscented Kalman filter(UKF),a novel adaptive filter method is proposed.The error between the covariance matrices of innovation measurements and their corresponding estimations/predictions is utilized as the cost function.On the basis of the MIT rule,an adaptive algorithm is designed to update the covariance of the process uncertainties online by minimizing the cost function.The updated covariance is fed back into the normal UKF.Such an adaptive mechanism is intended to compensate the lack of a priori knowledge of the process uncertainty distribution and to improve the performance of UKF for the active state and parameter estimations.The asymptotic properties of this adaptive UKF are discussed.Simulations are conducted using an omni-directional mobile robot,and the results are compared with those obtained by normal UKF to demonstrate its effectiveness and advantage over the previous methods.     

一种用于移动机器人状态和参数估计的自适应UKF算法

为了提高 UKF 的估计精度和收敛性, 提出了一种新的自适应滤波方法. 新息方差阵的测量值和其相应的估计/预测值的差被用于构造指标函数. MIT 规则被用于构造自适应机制以指标函数最小来在线更新过程不确定性的方差值. 更新后的方差反馈给常规 UKF. 这种自适应机制主要用于补偿过程噪声分布的先验信息不足以及提高 UKF 状态和参数的主动估计性能. 讨论了自适应 UKF 的渐进稳定性. 在全方位移动机器人上进行了仿真, 结果表明与常规的 UKF 相比自适应 UKF 更有效更精确.     

An immunological approach to mobile robot reactive navigation

In this paper, a reactive immune network (RIN) is proposed and employed for mobile robot navigation within unknown environments. Rather than building a detailed mathematical model of artificial immune systems, this study tries to explore the principle in an immune network focusing on its self-organization, adaptive learning capability, and immune feedback. In addition, an adaptive virtual target method is integrated to solve the local minima problem in navigation. Several trapping situations designed by the early researchers are adopted to evaluate the performance of the proposed architecture. Simulation results show that the mobile robot is capable of avoiding obstacles, escaping traps, and reaching the goal efficiently and effectively.     

Sliding Mode Neuro Adaptive Control in Trajectory Tracking for Mobile Robots

In this work a neural indirect sliding mode control method for mobile robots is proposed. Due to the nonholonomic property and restricted mobility, the trajectory tracking of this system has been one of the research topics for the last ten years. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a practical design that combines an indirect neural adaptation technique with sliding mode control to compensate the dynamics of the robot. Using an online adaptation scheme, a neural sliding mode controller is used to approximate the equivalent control in the neighbourhood of the sliding manifold. A sliding control is appended to ensure that the neural sliding mode control can achieve a stable closed-loop system for the trajectory-tracking control of a mobile robot with unknown nonlinear dynamics. The proposed design simultaneously guarantees the stability of the adaptation of the neural nets and obtains suitable equivalent control when the parameters of the robot model are unknown in advance. The robust adaptive scheme is applied to a mobile robot and shown to be able to guarantee that the output tracking error will converge to zero.     

Robust adaptive neural network control for environmental boundary tracking by mobile robots

The paper addresses the problem of environmental boundary tracking for the nonholonomic mobile robot with uncertain dynamics and external disturbances. To do environmental boundary tracking, a reference velocity is designed for the nonholonomic mobile robot. In this paper, a radial basis function neural network (NN) is used to approximate a nonlinear function containing the uncertain model terms and the elements of the Hessian matrix of the environmental concentration function. Then, the NN approximator is combined with a robust control to construct a robust adaptive NN control for the mobile robot to track the desired environment boundary. It is proved that the tracking error can be guaranteed to converge to zero in the ultimate. Simulation results are presented to illustrate the stability of the robust adaptive control. Copyright © 2011 John Wiley & Sons, Ltd.     

Homography-based visual servo regulation of mobile robots.

A monocular camera-based vision system attached to a mobile robot (i.e., the camera-in-hand configuration) is considered in this paper. By comparing corresponding target points of an object from two different camera images, geometric relationships are exploited to derive a transformation that relates the actual position and orientation of the mobile robot to a reference position and orientation. This transformation is used to synthesize a rotation and translation error system from the current position and orientation to the fixed reference position and orientation. Lyapunov-based techniques are used to construct an adaptive estimate to compensate for a constant, unmeasurable depth parameter, and to prove asymptotic regulation of the mobile robot. The contribution of this paper is that Lyapunov techniques are exploited to craft an adaptive controller that enables mobile robot position and orientation regulation despite the lack of an object model and the lack of depth information. Experimental results are provided to illustrate the performance of the controller.     

