Avoiding obstacles - multisensor navigation for nonholonomic robots in cluttered environments

This article introduces a navigation method for nonholonomic (differential drive) vehicles, based on odometry, regularly reset by a vision-based self-localization algorithm, and endowed with a sonar-based obstacle avoidance and guidance control algorithm that does not rely on path planning. The guidance controller is used in the soccer robots of the RoboCup middle-size league (MSL) ISocRob team, fully integrated in the state machine that coordinates task execution. The algorithm can be generally applied to structured indoor environments, provided that visual features can be observed by the self-localization method and that the visual information is not ambiguous.     

RoboCup中型组足球机器人系统关键技术

RoboCup中型组机器人足球比赛是一个研究多自主移动机器人、机器人视觉及其他相关领域的标准测试平台。从机器人机械结构、硬件结构、体系结构、环境感知、行为及策略等方面详细介绍了RoboCup中型组足球机器人系统相关关键技术的研究现状,最后展望了RoboCup中型组足球机器人系统的技术发展趋势。     

一种中型自主足球机器人自定位方法

针对RoboCup中型自主足球机器人比赛中的自定位要求,提出了一种新的自定位方法。利用电子罗盘获取航向角度,根据航向角度将全向视觉获取的白线初始信息转换为白线的半全局信息;利用白线实垂交类型、辅助白线的距离与角度信息,结合定位区域优先度算法确定机器人所在的最终定位区域;根据定位区域内两条实垂交白线的交点位置关系得到机器人在场地中的位置,实现自定位,并讨论该方法对全向视觉观测范围的要求。实际场地的实验结果,验证了该方法的有效性。     

Localization of legged robots combining a fuzzy-Markov method and a population of extended Kalman filters

This paper presents a new approach to robot vision-based self-localization in dynamic and noisy environments for legged robots when efficiency is a strong requirement. The major contribution of this paper is the improvement of a Markovian method based on a fuzzy occupancy grid (FMK). Our proposal combines FMK with a population of Extended Kalman Filters, making the complete algorithm both robust and accurate while keeping its computational cost bounded. Two different strategies have been designed to combine both the methods. They have been tested in the RoboCup environment and quantitatively compared with other approaches in several experiments with the real robot.     

自主移动机器人足球比赛视觉定位方法综述

综述了RoboCup足球赛中全自主移动机器人基于视觉的定位技术,包括机器人自定位和多机器人协作物体定位.介绍了定位技术的发展情况与分类.从机器人环境构建形式的不同以及先验位姿和概率方法的应用与否等方面,系统地分析和比较了各种自定位方法.对于多机器人协作物体定位,阐述了静态方法和动态跟踪方法.总结了定位过程中需要重点研究的传感器模型构建、图像处理、特征匹配以及协作过程涉及的相关问题.最后就视觉定位存在的问题和技术发展趋势进行了讨论.     

Cooperative control through objective achievement

Cooperative control is a key issue for multirobot systems in many practical applications. In this paper, we address the problem of coordinating a set of mobile robots in the RoboCup soccer middle-size league. We show how the coordination problem that we face can be cast as a specific coalition formation problem, and we propose a distributed algorithm to efficiently solve it. Our approach is based on the distributed computation of a measure of satisfaction (called Agent Satisfaction) that each agent computes for each task. We detail how each agent computes the Agent Satisfaction by acquiring sensor perceptions through an omnidirectional vision system, extracting aggregated information from the acquired perception, and integrating such information with that communicated by the teammates. We empirically validate our approach in a simulated scenario and within RoboCup competitions. The experiments in the simulated scenario allow us to analyse the behaviour of the algorithm in different situations, while the use of the algorithm in real competitions validates the applicability of our approach to robotic platforms involved in a dynamic and complex scenario.     

A fast,accurate and robust method for self-localization in polygonal environments using laser range finders

Self-localization is important in almost all robotic tasks. For playing an aesthetic and effective game of robotic soccer, self-localization is a necessary prerequisite. When we designed our robotic soccer team for participating in robotic soccer competitions, it turned out that none of the existing approaches met our requirements of being fast, accurate and robust. For this reason, we developed a new method, which is presented and analyzed in this paper. This method is one of the key components and is probably one of the explanations for the success of our team in national and international competitions. We also present experimental evidence that our method outperforms other self-localization methods in the RoboCup environment.     

Omnidirectional vision scan matching for robot localization in dynamic environments

The localization problem for an autonomous robot moving in a known environment is a well-studied problem which has seen many elegant solutions. Robot localization in a dynamic environment populated by several moving obstacles, however, is still a challenge for research. In this paper, we use an omnidirectional camera mounted on a mobile robot to perform a sort of scan matching. The omnidirectional vision system finds the distances of the closest color transitions in the environment, mimicking the way laser rangefinders detect the closest obstacles. The similarity of our sensor with classical rangefinders allows the use of practically unmodified Monte Carlo algorithms, with the additional advantage of being able to easily detect occlusions caused by moving obstacles. The proposed system was initially implemented in the RoboCup Middle-Size domain, but the experiments we present in this paper prove it to be valid in a general indoor environment with natural color transitions. We present localization experiments both in the RoboCup environment and in an unmodified office environment. In addition, we assessed the robustness of the system to sensor occlusions caused by other moving robots. The localization system runs in real-time on low-cost hardware.     

