带扰动的多非完整移动机器人分布式有限时间一致性控制

论文研究多个非完整移动机器人在控制输入存在干扰时,有限时间一致性控制问题.利用坐标变换,将多移动机器人系统的一致性问题转化为非完整约束链式系统的一致性问题,在控制输入带有未知有界干扰的条件下,设计了一种分布式控制算法,并利用Lyapunov理论证明了该算法能够使移动机器人的各个状态在有限时间内达到一致.最后通过数值仿真验证了算法的有效性.     

一种移动机器人路径规划新算法

快速搜索随机树（Rapidly-exploring random Tree Star,RRT*）算法在移动机器人实际应用中规划路径在转向部分存在较多的冗余转折点,导致移动机器人在移动转向过程中出现多次停顿与转向,为剔除规划路径中的冗余路径点,提高机器人移动流畅性,提出一种改进的 RRT*算法。算法将局部逆序试连法引入移动机器人路径规划,在确保RRT*算法概率完备性和渐进最优性的前提下,剔除规划路径中的冗余路径节点,使最终路径更加接近最短路径。通过MATLAB仿真实验证明,规划路径平均长度缩短4%,算法耗时缩短35%,改进后的RRT*算法能缩短规划路径且转向部分路径更加平滑。最后,使用改进后的RRT*算法在室内环境下进行移动机器人路径规划实验。实验结果表明：规划路径上无冗余路径点,且移动机器人沿路径移动流畅。     

机器人路径规划的快速扩展随机树算法综述

路径规划是移动机器人的重要研究内容。快速扩展随机树（Rapidly-Exploring Random Tree，RRT）算法因在机器人路径规划中的成功应用，自提出以来就得到了极大的研究与发展。快速扩展随机树作为一种新颖的随机节点采样算法，相对传统路径规划算法，具有建模时间短、搜索能力强、方便添加非完整约束等优点。介绍了快速扩展随机树算法的基本原理与性质，并从单向随机树扩展、多向随机树扩展、其他改进等方面概括了算法的研究现状。最后，展望了算法未来的研究方向与挑战。     

移动机器人RRT算法改进研究

目前,机器人路径规划常用算法有避障(Bug)算法、概率路线图(PRM)算法、快速搜索随机树(RRT)算法、蚁群算法、人工势场法等,其中RRT算法在路径规划中应用最广。针对RRT算法存在随机性强、偏差大、路径不一定最优、收敛速度慢等缺点,对RRT算法进行改进,引导随机树向目标点生长,借助人工势场的引力思想,并加入自适应策略,通过机器人与目标点位置、速度和加速度的不断变化来改变步长大小,使机器人快速到达目标点。实验结果表明,通过自适应RRT算法可以提高算法收敛性,缩短了算法时间,可以有效应用在移动机器人系统上,提高移动机器人的工作效率。     

移动机器人的实时路径规划研究与仿真

研究移动机器人的实时路径规划问题.为了实现移动机器人在动态不确定环境下实时地避开障碍物,安全到达目标点,提出一种人工势场法与指导性RRT算法结合的改进算法,利用人工势场法在初始环境中预规划一条初始路径.当有新的障碍物出现并且覆盖了初始路径时,启动实时规划.在新的障碍物周围确定合适的规划空间,可利用指导性RRT算法规划局部路径.最后,合并初始路径与局部路径,得出最终路径.仿真结果表明,改进的混合算法能够有效实现移动机器人的无碰撞实时规划,路径长度和规划时间有明显改善.     

加速度约束条件下的非完整移动机器人运动控制

将移动机器人的运动规划与跟踪控制问题合并在一起,对加速度约束条件下的非完整移动机器人运动控制问题进行研究,提出基于贝塞尔曲线的路径规划方法,以满足机器人的非完整约束．在考虑所受加速度约束的条件下,通过规划机器人状态时问轨线的方法实现了时间最优的轨迹规划．基于控制李亚普诺夫函数推导出了轨迹跟踪的控制律．仿真实验结果表明所提出的算法是有效的．     

基于强化学习的快速探索随机树特殊环境中路径重规划算法

针对移动机器人在未知的特殊环境(如U型、狭窄且不规则通道等)下路径规划效率低问题,本文提出一种强化学习(RL)驱动快速探索随机树(RRT)的局部路径重规划方法(RL-RRT).该方法利用Sarsa(λ)优化RRT的随机树扩展过程,既保持未知环境中RRT的随机探索性,又利用Sarsa(λ)缩减无效区域的探索代价.具体来说,在满足移动机器人运动学模型约束的同时,通过设定扩展节点的回报函数、目标距离函数和平滑度目标函数,缩减无效节点,加速探索过程,从而达到路径规划多目标决策优化的目标.仿真实验中,将本方法用于多种未知的特殊环境,实验结果显示出RL-RRT算法的可行性、有效性及其性能优势.     

