Two-wheeled mobile robot motion control in dynamic environments

In this paper, a feedback control scheme of a two-wheeled mobile robot is explored in dynamic environments. In the existence of local minima, the design of controller is based on Lyapunov function candidate and considers virtual forces information including detouring force. Simulation results are presented to show the effectiveness of the proposed control scheme.     

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.     

Modeling unknown environments with a mobile robot

The exploration of an unknown environment is an important task for the new generation of mobile service robots. These robots are supposed to operate in dynamic and changing environments together with human beings and other static or moving objects. Sensors that are capable of providing the quality of information that is required for the described scenario are optical sensors like digital cameras and laserscanners. In this paper sensor integration and fusion for such sensors is described. Complementary sensor information is transformed into a common representation in order to achieve a cooperating sensor system. Sensor fusion is performed by matching the local perception of a laserscanner and a camera system with a global model that is being built up incrementally. The Mahalanobis-distance is used as matching criterion and a Kalman-filter is used to fuse matching features. A common representation including the uncertainty and the confidence is used for all scene features. The system's performance is demonstrated for the task of exploring an unknown environment and incrementally building up a geometrical model of it.     

Intelligent control for the vision-based indoor navigation of an A-life mobile robot

Realizing steady and reliable navigation is a prerequisite for a mobile robot, but this facility is often weakened by an unavoidable slip or some irreparable drift errors of sensors in long-distance navigation. Although perceptual landmarks were solutions to such problems, it is impossible not to miss landmarks occasionally at some specific spots when the robot moves at different speeds, especially at higher speeds. If the landmarks are put at random intervals, or if the illumination conditions are not good, the landmarks will be easier to miss. In order to detect and extract artificial landmarks robustly under multiple illumination conditions, some low-level but robust image processing techniques were implemented. The moving speed and self-location were controlled by the visual servo control method. In cases where a robot suddenly misses some specific landmarks when it is moving, it will find them again in a short time based on its intelligence and the inertia of the previous search motion. These methods were verified by the reliable vision-based indoor navigation of an A-life mobile robot.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Saliency and spatial information-based landmark selection for mobile robot navigation in natural environments

In this paper, a landmark selection and tracking approach is presented for mobile robot navigation in natural environments, using textural distinctiveness-based saliency detection and spatial information acquired from stereo data. The presented method focuses on achieving high robustness of tracking rather than self-positioning accuracy. The landmark selection method is designed to select a small amount of the most salient feature points in a wide variety of sparse unknown environments to ensure successful matching. Landmarks are selected by an iterative algorithm from a textural distinctiveness-based saliency map extended with spatial information, where a repulsive potential field is created around the position of each already selected landmark for better distribution in order to increase robustness. The template matching of landmarks is aided with visual odometry-based motion estimation. Other robustness increasing strategies includes estimating landmark positions by unscented Kalman filters as well as from surrounding landmarks. Experimental results show that the introduced method is robust and suitable for natural environments.     

Sliding mode control for trajectory tracking of mobile robot in the RFID sensor space

This paper presents a sliding mode control method for wheeled mobile robots. Because of the nonlinear and nonholonomic properties, it is difficult to establish an appropriate model of the mobile robot system for trajectory tracking. A robust control law which is called sliding mode control is proposed for asymptotically stabilizing the mobile robot to a desired trajectory. The posture of the mobile robot (including the position and heading direction) is presented and the kinematics equations are established in the two-dimensional coordinates. According to the kinematics equations, the controller is designed to find an acceptable control law so that the tracking error will approximate 0 as the time approaches infinity with an initial error. The RFID sensor space is used to estimate the real posture of the mobile robot. Simulation and experiment demonstrate the efficacy of the proposed system for robust tracking of mobile robots. Recommended by Sooyong Lee under the direction of Editor Jae-Bok Song. This work was supported by the Korea Science and Engineering (KOSEF) grant funded by the Korea government (MOST) (No. R01-2007-000-10171-0). Jun Ho Lee received the M.S degree in Mechanical Engineering from Pusan National University. His research interests include factory automation and sliding mode control. Cong Lin received the B.S. degree in Electrical Engineering from Jilin University and the M.S degree in Electrical Engineering from Pusan National University. His research interests include neural network and sliding mode control. Hoon Lim is currently a M.S student in Electrical Engineering of Pusan National University. His research interests include mobile manipulator and sliding mode control. Jang Myung Lee received the B.S. and M.S degrees in Electronics Engineering from Seoul National University, Korea. He received the Ph.D. degree in Computer from the University of Southern California, Los Angeles. Now, he is a Professor in Pusan National University. His research interests include integrated manufacturing systems and intelligent control.     

