Tracking multiple moving targets with swarms of mobile robots

This paper presents a distributed approach to enable mobile robot swarms to track multiple targets moving unpredictably. The proposed approach consists of two constituent algorithms: local interaction and target tracking. When the robots are faster than the targets, Lyapunov theory can be applied to show that the robots converge asymptotically to each vertex of the desired equilateral triangular configurations while tracking the targets. Toward practical implementation of the algorithms, it is important to realize the observation capability of individual robots in an inexpensive and efficient way. A new proximity sensor that we call dual rotating infrared (DRIr) sensor is developed to meet these requirements. Both our simulation and experimental results employing the proposed algorithms and DRIr sensors confirm that the proposed distributed multi-target tracking method for a swarm of robots is effective and easy to implement.     

Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm

This paper proposes a TSK-type recurrent neuro fuzzy system (TRNFS) and hybrid algorithm- GA_BPPSO to develop a direct adaptive control scheme for stable path tracking of mobile robots. The TRNFS is a modified model of the recurrent fuzzy neural network (RFNN) to obtain generalization and fast convergence. The TRNFS is designed using hybridization of genetic algorithm (GA), back-propagation (BP), and particle swarm optimization (PSO), called GA_BPPSO. For the tracking control of mobile robot, two TRNFSs are designed to generate the control inputs by direct adaptive control scheme and hybrid algorithm GA_BPPSO. Through simulation results, we demonstrate the effectiveness of our proposed controller.     

基于虚拟现实技术的移动机器人视觉伺服控制系统设计

为了有效确保移动机器人视觉伺服控制效果,提高移动机器人视觉伺服控制精度,设计了基于虚拟现实技术的移动机器人视觉伺服控制系统。通过三维视觉传感器和立体显示器等虚拟环境的I/O设备、位姿传感器、视觉图像处理器以及伺服控制器元件,完成系统硬件设计。从运动学和动力学两个方面,搭建移动机器人数学模型,利用标定的视觉相机,生成移动机器人实时视觉图像,通过图像滤波、畸变校正等步骤,完成图像的预处理。利用视觉图像,构建移动机器人虚拟移动环境。在虚拟现实技术下,通过目标定位、路线生成、碰撞检测、路线调整等步骤,规划移动机器人行动路线,通过控制量的计算,实现视觉伺服控制功能。系统测试结果表明,所设计控制系统的位置控制误差较小,姿态角和移动速度控制误差仅为0.05°和0.12m/s,移动机器人碰撞次数较少,具有较好的移动机器人视觉伺服控制效果,能够有效提高移动机器人视觉伺服控制精度。     

Fuzzy logic path tracking control for autonomous non-holonomic mobile robots: Design of System on a Chip

This paper presents a System on Chip (SoC) for the path following task of autonomous non-holonomic mobile robots. The SoC consists of a parameterized Digital Fuzzy Logic Controller (DFLC) core and a flow control algorithm that runs under the Xilinx Microblaze soft processor core. The fuzzy controller supports a fuzzy path tracking algorithm introduced by the authors. The FPGA board hosting the SoC was attached to an actual differential-drive Pioneer 3-DX8 robot, which was used in field experiments in order to assess the overall performance of the tracking scheme. Moreover, quantization problems and limitations imposed by the system configuration are also discussed.     

Tracking control of a nonholonomic mobile robot using compound cosine function neural networks

The purpose of this paper is to propose a compound cosine function neural network with continuous learning algorithm for the velocity and orientation angle tracking control of a nonholonomic mobile robot with nonlinear disturbances. Herein, two neural network (NN) controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the adaptive control of the mobile robot. The neuron function of the hidden layer in the three-layer feed-forward network structure is on the basis of combining a cosine function with a unipolar sigmoid function. The developed neural network controllers have simple algorithm and fast learning convergence because the weight values are only adjusted between the nodes in hidden layer and the output nodes, while the weight values between the input layer and the hidden layer are one, i.e. constant, without the weight adjustment. Therefore, the main advantages of this control system are the real-time control capability and the robustness by use of the proposed neural network controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances which are considered as dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of nonholonomic mobile robots has real-time control capability, better robustness and higher control precision. The compound cosine function neural network provides us with a new way to solve tracking control problems for mobile robots.     

An Intelligent Robust Tracking Control for a Class of Electrically Driven Mobile Robots

This paper addresses the problem of designing robust tracking control for a class of uncertain wheeled mobile robots actuated by brushed direct current motors. This class of electrically‐driven mechanical systems consists of the robot kinematics, the robot dynamics, and the wheel actuator dynamics. Via the backstepping technique, an intelligent robust tracking control scheme that integrates a kinematic controller and an adaptive neural network‐based (or fuzzy‐based) controller is developed such that all of the states and signals of the closed‐loop system are bounded and the tracking error can be made as small as possible. Two adaptive approximation systems are constructed to learn the behaviors of unknown mechanical and electrical dynamics. The effects of both the approximation errors and the unmodeled time‐varying perturbations in the input and virtual‐input weighting matrices are counteracted by suitably tuning the control gains. Consequently, the robust control scheme developed here can be employed to handle a broader class of electrically‐driven wheeled mobile robots in the presence of high‐degree time‐varying uncertainties. Finally, a simulation example is given to demonstrate the effectiveness of the developed control scheme.     