采用碰撞测试和回归机制的非完整约束机器人快速扩展随机树运动规划

本文提出一种改进的快速扩展随机树(rapidly-exploring random trees,RRT)运动规划方法,用于非完整微分约束下的机器人运动规划.针对类似目标偏好与双向RRT(bi-directional RRT,bi-RRT)等目标区域导向的RRT运动规划所存在的局部极小问题,结合回归检测与碰撞检测机制,设计了一种碰撞检测与回归机制(collision-test and regression mechanism,CR)机制.该方法使得机器人在规划过程中能获取到全局障碍物信息,从而避免对已扩展节点的重复搜索,以及重复对边缘节点的回归测试和避障检测.该机制使得机器人可加快跳出局部极小区域,提高运动规划实的时性.将改进的RRT运动算法在容易产生局部极小值的环境中仿真测试,结果表明该算法在不显著影响其他性能的前提下,可以明显提高规划的实时性.     

基于自适应反步法的轮式移动机器人跟踪控制

轮式移动机器人是一种典型的非完整约束系统.基于反步法提出一种自适应扩展控制器,对含有未知参数的非完整轮式移动机器人动力学系统进行轨迹跟踪控制并且Lyapunov稳定性理论保证跟踪误差渐近收敛到零.为了克服速度跳变产生滑动,加入了神经动力学模型对控制器进行改进.以两驱动轮移动机器人为例,利用运动学自适应控制器设计出转矩控制器,有效解决了不确定非完整轮式移动机器人动力学系统的轨迹跟踪问题.仿真结果证明该方法的正确性和有效性.     

基于滚动时域优化的轮式移动机器人路径跟踪问题研究

轮式移动机器人是典型的非完整约束系统. 本文基于滚动时域控制策略研究轮式移动机器人的路径跟踪问题. 为了既能够保证移动机器人渐近收敛到期望轨迹, 又能够保证在线求解的优化问题的滚动可行性, 参考轨迹 被选为优化问题中的终端等式约束. 仿真结果验证了所提出的控制策略的有效性.     

带滚动约束轮移式机器人动态规划的研究

根据轮移式机器人的运动学模型,研究受到滚动约束轮移式机器人在动态环境中的运动规划问题.将快速随机搜索树算法与优化方法相结合,实现了一种新的算法,规划出既可避障又可满足机器人滚动约束的运动.将该算法运用到动态环境下机器人的运动规划中,并通过仿真表明该算法能较好地引导机器人在动态环境中实现满足滚动约束的避障路径.     

非完整链式系统的路径规划--多项式拟合法

针对非完整链式系统,给出了一种路径规划新算法.该算法将较为困难的非完整系统路径规划问题转化为满足给定端点条件的多项式拟合问题,不仅使路径规划问题得到简化,而且可以很容易地应用于复杂环境下的路径规划问题.仿真结果证实了该方法的有效性.     

Sliding Mode Adaptive Neural-Network Control for Nonholonomic Mobile Modular Manipulators

A general mobile modular manipulator can be defined as a m-wheeled holonomic/nonholonomic mobile platform combining with a n-degree of freedom modular manipulator. This paper presents a sliding mode adaptive neural-network controller for trajectory following of nonholonomic mobile modular manipulators in task space. Dynamic model for the entire mobile modular manipulator is established in consideration of nonholonomic constraints and the interactive motions between the mobile platform and the onboard modular manipulator. Multilayered perceptrons (MLP) are used as estimators to approximate the dynamic model of the mobile modular manipulator. Sliding mode control and direct adaptive technique are combined together to suppress bounded disturbances and modeling errors caused by parameter uncertainties. Simulations are performed to demonstrate that the dynamic modeling method is valid and the controller design algorithm is effective.     

Planning and control of under-actuated mobile manipulators using differential flatness

Mobile manipulators are intrinsically nonholonomic systems since the mobile base is subject to nonholonomic constraints that result from no-slip constraints on the wheels. The highly nonlinear dynamic coupling between the mobile base and the manipulator arm, in addition to the nonholonomic constraints at the base, makes these systems difficult to plan and control. If the system is under-actuated, the problem becomes even more difficult.     

