Experimental study on autonomous mobile robot acquiring optimal action to avoid moving obstacles

The principal aim of this study was to show how an autonomous mobile robot can acquire the optimal action to avoid moving multiobstacles through interaction with the real world. In this paper, we propose a new architecture using hierarchical fuzzy rules, a fuzzy evaluation system, and learning automata. By using our proposed method, the robot autonomously acquires finely tuned behavior which allows it to move to its goal and avoid moving obstacles by using the steering and velocity control inputs simultaneously. We also show experimental results which confirm the feasibility of our method.     

基于模糊控制的移动机器人避障研究

针对移动机器人在未知环境中的不确定性,利用Matlab构建了多传感器仿真试验移动平台,在Simulink中搭建移动机器人运动学模型,利用多传感器采集环境中的障碍物信息与目标物的方位角,设计了具有避障功能的模糊控制算法.通过模糊控制器控制移动机器人的左右轮速度实现机器人的转弯以及直走,根据机器人实时的角度反馈信息不断修正机器人的位姿以精确避障.仿真实验验证了该方法的可行性及有效性.     

基于障碍物运动预测的移动机器人路径规划

针对移动机器人局部动态避障路径规划问题开展优化研究。基于动态障碍物当前历史位置轨迹,提出动态障碍物运动趋势预测算法。在移动机器人的动态避障路径规划过程中,考虑障碍物当前的位置,评估动态障碍物的移动轨迹;提出改进的D*Lite路径规划算法,大幅提升机器人动态避障算法的效率与安全性。搭建仿真验证环境,给出典型的单动态障碍物、多动态障碍物场景,对比验证了避障路径规划算法的有效性。     

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。     

基于多传感器信息融合的移动机器人避障

为了更好地解决移动机器人在未知环境下的自主避障问题,采用多传感器信息融合的方法,通过多个超声传感器对障碍物信息进行采集。合理确立模糊控制器的输入输出,通过模糊推理将障碍物距离信息模糊化,建立模糊规则并解模糊,以达到对移动机器人的安全避障的控制。通过建立移动机器人运动模型,设计了仿真平台,得到实验结果表明:该算法具有良好的可行性。     

A bayesian approach to real-time obstacle avoidance for a mobile robot

Real-time obstacle avoidance is essential for the safe operation of mobile robots in a dynamically changing environment. This paper investigates how an industrial mobile robot can respond to unexpected static obstacles while following a path planned by a global path planner. The obstacle avoidance problem is formulated using decision theory to determine an optimal response based on inaccurate sensor data. The optimal decision rule minimises the Bayes risk by trading between a sidestep maneuver and backtracking to follow an alternative path. Real-time implementation is emphasised here as part of a framework for real world applications. It has been successfully implemented both in simulation and in reality using a mobile robot.     

两级传感器信息融合的移动机器人避障研究

针对移动机器人的避障问题,以AS-R移动机器人为研究平台,提出了一种将神经网络和模糊神经网络相结合的两级融合方法。采用BP神经网络对多超声波传感器信息进行融合,以减少传感器信息的不确定,提高对障碍物识别的准确率;采用模糊神经网络实现移动机器人的避障决策控制,使之更适合系统的避障要求。该方法使移动机器人在避障中具有较好的灵活性和鲁棒性。机器人避障实验验证了所提方法的有效性。     

Localization of a mobile robot based on an ultrasonic sensor using dynamic obstacles

In this article, we propose a localization scheme for a mobile robot based on the distance between the robot and moving objects. This method combines the distance data obtained from ultrasonic sensors in a mobile robot, and estimates the location of the mobile robot and the moving object. The movement of the object is detected by a combination of data and the object’s estimated position. Then, the mobile robot’s location is derived from the a priori known initial state. We use kinematic modeling that represents the movement of a robot and an object. A Kalman-filtering algorithm is used for addressing estimation error and measurement noise. Throughout the computer simulation experiments, the performance is verified. Finally, the results of experiments are presented and discussed. The proposed approach allows a mobile robot to seek its own position in a weakly structured environment. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles

This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.     