基于深度学习的移动机器人目标跟踪系统

针对当前智能移动机器人在跟踪过程中常因目标发生外观形态上的变化而丢失跟踪目标的问题,利用Caffe深度学习框架和ROS机器人操作系统作为开发平台,设计一个高准确度及高实时性的移动机器人目标跟踪系统并进行了研究.使用对于目标形变、视角、轻微遮挡及光照变化具有鲁棒性的基于孪生卷积神经网络的GOTURN目标跟踪算法,通过ROS系统为桥梁使离线训练的跟踪模型实时应用于TurtleBot移动机器人上,并开展了详细的测试.实验结果表明,该目标跟踪系统不仅设计方案可行,实现了移动机器人在各种复杂场景下有效地跟踪目标,还具有成本低、性能高和易扩展等特点.     

基于强化学习和模糊逻辑的移动机器人导航

自主导航是移动机器人的一项关键技术。该文采用强化学习结合模糊逻辑的方法实现了未知环境下自主式移动机机器人的导航控制。文中首先介绍了强化学习原理,然后设计了一种未知环境下机器人导航框架。该框架由避碰模块、寻找目标模块和行为选择模块组成。针对该框架,提出了一种基于强化学习和模糊逻辑的学习、规划算法：在对避碰和寻找目标行为进行独立学习后,利用超声波传感器得到的环境信息进行行为选择,使机器人在成功避碰的同时到达目标点。最后通过大量的仿真实验,证明了算法的有效性。     

Autonomous stair-climbing with miniature jumping robots.

The problem of vision-guided control of miniature mobile robots is investigated. Untethered mobile robots with small physical dimensions of around 10 cm or less do not permit powerful onboard computers because of size and power constraints. These challenges have, in the past, reduced the functionality of such devices to that of a complex remote control vehicle with fancy sensors. With the help of a computationally more powerful entity such as a larger companion robot, the control loop can be closed. Using the miniature robot's video transmission or that of an observer to localize it in the world, control commands can be computed and relayed to the inept robot. The result is a system that exhibits autonomous capabilities. The framework presented here solves the problem of climbing stairs with the miniature Scout robot. The robot's unique locomotion mode, the jump, is employed to hop one step at a time. Methods for externally tracking the Scout are developed. A large number of real-world experiments are conducted and the results discussed.     

加入临时路径的移动机器人路径跟踪模糊控制

对含不确定性的移动机器人系统设计了路径跟踪模糊控制方法。该方法引入临时路径,使机器人先从初始位置出发沿临时路径行进,当移动到期望路径附近时,再让机器人跟踪期望路径。整个控制过程只需要一个模糊控制器,极大地减少了工作量,并引进积分环节以消除稳态误差。仿真和实验结果验证了该方法的有效性。     

双轮移动机器人安全目标追踪与自动避障算法

设计了双轮移动机器人安全目标追踪算法和双回路的追踪与避障控制方案.内层控制回路是目标追踪的控制律,用来指导机器人追踪到指定目标并保持一定的安全距离,而且兼顾了机器人在运行速度上的限制和追踪的时间效率,其控制的渐近稳定性用Lyapunov函数法进行了证明.当遇到障碍物时,外层控制回路根据超声传感器的信息和阻抗控制的概念产生阻抗虚拟力,将期望目标调整到虚拟位置,使机器人能够自动转向以避开障碍物.仿真研究和实验结果证明了追踪算法的有效性和避障方法的可行性.     

基于粒子滤波的欠驱动移动机器人多目标点跟踪控制

针对欠驱动移动机器人的多目标点跟踪问题,提出了一种基于粒子滤波的高精度跟踪控制方法;具体地,在考虑移动机器人采样噪声的情况下,首先利用粒子滤波对移动机器人的位置信息进行处理,得到精准可靠的移动机器人状态信息;在此基础上,根据欠驱动移动机器人的运动学模型以及目标点的分布状况,设计基于反馈控制的多目标点跟踪控制方法;相对于传统的欠驱动移动机器人目标点跟踪控制算法,改进了该控制方法中增益参数的约束条件,有效避免了移动机器人在接近目标点时产生的奇异现象,有效提高了移动机器人对目标点的跟踪精度;此外,分析了该目标点跟踪控制系统的稳定性,并通过数值仿真验证了所提方法的可行性与有效性.     

