Mobile robot map making using sonar

This article describes a method of producing high-resolution maps of an indoor environment with an autonomous mobile robot equipped with sonar range-finding sensors. This method is based on investigating obstacles in the near vicinity of a mobile robot. The mobile robot examines the straight line segments extracted from the sonar range data describing obstacles near the robot. The mobile robot then moves parallel to the straight line sonar segments, in close proximity to the obstacles, continually applying sonar barrier test. The sonar barrier test exploits the physical constraints of sonar data, and eliminates noisy data. This test determines whether or not a sonar line segment is a true obstacle edge or a false reflection. Low resolution sonar sensors can be used with the method described. The performance of the algorithm is demonstrated using a Denning Corp. Mobile Robot, equipped with a ring of Polaroid Corp. Ultrasonic Rangefinders.     

Novel Sonar Salient Feature Structure for Extended Kalman Filter-Based Simultaneous Localization and Mapping of Mobile Robots

Abstract

Not all line or point features capable of being extracted by sonar sensors from a cluttered home environment are useful for simultaneous localization and mapping (SLAM) of a mobile robot. This is due to unfavorable conditions such as environmental ambiguity and sonar measurement uncertainty. We present a novel sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle feature clouds on salient convex objects by sonar data association called convex saliency circling. The centroid of each circle cloud, called a sonar salient feature, is used as a natural landmark for EKF-based SLAM. By investigating the environmental inherent feature locality, cylindrical objects are augmented conveniently at the weak SLAM-able area as a natural supplementary saliency to achieve consistent SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar salient feature structure for EKF-based SLAM.     

一种基于声纳信息的移动机器人实时导航方法

提出了一种基于声纳信息的移动机器人实时导航方法。首先建立声纳感知数据向地图映射的概率模型,将声纳感知到的环境信息以基于栅格的概率值进行表示,并利用D-S证据理论对其进行数据融合,得到机器人的局部环境。在此基础上,采用基于滚动窗口的方法进行移动机器人路径规划,最终实现实时导航。试验结果表明该方法是可行和有效的。     

Absolute localization for a mobile robot using place cells

This paper describes a method for absolute localization and environment recognition for an autonomous, sonar-equipped robot. The addition of an auto-associative memory to previously developed non-neural map making software results in a system that is capable of recognizing its environment and its position within the environment using remembered features and room geometry. In the prior system the robot used sonar to construct a metric map of an environment, but the map information had to be reconstructed each time the robot returned to an environment. We evaluated the system with a task that requires memory of the position of a goal that is not directly detectable by sonar.     

基于EKF的全景视觉机器人SLAM算法

研究全景视觉机器人同时定位和地图创建（SLAM）问题。针对普通视觉视野狭窄, 对路标的连续跟踪和定位能力差的问题, 提出了一种基于改进的扩展卡尔曼滤波（EKF）算法的全景视觉机器人SLAM方法, 用全景视觉得到机器人周围的环境信息, 然后从这些信息中提取出环境特征, 定位出路标位置, 进而通过EKF算法同步更新机器人位姿和地图库。仿真实验和实体机器人实验结果验证了该算法的准确性和有效性, 且全景视觉比普通视觉定位精度更高。     

Optimal Navigation for a Differential Drive Disc Robot: A Game Against the Polygonal Environment

This paper considers the problem of globally optimal navigation with respect to minimizing Euclidean distance traveled by a disc-shaped, differential-drive robot (DDR) to reach a landmark. The robot is equipped with a gap sensor, which indicates depth discontinuities and allows the robot to move toward them. In this work we assume that a topological representation of the environment called GNT has already been built, and that the landmark has been encoded in the GNT. A motion strategy is presented that optimally navigates the robot to any landmark in the environment, without the need of using a previously known geometric map of the environment. To our knowledge this is the first time that the shortest path for a DDR (underactuated system) is found in the presence of obstacle constraints without knowing the complete geometric representation of the environment. The robot’s planner or navigation strategy is modeled as a Moore Finite State Machine (FSM). This FSM includes a sensor-feedback motion policy. The motion policy is based on the paradigm of avoiding the state estimation to carry out two consecutive mappings, that is, from observation to state and then from state to control, but instead of that, there is a direct mapping from observation to control. Optimality is proved and the method is illustrated in simulation.     

CAD-Vision-Range-Based Self-Localization for Mobile Robot Using One Landmark

Localization is the process of determining the robot's posture within its environment including its current position and heading direction (or orientation). The process is of utmost importance for the autonomous navigational functions of a service robot. This paper describes a new localization method for service robots operating in a building based on a CAD model of the indoor environment in reasonable details. Only one specific landmark pasted within a specific region on the wall is needed. The camera with pan/tilt/zoom functions mounted on the robot first searches for this identification landmark and starts to conduct measurements using a laser rangefinder. With the polar coordinates of few measurement points on the wall and an accurate local CAD model, the exact position and orientation of the robot can be identified. This method has five distinctive advantages. First, the position of the landmark does not need to be precise. Second, each localization exercise is independent and no previous history of the moving track of the robot is required but the computational speed is still high. Third, the method is very robust with good fault-tolerance because it makes use of the reliable Hough transform. Fourth, the resolution is automatically adjusted because the panning resolution of the camera is based on the first effective measurement representing the distance of the robot from the landmark. Fifth, only the local CAD model of the room at the vicinity of the landmark needs a high precision because this model is used for localization. The system does not demand a highly accurate CAD model of the entire built environment. CAD models at other places are for navigation and path planning only.     