基于遗传算法的移动机器人动态路径规划研究

针对移动机器人未知、动态环境下路径规划的难题,对移动机器人进行了系统设计,采用动态栅格法对环境建模,在对传统遗传算法进行一定的改进的基础上,个体评价函数采取可行路径适应度函数和不可行路径适应度函数分别进行处理,通过算法设计和仿真可知,采用该方法对移动机器人进行动态路径规划时,与任何障碍物不发生碰撞,路径短而且规划曲线平滑,达到了满意的规划效果和收敛速度。     

Navigation Among Moving Obstacles Using the <Emphasis Type="Italic">NLVO</Emphasis>: Principles and Applications to Intelligent Vehicles

Vehicle navigation in dynamic environments is a challenging task, especially when the motion of the obstacles populating the environment is unknown beforehand and is updated at runtime. Traditional motion planning approaches are too slow to be applied in real-time to this problem, whereas reactive navigation methods have generally a too short look-ahead horizon. Recently, iterative planning has emerged as a promising approach, however, it does not explicitly take into account the movements of the obstacles.This paper presents a real-time motion planning approach, based on the concept of the Non-Linear Vobst (NLVO) (Shiller et al., 2001). Given a predicted environment, the NLVO models the set of velocities which lead to collisions with static and moving obstacles, and an estimation of the times-to-collision. At each controller iteration, an iterative A* motion planner evaluates the potential moves of the robot, based on the computed NLVO and the traveling time. Previous search results are reused to both minimize computation and maintain the global coherence of the solutions.We first review the concept of the NLVO, and then present the iterative planner. The planner is then applied to vehicle navigation and demonstrated in a complex traffic scenario.Frédéric Large is a member of the French National Consortium on road automation, namely LaRA (“La Route Automatisée”), in the eMotion (formely known as Sharp) research project-team at INRIA (French National Institute for Research on Computer Science and Control). He holds an engineer degree in artificial intelligence (1998) and a master and his Ph.D. in computer science (2003) from the University of Savoie in the Rhône-Alpes (France). Dr. Large works on modelling and reasonning for mobile service-robots and intelligent vehicles. In particular, he specialises on autonomous navigation in environments shared by moving obstacles whose future behaviour is uncertain.Christian Laugier is Research Director at INRIA (French National Institute for Research on Computer Science and Control), and leader of the robotics project-team at INRIA Rhône-Alpes since 1984. He received the Ph.D. and “State Doctor” degrees in Computer Science from Grenoble University (France) in 1976, and 1987 respectively. His current research interests mainly lies in the areas of Motion Autonomy, Intelligent Vehicles, Decisional Processes, and Virtual Reality. In 1997, he was awarded the Nakamura Prize for his contribution to the advancement of the technology on Intelligent Robots and Systems. Pr. Christian Laugier is a member of several scientific national and international committees (several French scientific committees, Adcom of IROS, Adcom of the EURON European Network, etc.), and he is regularly involved in the organizing committees of the major international conferences in Robotics (e.g. IEEE ICRA and IEEE/RSJ IROS). In addition to his research and teaching activities, he participated in the start-up of four industrial companies in the fields of Robotics, Computer Vision, and Computer Graphics.Zvi Shiller is a Professor, founder and head of the Department of Mechanical Engineering-Mechatronics at the College of Judea and Samaria in Ariel, Israel. He received the B.Sc. (‘76) from Tel Aviv University, and the M.Sc. (‘84) and Sc.D. (‘87) from the Massachusetts Institute of Technology, all in Mechanical Engineering. Before joining the College of Judea and Samaria in 2001, he served fourteen years on the faculty of the Department of Mechanical and Aerospace Engineering at UCLA. Professor Shiller’s current research interests include trajectory planning, multi-robot coordination, navigation of autonomous off-road vehicles and active safety of intelligent road vehicles.     