轮式移动机械臂的建模与仿真研究

移动机械臂系统一般由移动平台和机器臂组成,它既具有机器臂的操作灵活性,又具有移动机器人的可移动性,因此其应用范围要比单个系统宽得多。这篇文章主要研究了由非完整移动平台和完整机械臂组成的轮式移动机械臂系统的建模、跟踪控制及仿真问题。首先。利用拉格朗日动力学方程和非完整动力学罗兹方程建立了移动机械臂系统的精确数学模型;然后。利用非线性反馈将系统解耦。采用类PD控制器进行控制。在考虑了非完整约束及移动平台和机械臂的动态交互影响情况下,该控制算法保证系统同时跟踪给定的终端执行器和平台轨迹;最后,使用Maflah6．5对系统进行了仿真研究,仿真结果表明了其数学模型及控制方法的正确有效性。     

基于迭代学习控制的移动机器人轨迹跟踪控制

为提高移动机器人对特定轨迹的重复跟踪能力,提出了采用开闭环PD型迭代学习控制算法对移动机器人进行轨迹跟踪控制的方法。建立了包含外界干扰的非完整约束条件下的轮式移动机器人运动学模型,给出了系统的控制算法和控制结构。仿真结果表明,采用开闭环PD型迭代学习控制算法对轨迹跟踪是可行有效的,收敛速度优于其他迭代学习算法。     

On Multiple Secondary Task Execution of Redundant Nonholonomic Mobile Manipulators

This paper investigates self-motion control of redundant nonholonomic mobile manipulators, to execute multiple secondary tasks including tip-over prevention, singularity removal, obstacle avoidance and physical limits escape. An extended gradient projection method (EGPM) is proposed to determine self-motion directions, and a real-time fuzzy logic self-motion planner (FLSMP) is devised to generate the corresponding self-motion magnitudes. Unlike the task-priority allocation method and the extended Jacobian method, the proposed scheme is simple to implement and is free from algorithm singularities. The proposed dynamic model is established with consideration of nonholonomic constraints of the mobile platform, interactive motions between the mobile platform and the onboard manipulator, as well as self-motions allowed by redundancy of the entire robot. Furthermore, a robust adaptive neural-network controller (RANNC) is developed to accomplish multiple secondary tasks without affecting the primary one in the workspace. The RANNC does not rely on precise prior knowledge of dynamic parameters and can suppress bounded external disturbance effectively. In addition, the RANNC does not require any off-line training and can ensure the control performance by online adjusting the neural-network parameters through adaptation laws. The effectiveness of the proposed algorithm is verified via simulations on a three-wheeled redundant nonholonomic mobile manipulator.     

Cooperative adaptive consensus tracking for multiple nonholonomic mobile robots

This paper addresses the cooperative adaptive consensus tracking for a group of multiple nonholonomic mobile robots, where the nonholonomic robot model is assumed to be a canonical vehicle having two actuated wheels and one passive wheel. By integrating a kinematic controller and a torque controller for the nonholonomic robotic system, a cooperative adaptive consensus tracking strategy is developed for the uncertain dynamic models using Lyapunov-like analysis in combination with backstepping approach and sliding mode technique. A key feature of the developed adaptive consensus tracking algorithm is the introduction of a directed network topology into the control constraints based on algebraic graph theory to characterise the communication interaction among robots, which plays an important role in realising the cooperative consensus tracking with respect to a specific common reference trajectory. Furthermore, a novel framework is proposed for developing a unified methodology for the convergence analysis of the closed-loop control systems, which can fully ensure the desired adaptive consensus tracking for multiple nonholonomic mobile robots. Subsequently, illustrative examples and numerical simulations are provided to demonstrate and visualise the theoretical results.     

A novel obstacle avoidance algorithm: “Follow the Gap Method”

In this paper, a novel obstacle avoidance method is designed and applied to an experimental autonomous ground vehicle system. The proposed method brings a new solution to the problem and has several advantages compared to previous methods. This novel algorithm is easy to tune and it takes into consideration the field of view and the nonholonomic constraints of the robot. Moreover the method does not have a local minimum problem and results in safer trajectories because of its inherent properties in the definition of the algorithm. The proposed algorithm is tested in simulations and after the observation of successful results, experimental tests are performed using static and dynamic obstacle scenarios. The experimental test platform is an autonomous ground vehicle with Ackermann steering geometry which brings nonholonomic constraints to the vehicle. Experimental results show that the task of obstacle avoidance can be achieved using the algorithm on the autonomous vehicle platform. The algorithm is very promising for application in mobile and industrial robotics where obstacle avoidance is a feature of the robotic system.