Mobile robot relocation from echolocation constraints

This paper presents a method for relocation of a mobile robot using sonar data. The process of determining the pose of a mobile robot with respect to a global reference frame in situations where no a priori estimate of the robot's location is available is cast as a problem of searching for correspondences between measurements and an a priori map of the environment. A physically-based sonar sensor model is used to characterize the geometric constraints provided by echolocation measurements of different types of objects. Individual range returns are used as data features in a constraint-based search to determine the robot's position. A hypothesize and test technique is employed in which positions of the robot are calculated from all possible combinations of two range returns that satisfy the measurement model. The algorithm determines the positions which provide the best match between the range returns and the environment model. The performance of the approach is demonstrated using data from both a single scanning Polaroid sonar and from a ring of Polaroid sonar sensors     

A Probabilistic Framework for Sonar Scan Matching Localization

Scan matching is a popular localization technique based on comparing two sets of range readings gathered at consecutive robot poses. Scan matching algorithms implicitly assume that matching readings correspond to the same object in the environment. This is a reasonable assumption when using accurate sensors such as laser range finders and that is why they are extensively used to perform scan matching localization. However, when using other sensors such as ultrasonic range finders or visual sonar, this assumption is no longer valid because of their lower angular resolution and the sparsity of the readings. In this paper we present a sonar scan matching framework, the spIC, which is able to deal with the sparseness and low angular resolution of sonar sensors. To deal with sparseness, a process to group sonar readings gathered along short robot trajectories is presented. Probabilistic models of ultrasonic and odometric sensors are defined to cope with the low sonar angular resolution. Consequently, a probabilistic scan matching process is performed. Finally, the correction of the whole robot trajectory involved in the matching process is presented as a constrained optimization problem.     

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.     

Application of Heuristic Asymmetric Mapping for mobile robot navigation using ultrasonic sensors

In this paper, an experimental study of a navigation system that allows a mobile robot to travel in an environment about which it has no prior knowledge is described. Data from multiple ultrasonic range sensors are fused into a representation called Heuristic Asymmetric Mapping to deal with the problem of uncertainties in the raw sensory data caused mainly by the transducer's beam-opening angle and specular reflections. It features a fast data-refresh rate to handle a dynamic environment. Potential-field method is used for on-line path planning based on the constructed gridtype sonar map. The mobile robot can therefore learn to find a safe path according to its self-built sonar map. To solve the problem of local minima in conventional potential field method, a new type of potential function is formulated. This new method is simple and fast in execution using the concept from distance-transform path-finding algorithms. The developed navigation system has been tested on our experimental mobile robot to demonstrate its possible application in practical situations. Several interesting simulation and experimental results are presented.This work was supported partly by the National Science Council of Taiwan, ROC under the grant NSC-82-0422-E-009-321.     

一种用于未知环境探索的可动态配置路标系统

研究了移动机器人探索未知环境所面临的定位问题．针对在某些未知环境中难以可靠提取自然路标、 并且存在数据关联难题的实际情况,提出了一种可以根据定位需要进行动态配置的路标系统,设计并实现了可以动 态配置的路标、路标投放装置、路标探测装置,能够在探索未知环境的过程中动态在线配置路标以辅助完成机器人 定位与对环境的探索．可动态配置路标具有成本低、体积小、能耗低的特点,便于携带与动态配置,实验结果证明 了这种路标系统在未知环境探索中的有效性．     

Optimal landmark selection for triangulation of robot position

A mobile robot can identify its own position relative to a global environment model by using triangulation based on three landmarks in the environment. It is shown that this procedure may be very sensitive to noise depending on spatial landmark configuration, and relative position between robot and landmarks. A general analysis is presented which permits prediction of the uncertainty in the triangulated position.

In addition an algorithm is presented for automatic selection of optimal landmarks. This algorithm enables a robot to continuously base its position computation on the set of available landmarks, which provides the least noise sensitive position estimate. It is demonstrated that using this algorithm can result in more than one order of magnitude reduction in uncertainty.     