Fast self-localization method for mobile robots using multiple omnidirectional vision sensors

This paper presents a method for estimating position and orientation of multiple robots from a set of azimuth angles of landmarks and other robots which are observed by multiple omnidirectional vision sensors. Our method simultaneously performs self-localization by each robot and reconstruction of a relative configuration between robots. Even if it is impossible to identify correspondence between each index of the observed azimuth angles and those of the robots, our method can reconstruct not only a relative configuration between robots using `triangle and enumeration constraints' but also an absolute one using the knowledge of landmarks in the environment. In order to show the validity of our method, this method is applied to multiple mobile robots each of which has an omnidirectional vision sensor in simulation and the real environment. The experimental results show that the result of our method is more precise and stabler than that of self-localization by each robot and our method can handle the combinatorial explosion problem. Correspondence to:T. Nakamura (e-mail: ntakayuk@sys.wakayama-u.ac.jp)     

霍夫空间中多足球机器人协作目标定位算法

针对嵌入式仿人足球机器人提出一种霍夫空间中的多机器人协作目标定位算法。机器人利用实验场地中的标志物采用基于三角几何定位方法进行自定位,把机器人多连杆模型进行简化,通过坐标系位姿变换把图像坐标系转换到世界坐标系中,实现机器人目标定位;在多机器人之间建立ZigBee无线传感器网络进行通信,把多个机器人定位的坐标点进行霍夫变换,在霍夫空间中进行最小二乘法线性拟合,获取最优参数,然后融合改进后的粒子滤波实现对目标小球的跟踪;最后在21自由度的仿人足球机器人上进行仿真和实验。数据结果表明,这种多机器人协作的定位算法的精度提高了约48%,在满足实时性的前提下,对目标的跟踪效果也得到了改善。     

RoboCup中型组足球机器人的改进模糊HSIC运动控制

针对RoboCup中型组三轮全向足球机器人路径规划和轨迹跟踪的特点,结合仿人智能控制(human-simulated intelligent control,HSIC)算法,提出了改进模糊仿人智能运动控制算法,并将其运用于该系统的运动控制.实验研究证明,该算法具有控制精度高、稳定性好、能实时逼近期望路径的特点,完全满足RoboCup足球机器人运动控制的要求.另外,模糊控制的控制规则表可以由改进HSIC的逻辑控制规则所确定,使模糊控制器的设计更易于实现.     

Kick it with elasticity: Safety and performance in human–robot soccer

The RoboCup community has one definite goal [H. Kitano, M. Asada, RoboCup humanoid challenge: That’s one small step for a robot, one giant leap for mankind, in: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS1998, Victoria, pp. 419–424, 1998]: winning against the human world soccer champion team by the year 2050. This implies real tackles and fouls between humans and robots, rising safety concerns for the robots and even more important for the human players. Nowadays, similar questions are discussed in the field of physical human–robot interaction (pHRI), but mainly in the context of industrial and service robotics applications.The first part of our paper is an attempt for a pHRI view on human–robot soccer. We take scenes from real soccer matches and discuss what could have happened if one of the teams consisted of robots instead of humans. The most important result is that elastic joints are needed to reduce the impact during collisions. The second and third part consider conversely, how the robot can handle the impact of kicking the ball and how it can reach the velocity of human-level soccer. Again joint elasticity is the key point.Overall, the paper analyzes a vision far ahead. However, all our conclusions are based on concrete simulations, experiments, derivations, or findings from sports science, forensics, and pHRI.     

Dynamic Modeling of a Class of Continuum Manipulators in Fixed Orientation

A novel simultaneous localization and mapping (SLAM) technique based on independent particle filters for landmark mapping and localization for a mobile robot based on a high-frequency (HF)-band radio-frequency identification (RFID) system is proposed in this paper. SLAM is a technique for performing self-localization and map building simultaneously. FastSLAM is a standard landmark-based SLAM method. RFID is a robust identification system with ID tags and readers over wireless communication; further, it is rarely affected by obstacles in the robot area or by lighting conditions. Therefore, RFID is useful for self-localization and mapping for a mobile robot with a reasonable accuracy and sufficient robustness. In this study, multiple HF-band RFID readers are embedded in the bottom of an omnidirectional vehicle, and a large number of tags are installed on the floor. The HF-band RFID tags are used as the landmarks of the environment. We found that FastSLAM is not appropriate for this condition for two reasons. First, the tag detection of the HF-band RFID system does not follow the standard Gaussian distribution, which FastSLAM is supposed to have. Second, FastSLAM does not have a sufficient scalability, which causes its failure to handle a large number of landmarks. Therefore, we propose a novel SLAM method with two independent particle filters to solve these problems. The first particle filter is for self-localization based on Monte Carlo localization. The second particle filter is for landmark mapping. The particle filters are nonparametric so that it can handle the non-Gaussian distribution of the landmark detection. The separation of localization and landmark mapping reduces the computational cost significantly. The proposed method is evaluated in simulated and real environments. The experimental results show that the proposed method has more precise localization and mapping and a lower computational cost than FastSLAM.     