Supervised learning technique for a mobile robot controller in a visual line tracking task

This article deals with the development of learning methods for an intelligent control system for an autonomous mobile robot. On the basis of visual servoing, an approach to learning the skill of tracking colored guidelines is proposed. This approach utilizes a robust and adaptive image processing method to acquire features of the colored guidelines and convert them into the controller input. The supervised learning procedure and the neural network controller are discussed. The method of obtaining the learning data and training the neural network are described. Experimental results are presented at the end of the article. This work was presented, in part, at the Sixth International Symposium on Artificial Life and Robotics, Tokyo, Japan, January 15–17, 2001     

基于路径跟踪的拖车式移动机器人动态窗口法

针对目前局部路径规划算法只适用于单车体机器人的问题,提出了一种针对拖车式移动机器人的动态窗口法。首先,利用多车体结构的路径跟踪方程实现对拖车式移动机器人的运动控制;然后,利用评价函数同时对牵引车和拖车进行评价并根据权重相加;最后,针对拖车结构特性,添加了运动过程中牵引车与拖车的夹角约束,保证运动轨迹的稳定性。仿真实验表明:拖车式移动机器人的运动控制可满足收敛性,同时所提算法实现了拖车式移动机器人局部路径规划的任务,且在运动过程中夹角变化均未超出限制。该研究对拖车式移动机器人的自主导航有极大的参考价值。     

Linear interpolation based controller design for trajectory tracking under uncertainties: Application to mobile robots

The problem of trajectory tracking control in mobile robots under uncertainties is addressed in this paper. Following the results of mobile robots trajectory tracking reported in (Scaglia et al., 2010), the problem of model errors is focused and the zero convergence of tracking errors under polynomial uncertainties is demonstrated. A simple scheme is obtained, which can be easily implemented. Simulation and experimental results are presented and discussed, showing the good performance of the controller.     

Development of modularized airtight controller for mobile welding robot working in harsh environments

This paper proposes the design and validation of a new hermetic controller for mobile robots working in a hazardous environment. Two years ago, we obtained very successful results with regard to the development of the new mobile robot RRX, which can weld, blast, and paint double-hulled structures during the shipbuilding process. At that time, some attempts were made to design a modular and hermetic controller to secure a robust cooling performance and dustproof quality because the temperature is 40–50 °C during the summer, and such operations produce considerable amounts of metallic dust such as fumes. This naturally represents a very hazardous environment for the robot's controller, for which the temperature should be maintained at its rated level, and the body should be kept fully airtight to prevent the inflow of metallic dust. Thus, in that research, heat pipes were successfully adopted to satisfy these design constraints by dissipating the heat from the servomotor drivers and several power units without any airflow into the controller for cooling. The proposed cooling system is composed of heat pipes, cooling fins, fans, and L-shaped brackets for transferring the produced heat from the heating resources to the heat pipes. Experiments were performed in the field to obtain information on the motor driver's heating value and work site temperatures as boundary conditions of the heat transfer problem, and a modular and hermetic controller for mobile robots working in hazardous environments was successfully developed and validated. The obtained experimental results fully support the idea that this design is appropriate for the controller to maintain a stable performance in a harsh environment.     

Fuzzy logic techniques for mobile robot obstacle avoidance

This paper is concerned with the problem of reactive navigation for a mobile robot in an unknown clustered environment. We will define reactive navigation as a mapping between sensory data and commands. Building a reactive navigation system means providing such a mapping. It can come from a family of predefined functions (like potential fields methods) or it can be built using ‘universal’ approximators (like neural networks). In this paper, we will consider another ‘universal’ approximator: fuzzy logic. We will explain how to choose the rules using a behaviour decomposition approach. It is possible to build a controller working quite well but the classical problems are still there: oscillations and local minima. Finally, we will conclude that learning is necessary for a robust navigation system and fuzzy logic is an easy way to put some initial knowledge in the system to avoid learning from zero.     

A bacterial colony growth algorithm for mobile robot localization

Achieving robot autonomy is a fundamental objective in Mobile Robotics. However in order to realize this goal, a robot must be aware of its location within an environment. Therefore, the localization problem (i.e.,the problem of determining robot pose relative to a map of its environment) must be addressed. This paper proposes a new biology-inspired approach to this problem. It takes advantage of models of species reproduction to provide a suitable framework for maintaining the multi-hypothesis. In addition, various strategies to track robot pose are proposed and investigated through statistical comparisons. The Bacterial Colony Growth Algorithm (BCGA) provides two different levels of modeling: a background level that carries on the multi-hypothesis and a foreground level that identifies the best hypotheses according to an exchangeable strategy. Experiments, carried out on the robot ATRV-Jr manufactured by iRobot, show the effectiveness of the proposed BCGA.