Sonar Sensor-Based Efficient Exploration Method Using Sonar Salient Features and Several Gains

This paper describes a sonar sensor-based exploration method. To build an accurate map in an unknown environment during exploration, a simultaneous localization and mapping problem must be solved. Therefore, a new type of sonar feature called a ??sonar salient feature?? (SS-feature), is proposed for robust data association. The key concept of an SS-feature is to extract circle feature clouds on salient convex objects from environments by associating sets of sonar data. The SS-feature is used as an observation in the extended Kalman filter (EKF)-based SLAM framework. A suitable strategy is needed to efficiently explore the environment. We used utilities of driving cost, expected information about an unknown area, and localization quality. Through this strategy, the exploration method can greatly reduce behavior that leads a robot to explore a previously visited place, and thus shorten the exploration distance. A robot can select a favorable path for localization by localization gain during exploration. Thus, the robot can estimate its pose more robustly than other methods that do not consider localizability during exploration. This proposed exploration method was verified by various experiments, and it ensures that a robot can build an accurate map fully autonomously with sonar sensors in various home environments.     

基于场景图谱的室内移动机器人目标搜索

在移动机器人执行日常家庭任务时,首先需要其能够在环境中避开障碍物,自主地寻找到房间中的物体。针对移动机器人如何有效在室内环境下对目标物体进行搜索的问题,提出了一种基于场景图谱的室内移动机器人目标搜索,其框架结合了导航地图、语义地图和语义关系图谱。在导航地图的基础上建立了包含地标物体位置信息的语义地图,机器人可以轻松对地标物体进行寻找。对于动态的物体,机器人根据语义关系图中物体之间的并发关系,优先到关系强度比较高的地标物体旁寻找。通过物理实验展示了机器人在语义地图和语义关系图的帮助下可以实现在室内环境下有效地寻找到目标,并显著地减少了搜索的路径长度,证明了该方法的有效性。     

全局环境未知时机器人导航和避障的一种新方法

研究了全局环境未知情况下的移动机器人实时导航问题．将栅格法描述环境与基于滚动窗口的路径规划相结合，提出了一种新的移动机器人导航方法．将超声传感阵列探测到的环境信息以基于栅格的概率值进行表示，利用不确定性证据推理对其进行数据融合，得到机器人的局部环境信息；在此基础上，采用基于滚动窗口的方法进行机器人路径规划，实现机器人的实时导航．仿真与实验结果表明了该方法的有效性．     

Robot docking based on omnidirectional vision and reinforcement learning

We present a system for visual robotic docking using an omnidirectional camera coupled with the actor critic reinforcement learning algorithm. The system enables a PeopleBot robot to locate and approach a table so that it can pick an object from it using the pan-tilt camera mounted on the robot. We use a staged approach to solve this problem as there are distinct subtasks and different sensors used. Starting with random wandering of the robot until the table is located via a landmark, then a network trained via reinforcement allows the robot to turn to and approach the table. Once at the table the robot is to pick the object from it. We argue that our approach has a lot of potential allowing the learning of robot control for navigation and remove the need for internal maps of the environment. This is achieved by allowing the robot to learn couplings between motor actions and the position of a landmark.     

基于曲率和的主动声呐干涉条纹特征表征方法

干涉条纹特征是主动声呐目标识别所依据的重要特征之一。在浅水域环境中,声波能量传播特性较为复杂,信号会经过发射源到目标以及目标到接收器这2条不同的路径,目标的散射特性在声波的入射模式和散射模式之间相互作用,使得主动声呐干涉条纹呈现弯曲的结构。为对干涉条纹特征进行表征,提出一种基于曲率和的声呐特征提取方法。根据浅海声场主动声呐频率-距离干涉条纹图得到一段区间内的干涉条纹曲线散点曲率值,利用曲率求和的方法刻画条纹的弯曲程度,进而表征这一区间内的条纹特征。在理想波导环境下计算刚性球体目标在不同频率段下的曲率特征,通过统计平均值的方式得到平均曲率和为0.6左右,从特征表征结果可以看出,曲率和可通过统计离散点曲率强度描述干涉条纹特征,且在同一环境条件下,发射频率越大时曲率和强度越小。根据实测数据进行计算得到曲率和为0.512,实验与理论结果较为吻合,表明通过该方法可以获取主动声呐干涉条纹特征,进而实现目标识别和探测。     

Workspace mapping based on multisensor information fusion using heterogeneous onboard sensors

In recent years, multiple robot systems that perform team operations have been developed. These robot systems are expected to execute complicated tasks smoothly in a given congested workspace. In this article, we propose a workspace mapping algorithm using ultrasonic stereo sonar and an image sensor in order to operate the mobile robots among obstacles. This workspace mapping algorithm involves two steps: (1) the position detection of obstacles using ultrasonic stereo sonar, and (2) the shape detection of obstacles using an image sensor. While each robot moves around in the given workspace, the two steps of the mapping algorithm are repeated and sensor data are collected. The robot measures the distance and the direction of obstacles using ultrasonic stereo sonar. The shape of obstacles is also captured using an onboard image sensor. A workspace map is created based on the sensor data accumulated from the proposed method, and successful results are also obtained through experiments.