Robust place recognition based on omnidirectional vision and real-time local visual features for mobile robots

Topological localization is especially suitable for human–robot interaction and robot’s high level planning, and it can be realized by visual place recognition. In this paper, bag-of-features, a popular and successful approach in pattern recognition community, is introduced to realize robot topological localization. By combining the real-time local visual features proposed by ourselves for omnidirectional vision and support vector machines, a robust and real-time visual place recognition algorithm based on omnidirectional vision is proposed. The panoramic images from the COLD database were used to perform experiments to determine the best algorithm parameters and the best training condition. The experimental results show that the robot can achieve robust topological localization with high successful rate in real time by using our algorithm.     

Multi-robot cooperative spherical-object tracking in 3D space based on particle filters

This article presents a cooperative approach for tracking a moving spherical object in 3D space by a team of mobile robots equipped with sensors, in a highly dynamic environment. The tracker’s core is a particle filter, modified to handle, within a single unified framework, the problem of complete or partial occlusion for some of the involved mobile sensors, as well as inconsistent estimates in the global frame among sensors, due to observation errors and/or self-localization uncertainty. We present results supporting our approach by applying it to a team of real soccer robots tracking a soccer ball, including comparison with ground truth.     

一种新的用于足球机器人的全向视觉系统

全向视觉系统是RoboCup中型组足球机器人最重要的传感器之一。为了实现足球机器人的目标识别与自定位,提示了一种新的足球机器人全向视觉系统的设计与实现,其中在硬件上设计了一种由水平等比镜面和垂直等比镜面组合而成的新型全向反射镜面,其能够采集到较理想的全景图像;软件上则根据该镜面的成像特性实现了一种新颖的基于场地标志线信息的机器人自定位算法,该算法能够获得较准确的机器人自定位值。实验结果表明,该全向视觉系统能够有效地应用于机器人足球赛中。     

Cooperation without deliberation: A minimal behavior-based approach to multi-robot teams

While terminology and some concepts of behavior-based robotics have become widespread, the central ideas are often lost as researchers try to scale behavior to higher levels of complexity. “Hybrid systems” with model-based strategies that plan in terms of behaviors rather than simple actions have become common for higher-level behavior. We claim that a strict behavior-based approach can scale to higher levels of complexity than many robotics researchers assume, and that the resulting systems are in many cases more efficient and robust than those that rely on “classical AI” deliberative approaches. Our focus is on systems of cooperative autonomous robots in dynamic environments. We will discuss both claims that deliberation and explicit communication are necessary to cooperation and systems that cooperate only through environmental interaction. In this context we introduce three design principles for complex cooperative behavior—minimalism, statelessness and tolerance—and present a RoboCup soccer system that matches the sophistication of many deliberative soccer systems while exceeding their robustness, through the use of strict behavior-based techniques with no explicit communication.     

足球机器人对彩球的鲁棒跟踪识别

在基于视觉的足球机器人系统中,对场上焦点目标——球的动态跟踪识别是系统设计的第一要务。针对半自主微型机器人足球比赛中的小球易受场上干扰、小车遮挡造成的识别丢失问题,提出基于预测与搜索窗的图像目标跟踪识别方法。通过最小二乘法预测丢失小球的可能位置,将图像目标搜索限制在局部小区域内,并利用搜索窗内的在线状态信息加以判断,实现运动目标被遮挡情况下的有效跟踪识别。实验与比赛结果统计表明,该方法实时跟踪识别效果好、鲁棒性强。     

基于多传感器的全向足球机器人自定位

随着对足球机器人智能水平的要求进一步提高,机器人足球委员会将比赛场地的立柱、球门颜色取消.这使以前的基于球门、立柱地标的足球机器人自定位方法失效了.本文提出了一种利用了里程计、罗盘和全景摄像头多种传感器信息的视觉图像特征匹配的足球机器人自定位方法.首先,机器人通过里程计和罗盘取定一个可能位姿.然后,由视觉处理系统把机器人的可能位姿当作变换因子对实时拍摄到的场景图像作旋转、平移变换.最后,将变换后的图像中的白线与参考图像中的白线相比较,选择使图像匹配程度最大的变换因子作为机器人自定位的结果.实验结果表明该自定位方法达到了较高定位精度并能满足比赛的高实时性要求.     

Dynamic omnidirectional vision for mobile robots

Mobile robotic devices hold great promise for a variety of applications in industry. A key step in the design of a mobile robot is to determine the navigation method for mobility control. The purpose of this paper is to describe a new algorithm for omnidirectional vision navigation. A prototype omnidirectional vision system and the implementation of the navigation techniques using this modern sensor and an advanced automatic image processor is described. The significance of this work is in the development of a new and novel approach—dynamic omnidirectional vision for mobile robots and autonomous guided vehicles.