Real-time panospheric image dewarping and presentation for remote mobile robot control

The panospheric camera used by novice drivers in the United States to navigate the Nomad robot across a desert in Chile provides an omnidirectional, ground-to-sky view of the remote robot's environment. This paper describes the texture mapping techniques used to continuously dewarp and display the image for tele-explorers in Pittsburgh, PA and Mountain View, CA.     

Robust tracking control of nonholonomic dynamic systems with application to the bi-steerable mobile robot

A tracking controller for nonholonomic dynamic systems is proposed which allows global tracking of arbitrary reference trajectories and renders the closed loop system robust with respect to bounded disturbances. The controller is based on [Chwa, D. (2004). Sliding-mode tracking control of nonholonomic wheeled mobile robots in polar coordinates. IEEE Transactions on Control Systems Technology, 12(4), 637-644] and shows several generalizations and improvements. The control law for tracking of general nonholonomic systems using inverse kinematic models (IKM) and sliding surfaces is stated. Conditions are proven under which robust tracking is achieved for a specific system. Tracking control is applied to the bi-steerable mobile robot, and simulation results are presented.     

Evolutionary computing based mobile robot localization

Evolutionary computing techniques, including genetic algorithms (GA), particle swarm optimization (PSO) and ants system (AS) are applied to the localization problem of a mobile robot. Salient features of robot localization are that the system is partially dynamic and information for fitness evaluation is incomplete and corrupted by noise. In this research, variations to the above three evolutionary computing techniques are proposed to tackle the specific dynamic and noisy system. Their performances are compared based on simulation and experiment results and the feasibility of the proposed approach to mobile robot localization is demonstrated.     

新的室内移动机器人自定位方法

针对现有室内移动机器人自定位方法中存在的定位精度不高,随时间积累定位误差增大,复杂室内环境下信号存在多径效应和非视距效应等问题,提出了一种基于蒙特卡罗定位(MCL)的新的移动机器人自定位方法。首先,通过分析基于无线射频识别(RFID)技术的移动机器人自定位系统,建立机器人运动模型;然后,通过分析基于接收信号强度指示(RSSI)的移动机器人自定位系统,提出机器人移动过程的观测模型;最后,针对粒子滤波定位执行效率不高的问题,提出粒子剔除策略和依据粒子方位赋予粒子权值策略,提高系统的定位精度和执行效率。仿真实验表明,机器人在移动过程中的自定位误差在X轴和Y轴方向上为3 cm,传统定位算法误差为6cm,新算法定位精度提高近1倍,且算法具有很好的鲁棒性。     

Implementing distributed control system for intelligent mobile robot

The control system of a mobile robot has a number of issues to deal with in real time, including motion control, mapping, localization, path planning, and sensor processing. Intelligent reasoning, task-oriented behaviors, human–robot interfaces, and communications add more tasks to be solved. This naturally leads to a complex hierarchical control system where various tasks have to be processed concurrently. Many low-level tasks can be handled by a robots onboard (host) computer. However, other tasks, such as speech recognition or vision processing, are too computationally intensive for one computer to process. In this case, it is better to consider a distributed design for the control system in networked environments. In order to achieve maximum use of the distributed environment, it is important to design and implement the distributed system and its communication mechanisms in an effective and flexible way. This article describes our approach to designing and implementing a distributed control system for an intelligent mobile robot. We present our implementation of such a distributed control system for a prototype mobile robot. We focus our discussion on the system architecture, distributed communication mechanisms, and distributed robot control.This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

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。     

Reliable distributed data stream management in mobile environments

The proliferation of sensor technology, especially in the context of embedded systems, has brought forward novel types of applications that make use of streams of continuously generated sensor data. Many applications like telemonitoring in healthcare or roadside traffic monitoring and control particularly require data stream management (DSM) to be provided in a distributed, yet reliable way. This is even more important when DSM applications are deployed in a failure-prone distributed setting including resource-limited mobile devices, for instance in applications which aim at remotely monitoring mobile patients. In this paper, we introduce a model for distributed and reliable DSM. The contribution of this paper is threefold. First, in analogy to the SQL isolation levels, we define levels of reliability and describe necessary consistency constraints for distributed DSM that specify the tolerated loss, delay, or re-ordering of data stream elements, respectively. Second, we use this model to design and analyze an algorithm for reliable distributed DSM, namely efficient coordinated operator checkpointing (ECOC). We show that ECOC provides lossless and delay-limited reliable data stream management and thus can be used in critical application domains such as healthcare, where the loss of data stream elements cannot be tolerated. Third, we present detailed performance evaluations of the ECOC algorithm running on mobile, resource-limited devices. In particular, we can show that ECOC provides a high level of reliability while, at the same time, featuring good performance characteristics with moderate resource consumption.     

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