Robust Adaptive Terminal Sliding Mode Control of an Omnidirectional Mobile Robot for Aircraft Skin Inspection

In this paper, an adaptive terminal sliding mode control scheme for an omnidirectional mobile robot is proposed as a robust solution to the trajectory tracking control problem. The omnidirectional mobile robot has a double-frame structure, which adsorbes on the aircraft surface by suction cups. The major difficulties lie in the existence of nonholonomic constraints, system uncertainty and external disturbance. To overcome these difficulties, the kinematic model is established, the dynamic model is derived by using Lagrange method. Then, a robust adaptive terminal sliding mode (RATSM) control scheme is proposed to solve the problem of state stabilization and trajectory tracking. In order to enhance the robustness of the system, an adaptive online estimation law is designed to overcome the total uncertainty. Subsequently, the asymptotic stability of the system without total uncertainty is proved with basis on Lyapunov theory, and the system considering total uncertainty can converge to the domain containing the origin. Simulation results are given to show the verification and validation of the proposed control scheme.

     

Approximation-based adaptive control for a class of mobile robots with unknown skidding and slipping

An adaptive tracking control approach using function approximation technique is proposed for trajectory tracking of Type (2,0) wheeled mobile robots with unknown skidding and slipping in polar coordinates and at the dynamic level. The nonlinear disturbance observer (NDO) is used to estimate a nonlinear disturbance term including unknown skidding and slipping. The adaptive control system is designed via the function approximation technique using neural networks employed to compensate the NDO error. It is proved that all signals of the controlled closed-loop system are uniformly bounded and the point tracking errors converge to an adjustable neighborhood of the origin regardless of large initial tracking errors and unknown skidding and slipping. Simulation results are presented to validate the good tracking performance and robustness of the proposed control system against unknown skidding and slipping.     

移动机器人滑动参数定界及鲁棒镇定控制

本文针对带运动学参数不确定性的野外轮式移动机器人模型的在线辨识、定界和点镇定控制问题展开了研究.考虑了移动机器人二维平面运动过程中所存在的滑动效应和自身几何参数未知等不确定性,并将其建模为运动学模型中所包含的未知时变参数.通过引入基于有界误差假设的非线性集员滤波方法,对移动机器人运动学模型中存在的不确定性参数进行了辨识和定界.在此基础上结合backstepping控制思想和Lyapunov分析方法解决了移动机器人的鲁棒镇定问题,在存在滑动参数干扰的情况下实现了移动机器人的全局指数收敛点镇定控制,提高了整体控制系统的稳定性和鲁棒性.仿真结果证明了本方法的有效性和鲁棒性.     

Adaptive Polar-Space Motion Control for Embedded Omnidirectional Mobile Robots with Parameter Variations and Uncertainties

This paper presents an adaptive polar-space motion controller for trajectory tracking and stabilization of a three-wheeled, embedded omnidirectional mobile robot with parameter variations and uncertainties caused by friction, slip and payloads. With the derived dynamic model in polar coordinates, an adaptive motion controller is synthesized via the adaptive backstepping approach. This proposed polar-space robust adaptive motion controller was implemented into an embedded processor using a field-programmable gate array (FPGA) chip. Furthermore, the embedded adaptive motion controller works with a reusable user IP (Intellectual Property) core library and an embedded real-time operating system (RTOS) in the same chip to steer the mobile robot to track the desired trajectory by using hardware/software co-design technique and SoPC (system-on-a-programmable-chip) technology. Simulation results are conducted to show the merit of the proposed polar-space control method in comparison with a conventional proportional-integral (PI) feedback controller and a non-adaptive polar-space kinematic controller. Finally, the effectiveness and performance of the proposed embedded adaptive motion controller are exemplified by conducting several experiments on steering an embedded omnidirectional mobile robot.     

基于迁移学习的移动机器人单帧图像坡度检测算法

针对未知环境下移动机器人平稳上坡控制对坡度感知精度的要求,本文提出了一种基于迁移学习的移动机器人单帧图像坡度检测算法.利用室内图像标准数据集训练深度卷积神经场-全连接超像素池化网络(deep convolutional neural field-fully connected superpixel pooling ne...     

轮子纵向打滑条件下的移动机器人自适应跟踪控制

针对纵向滑动参数未知的轮式移动机器人的轨迹跟踪问题,提出一种自适应跟踪控制策略.利用两个未知参数来描述移动机器人左右轮的纵向打滑程度,建立了产生纵向滑动的差分驱动轮式移动机器人的运动学模型;设计了补偿纵向滑动的自适应非线性反馈控制律;应用 Lyapunov 稳定性理论与 Barbalat 定理证明了闭环系统的稳定性;同时,提出了一种由极点配置方法在线调整控制器增益的方法.仿真结果验证了所提出控制方法的有效性.     

非完整移动机器人轨迹跟踪自适应控制器设计

针对轮式移动机器人的非完整运动学模型,将自适应反演控制技术和李亚普诺夫稳定性理论应用于机器人轨迹跟踪控制,设计了具有全局渐近稳定性的自适应轨迹跟踪控制器,并在Matlab环境下实现了移动机器人对直线和椭圆2种轨迹追踪的仿真实验.实验表明:该控制方法在轨迹跟踪控制中有较好的航向跟踪效果,对机器人非完整系统模型的非线性特性...