An approach to learning mobile robot navigation

This paper describes an approach to learning an indoor robot navigation task through trial-and-error. A mobile robot, equipped with visual, ultrasonic and laser sensors, learns to servo to a designated target object. In less than ten minutes of operation time, the robot is able to navigate to a marked target object in an office environment. The central learning mechanism is the explanation-based neural network learning algorithm (EBNN). EBNN initially learns function purely inductively using neural network representations. With increasing experience, EBNN employs domain knowledge to explain and to analyze training data in order to generalize in a more knowledgeable way. Here EBNN is applied in the context of reinforcement learning, which allows the robot to learn control using dynamic programming.     

Wheeled mobile robot navigation using proportional navigation

We present a method for wheeled mobile robot navigation based on the proportional navigation law. This method integrates the robot's kinematics equations and geometric rules. According to the control strategy, the robot's angular velocity is proportional to the rate of turn of the angle of the line of sight that joins the robot and the goal. We derive a relative kinematics system which models the navigation problem of the robot in polar coordinates. The kinematics model captures the robot path as a function of the control law parameters. It turns out that different paths are obtained for different control parameters. Since the control parameters are real, the number of possible paths is infinite. Results concerning the navigation using our control law are rigorously proven. An extensive simulation confirms our theoretical results.     

Empirical learning in mobile robot navigation

The purpose of this study is to improve the locomotion performance for autonomous mobile robots in outdoor environments. In this paper improvement of an environment model is called empirical locomotion performance leaming. A system avoids wasting time of observations and actions by analyzing data from the last run. We propose a method of empirical learning. The method is expressed by rewriting the rules on the trajectory data. Brief route information for navigating a robot is represented with motion directions at intersections and metric distances between intersections. The behavior of our robot is based on a locomotion strategy 'sign pattern-based stereotyped motion'. The behaviors are implemented on our mobile robot HARUNOBU-4 and tested at our university campus. Experimental results show a robustness of our proposed behaviors under dynamic environments with existing obstacles. Furthermore, they showed that our proposed rewriting rules improved the locomotion performance. In particular, searching time was shortened by 87% (from 453 to 61 s) and the travel distance was shortened by 10% (from 173.8 to 157.5 m).     

Autonomous mobile robot navigation and learning

Research focused on the development and experimental validation of intelligent control techniques for autonomous mobile robots able to plan and perform a variety of assigned tasks in unstructured environments is presented. In particular, an autonomous mobile robot, HERMIES-IIB intelligence experiment series, is described. It is a self-powered, wheel-driven platform containing an onboard 16-node Ncube hypercube parallel processor interfaced to effectors and sensors through a VME-based system containing a Motorola 68020 processor, a phased sonar array, dual manipulator arms, and multiple cameras. Research on navigation and learning is examined     

Positioning and navigation of mobile robot

Positioning tracking is not a new idea as we have been seen from the ability of the GPS (Global Positioning System) to track the position of the object, in general with acceptable accuracy but the cost of GPS installation is expensive. However, in the case of detecting the exact position in signal-blocked closed environment (e.g. inside building, forest, mines and others); the accuracy factor provide by GPS is low. Thus, this project presents a positioning tracking system that is able to track the movement of an object within a small area or inside buildings. A complete set of the positioning tracking system consists of a pair of computer mechanical mouse and a microcontroller. From the position displayed on the computer screen, the position of the object can be located. The pair of mouse detects each movement of the object and sends the movement data to microcontroller. Linear, angular displacement and positioning calculation are also being discussed. From the results, it’s shown that the positioning system is applicable. However, some small errors are also occurred but in acceptable range. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Learning to select distinctive landmarks for mobile robot navigation

In landmark-based navigation systems for mobile robots, sensory perceptions (e.g., laser or sonar scans) are used to identify the robot’s current location or to construct internal representations, maps, of the robot’s environment. Being based on an external frame of reference (which is not subject to incorrigible drift errors such as those occurring in odometry-based systems), landmark-based robot navigation systems are now widely used in mobile robot applications.The problem that has attracted most attention to date in landmark-based navigation research is the question of how to deal with perceptual aliasing, i.e., perceptual ambiguities. In contrast, what constitutes a good landmark, or how to select landmarks for mapping, is still an open research topic. The usual method of landmark selection is to map perceptions at regular intervals, which has the drawback of being inefficient and possibly missing ‘good’ landmarks that lie between sampling points.In this paper, we present an automatic landmark selection algorithm that allows a mobile robot to select conspicuous landmarks from a continuous stream of sensory perceptions, without any pre-installed knowledge or human intervention during the selection process. This algorithm can be used to make mapping mechanisms more efficient and reliable. Experimental results obtained with two different mobile robots in a range of environments are presented and analysed.     