非完整移动机器人道路跟踪控制器设计及应用

讨论一类非完整约束条件下的移动机器人道路跟踪控制问题，综合后推方法与模糊滑模控制方法设计非完整移动机器人的状态反馈控制系统，并根据Lyapunov稳定性定理后推设计时变光滑反馈控制律，当存在较大侧向误差时，模糊滑模控制器确保移动机器人沿稳定的工作区域减小误差；当误差比较小时，时变光滑状态反馈控制实现对移动机器人的平稳镇定，采用移动机器人Amigobot作为实验平台，验证了控制器设计的有效性。     

轮式机器人滑模轨迹跟踪控制器设计

轮式机器人是一个典型的非完整性系统。由于非线性和非完整特性,很难为移动机器人系统的轨迹跟踪建立一个合适的模型。介绍了一种轮式机器人滑模轨迹跟踪控制方法。滑模控制是一个鲁棒的控制方法,能渐近的按一条所期望的轨迹稳定移动机器人。以之为基础,描述了轮式机器人的动力学模型并在二维坐标下建立了运动学方程,根据运动学方程设计滑模控制器,该控制器使得机器人的位置误差收敛到零。     

基于行为的移动机器人避障系统的设计

随着机器人应用范围的不断扩展,机器人所面临的工作环境也越来越复杂,多数是未知的、动态的和非结构化的。通过对基于行为的机器人控制技术的研究,提出了一种实用的自主移动机器人智能避障控制方案,阐述了其具体实现技术;将基于行为的控制技术融合进模糊控制的思想中,使移动机器人的行为通过运用模糊控制和基于优先度的行为决策来实现。试验证明了这一方法的有效性和实时性。     

遥自主移动机器人系统设计及实现

为了解决移动机器人在特定环境下自主性不强的问题,构建了自主移动机器人的遥操作控制系统。通过无线网络传输的通信方式实现了经过透视解算展开后的全景图像的传输和基于USB操纵杆的多功能远程控制平台的开发。详细介绍了机器人利用超声波传感器进行自主模糊避障的算法,利用USB操纵杆对机器人遥操作的程序实现,以及全景摄像头透视解算和视频压缩的方法。实验结果表明,构建的遥操作控制系统可以实现良好的人机交互,使移动机器人的自主性更强,更加智能化。     

Fuzzy logic techniques for navigation of several mobile robots

In this paper, navigation techniques for several mobile robots as many as one thousand robots using fuzzy logic are investigated in a totally unknown environment. Fuzzy logic controllers (FLC) using different membership functions are developed and used to navigate mobile robots. First a fuzzy controller has been used with four types of input members, two types of output members and three parameters each. Next two types of fuzzy controllers have been developed having same input members and output members with five parameters each. Each robot has an array of ultrasonic sensors for measuring the distances of obstacles around it and an infrared sensor for detecting the bearing of the target. These techniques have been demonstrated in various exercises, which depicts that the robots are able to avoid obstacles as well as negotiate the dead ends and reach the targets efficiently. Amongst the techniques developed, FLC having Gaussian membership function is found to be most efficient for mobile robots navigation.     

A geometric approach to deploying robot swarms

We discuss the fundamental problems and practical issues underlying the deployment of a swarm of autonomous mobile robots that can potentially be used to build mobile robotic sensor networks. For the purpose, a geometric approach is proposed that allows robots to configure themselves into a two-dimensional plane with uniform spatial density. Particular emphasis is paid to the hole repair capability for dynamic network reconfiguration. Specifically, each robot interacts selectively with two neighboring robots so that three robots can converge onto each vertex of the equilateral triangle configuration. Based on the local interaction, the self-configuration algorithm is presented to enable a swarm of robots to form a communication network arranged in equilateral triangular lattices by shuffling the neighbors. Convergence of the algorithms is mathematically proved using Lyapunov theory. Moreover, it is verified that the self-reparation algorithm enables robot swarms to reconfigure themselves when holes exist in the network or new robots are added to the network. Through extensive simulations, we validate the feasibility of applying the proposed algorithms to self-configuring a network of mobile robotic sensors. We describe in detail the features of the algorithm, including self-organization, self-stabilization, and robustness, with the results of the simulation.     

A Behavioral Adaptive Fuzzy controller of multi robots in a cluster space

Cooperation between autonomous robot vehicles holds several promising advantages like robustness, adaptability, configurability, and scalability. Coordination between the different robots and the individual relative motion represent both the main challenges especially when dealing with formation control and maintenance. Cluster space control provides a simple concept for controlling multi-agent formation. In the classical approach, formation control is the unique task for the multi-agent system. In this paper, the development and application of a novel Behavioral Adaptive Fuzzy-based Cluster Space Control (BAFC) to non-holonomic robots is presented. By applying a fuzzy priority control approach, BAFC deals with two conflicting tasks: formation maintenance and target following. Using priority rules, the fuzzy approach is used to adapt the controller and therefore the behavior of the system, taking into accounts the errors in the formation states and the target following states. The control approach is easy to implement and has been implemented in this paper using SIMULINK real-time platform. The communication between the different agents and the controller is established through Wi-Fi link. Both simulation and experimental results demonstrate the behavioral response where the robot performs the higher priority tasks first. This new approach shows a great performance with a lower control signal when benchmarked with previously known results in the literature.     

模糊神经网络在移动机器人信息融合中的应用

针对移动机器人所用的传感器,提出了一种用于多传感器信息融合的方法,将模糊逻辑和神经网络结合起来,构建了模糊神经网络,并建立了网络的计算模型.通过建立的模糊神经网络对移动机器人的多传感器信息进行融合,实现了移动机器人对动态环境中障碍和环境类型的实时识别以及无冲突运动.网络的训练和试验表明该方法在移动机器人躲避运动物体中是可行的.