基于神经网络的多传感器信息融合及其在机器人中的应用

多传感器信息融合即融合多个传感器提供的冗余、互补或更实时的信息,可以获得系统所需的更准确和更精确的信息。介绍了神经网络融合方法,探讨了信息融合技术在机器人方面的应用。机器人避障实验验证了所提方法的有效性。     

智能移动机器人的超声避障研究

智能移动机器人是机器人研究领域的重要方向,是当前机器人领域中最活跃的研究主题之一.在分析了智能移动机器人避障常用传感器的基础上,提出了基于多超声传感器的移动机器人的超声避障系统.介绍了超声避障系统的模糊控制规则和非模糊化,并给出了实验结果.实验结果表明,模糊控制机理和策略易于接受和理解,便于应用开发,模糊避障算法对环境有很大的适应性,机器人在不同的环境条件下实现了避障.     

Coordinated transportation of a single object by two nonholonomic mobile robots

We propose a decentralized control algorithm for transporting a single object by two nonholonomic mobile robots. One of the robots acts as a leader, whose trajectory is planned by itself or defined previously, whereas the other robot, referred to as a follower, follows the leader by keeping a constant distance from the leader. The follower can also avoid obstacles while following the leader without any absolute information about their position. Furthermore, the two mobile robots can realize an omnidirectional motion of the object when the leader broadcasts some simple information to the follower. Some simulation results show a good performance by the proposed decentralized control algorithm. This work was presented, in part, at the Seventh International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002.     

自主机器人非视觉传感器数据采集系统的研制

介绍了一种基于数字信息处理器(DSP)的非视觉传感器数据采集系统。它以DSPTMS320LF2407A为核心,集成了超声波传感器、红外测障传感器、红外测距传感器和方位传感器等模块,通过多传感器信息融合,可以弥补超声波传感器的盲区问题和多次反射问题,本系统通过串口或者通用系列总线(USB)进行通信。经实验验证:该系统结构简单、配置灵活、功耗低,具有较高的可靠性和实时性。     

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.     

未知环境下基于约束点的移动机器人路径规划

针对未知环境中障碍物种类多样性和位置不确定性的特点,提出了基于约束点的路径规划方法。首先对机器人在未知环境中检测到的局部障碍物信息进行分类和几何特征属性描述,得其约束点信息,然后引入改进后的A*算法,将其搜索范围局限于约束点上,计算约束点的评价函数值后得到子目标点,机器人到达子目标点后,若陷入死区,则采取回溯路径策略,重新选择子目标点,否则根据该点所属的障碍物种类采取跨越或绕行避障策略,最后移动机器人在未知环境中顺利到达目标点。仿真研究说明本文提出的路径规划方法具有可行性和有效性。     

基于模糊控制器的机器人路径规划研究

针对复杂环境下移动机器人路径规划实际问题,提出了一种基于行为的移动机器人控制体系结构,设计了一种基于模糊控制器的移动机器人实时路径规划算法,为移动机器人在未知环境中的导航提出了一种新的思路.仿真结果表明,移动机器人能够克服环境中的不确定性,可靠地完成复杂任务,该算法有计算量小,效率高,鲁棒性好等优点.     

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.     

基于对方信息的足球机器人攻防策略研究与设计

通过引入人类足球比赛中的战术思想,对半自主足球机器人系统的攻防策略进行了详细分析和深入研究.提出了在危险区域盯人防守的概念,并详细介绍了该防守角色的具体设计和实现.对于进攻中基于对方信息的避障方法进行了研究,通过对比赛中多种情况的具体分析,重点针对无球避障和带球避障建立了数学模型,根据不同的回避方式分别规划机器人队员的进攻路径,在实际系统中的应用取得了良好效果.