An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation

A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.     

基于行为的轮式移动机器人导航控制

介绍了一种轮式移动机器人CASIA-I及其运动机构,针对该运动机构给出了机器人的运动方程和基于行为的导航控制算法,并根据该算法进行了软件仿真和实物实验．实验结果表明,该导航控制算法是一种有效的导航算法．     

Adaptive behavior navigation of a mobile robot

Describes a neural network model for the reactive behavioral navigation of a mobile robot. From the information received through the sensors the robot can elicit one of several behaviors (e.g., stop, avoid, stroll, wall following), through a competitive neural network. The robot is able to develop a control strategy depending on sensor information and learning operation. Reinforcement learning improves the navigation of the robot by adapting the eligibility of the behaviors and determining the linear and angular robot velocities     

移动机器人导航控制研究的重要进展——评专著《未知环境中移动机器人导航控制理论与方法》

蔡自兴、贺汉根、陈虹等教授的新著《未知环境中移动机器人导航控制理论与方法》已在科学出版社问世,成为《21世纪先进制造技术丛书》的一枝新秀,作为该丛书的主编,我深感荣幸。专著是三位教授和研究组成员,在智能机器人领域多年来辛苦耕耘,取得丰硕成果的基础上,精心凝练写成的,高屋建瓴,叶茂根深。它的出版值得大家庆贺。     

Reactive navigation of underwater mobile robot using ANFIS approach in a manifold manner

Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot’s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system (ANFIS) has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace. An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.     

基于LiDAR/INS的野外移动机器人组合导航方法

移动机器人在地形复杂等野外环境跨区域运动时,机器人运动特性和环境特征变化更为明显,由此引起的点云畸变和特征点稀疏等问题尤为突出,有必要结合传感器标定误差、车轮打滑和车体颠簸等因素进一步改进机器人的位姿估计精度。本文对基于LiDAR/INS的移动机器人环境建模和自主导航方法展开研究,针对LeGO-LOAM等在处理车体姿态快速变化时的性能退化问题,提出一种适用于野外移动机器人运动特性的点云特征分析和多传感融合方法,利用IMU的预积分与LiDAR的scan-to-map构成优化函数,进而迭代更新机器人的位姿。野外环境实验结果表明,当机器人以较高速度做转弯运动或在短时间内多次转向时,本文所提方法仍可以为优化提供良好的初值估计,相比LeGO-LOAM等方法具有更高的位姿估计精度。     

A mobile robot employing insect strategies for navigation

The ability to navigate in a complex environment is crucial for both animals and robots. Many animals use a combination of different strategies to return to significant locations in their environment. For example, the desert ant Cataglyphis is able to explore its desert habitat for hundreds of meters while foraging and return back to its nest precisely and on a straight line. The three main strategies that Cataglyphis is using to accomplish this task are path integration, visual piloting and systematic search. In this study, we use a synthetic methodology to gain additional insights into the navigation behavior of Cataglyphis. Inspired by the insect’s navigation system we have developed mechanisms for path integration and visual piloting that were successfully employed on the mobile robot Sahabot 2. On the one hand, the results obtained from these experiments provide support for the underlying biological models. On the other hand, by taking the parsimonious navigation strategies of insects as a guideline, computationally cheap navigation methods for mobile robots are derived from the insights gained in the experiments.     

基于单目视觉的移动机器人导航算法研究进展

基于单目视觉的移动机器人导航的研究,涵盖了机器视觉、模式识别和多目标跟踪多个领域．其算法框架不仅成功应用于移动机器人导航,还为目标检测、识别与跟踪领域的研究提供了可供参考的模型．该综述将以算法发展历史为脉络,结合一些典型系统,通过对关键技术和算法结构的分析比较,总结算法本身的发展前景和由此发展起来的可供相关研究参考的算法框架．     

Reinforcement based mobile robot navigation in dynamic environment

In this paper, a new approach is developed for solving the problem of mobile robot path planning in an unknown dynamic environment based on Q-learning. Q-learning algorithms have been used widely for solving real world problems, especially in robotics since it has been proved to give reliable and efficient solutions due to its simple and well developed theory. However, most of the researchers who tried to use Q-learning for solving the mobile robot navigation problem dealt with static environments; they avoided using it for dynamic environments because it is a more complex problem that has infinite number of states. This great number of states makes the training for the intelligent agent very difficult. In this paper, the Q-learning algorithm was applied for solving the mobile robot navigation in dynamic environment problem by limiting the number of states based on a new definition for the states space. This has the effect of reducing the size of the Q-table and hence, increasing the speed of the navigation algorithm. The conducted experimental simulation scenarios indicate the strength of the new proposed approach for mobile robot navigation in dynamic environment. The results show that the new approach has a high Hit rate and that the robot succeeded to reach its target in a collision free path in most cases which is the most desirable feature in any navigation algorithm.     

Control architecture for mobile robot operation and navigation

This paper presents the navigation and operation system (NOS) for a multipurpose industrial autonomous mobile robot for both indoor and outdoor environments. This architecture supports task specification in terms of an event-driven state-based machine that provides high quality mission performance in uncertain environments. All processes in the NOS have been integrated in a distributed architecture designed to consider the real-time constraints of each control level of the system. Particular task models obtained from the system requirements specifications are integrated at the highest level of the architecture so that the rest of the levels remain unchanged for a wide range of industrial applications, such as transportation and operation with onboard devices.     

Appearance-based navigation and homing for autonomous mobile robot

In this paper, we develop an algorithm for navigating a mobile robot using the visual potential. The visual potential is computed from an image sequence and optical flow computed from successive images captured by a camera mounted on the robot, that is, the visual potential for navigation is computed from appearances of the workspace observed as an image sequence. The direction to the destination is provided at the initial position of the robot. The robot dynamically selects a local pathway to the destination without collision with obstacles and without any knowledge of the robot workspace. Furthermore, the guidance algorithm to destination allows the mobile robot to return from the destination to the initial position. We present the experimental results of navigation and homing in synthetic and real environments.     

使用单目视觉的移动机器人导航方法

机器视觉与机器人的结合是未来机器人行业发展的一大趋势。在移动机器人的避障导航方案中,使用传统的传感器存在诸多问题,且获取的信息有限。提出一种基于单目视觉的移动机器人导航算法,在算法应用中,如果使用镜头焦距已知的相机,则无需对相机标定。为降低光照对障碍物边缘检测的影响,将机器人拍摄的彩色图像转换到HSI空间。采用canny算法对转换后的分量分别进行边缘检测,并合成检测结果。通过阈值处理过滤合成边缘,去除弱边缘信息,提高检测准确度。采用形态学处理连接杂散边缘,通过区域生长得到非障碍区域,并由几何关系建立图像坐标系与机器人坐标系之间的映射关系。利用结合隶属度函数的模糊逻辑得出机器人控制参数。实验结果表明,对图像颜色空间的转换降低了地面反光、阴影的影响,算法能有效排除地面条纹等的干扰并准确检测出障碍物边缘,而模糊逻辑决策方法提高了算法的鲁棒性和结果的可靠性。     

农业自主行走机器人视觉导航技术研究

对农业自主行走机器人的单目视觉导航技术展开了研究,包括行走路径的提取方法和机器人的自定位方法.对复杂农田场景和道路场景进行了描述和合理的假设,通过对图像信息的处理和理解,采用改进的Hough变换的方法提取出导航路径,并根据导航路径信息对农业自主行走机器人的自定位技术进行了研究,最终求得机器人相对于导航路径的横向偏离和角度偏差.实验结果表明,该方法能够满足农业机器人自主行走的要求.