共查询到20条相似文献,搜索用时 31 毫秒
1.
2.
3.
4.
Suat Karakaya Gurkan Kucukyildiz Hasan Ocak 《Journal of Intelligent and Robotic Systems》2017,87(1):125-140
In this study, a wheeled mobile robot navigation toolbox for Matlab is presented. The toolbox includes algorithms for 3D map design, static and dynamic path planning, point stabilization, localization, gap detection and collision avoidance. One can use the toolbox as a test platform for developing custom mobile robot navigation algorithms. The toolbox allows users to insert/remove obstacles to/from the robot’s workspace, upload/save a customized map and configure simulation parameters such as robot size, virtual sensor position, Kalman filter parameters for localization, speed controller and collision avoidance settings. It is possible to simulate data from a virtual laser imaging detection and ranging (LIDAR) sensor providing a map of the mobile robot’s immediate surroundings. Differential drive forward kinematic equations and extended Kalman filter (EKF) based localization scheme is used to determine where the robot will be located at each simulation step. The LIDAR data and the navigation process are visualized on the developed virtual reality interface. During the navigation of the robot, gap detection, dynamic path planning, collision avoidance and point stabilization procedures are implemented. Simulation results prove the efficacy of the algorithms implemented in the toolbox. 相似文献
5.
一种基于特征地图的移动机器人SLAM方案 总被引:1,自引:0,他引:1
设计了一种结构化环境中基于特征地图的地图创建方案;采用激光测距仪进行特征地图创建,利用"聚合-分害虫-聚合"的方法来提取线段表示环境信息实现局部地图创建;为了实现移动机器人的同时定位与地图创建,采用扩展卡尔曼滤波方法对机器人的位姿与地图信息进行预测及更新,结合状态估计和数据关联理论,实验显示x的校正量保持在±0.9cm之内;y的校正量保持在±2.5cm之内;θ的校正量在±1.2之内,实现了基于扩展卡尔曼滤波器的SLAM. 相似文献
6.
移动机器人基于激光测距和单目视觉的室内同时定位和地图构建 总被引:17,自引:1,他引:16
该文研究了部分结构化室内环境中自主移动机器人同时定位和地图构建问题.基于激光和视觉传感器模型的不同,加权最小二乘拟合方法和非局部最大抑制算法被分别用于提取二维水平环境特征和垂直物体边缘.为完成移动机器人在缺少先验地图支持的室内环境中的自主导航任务,该文提出了同时进行扩展卡尔曼滤波定位和构建具有不确定性描述的二维几何地图的具体方法.通过对于SmartROB-2移动机器人平台所获得的实验结果和数据的分析讨论,论证了所提出方法的有效性和实用性. 相似文献
7.
The rotation matrix estimation problem is a keypoint for mobile robot localization, navigation, and control. Based on the quaternion theory and the epipolar geometry, an extended Kalman filter (EKF) algorithm is proposed to estimate the rotation matrix by using a single-axis gyroscope and the image points correspondence from a monocular camera. The experimental results show that the precision of mobile robot s yaw angle estimated by the proposed EKF algorithm is much better than the results given by the image-only and gyroscope-only method, which demonstrates that our method is a preferable way to estimate the rotation for the autonomous mobile robot applications. 相似文献
8.
《Advanced Robotics》2013,27(1-2):179-206
The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm. 相似文献
9.
多传感器信息融合在移动机器人定位中的应用 总被引:8,自引:1,他引:7
机器人自定位是实现自主导航的关键问题之一。为了满足机器人在导航时精确定位的要求,提出一种基于多传感器信息融合的自定位算法。根据对机器人运动机构的分析和运动机构间的刚体约束,建立起机器人的运动学模型;由传感器的工作原理建立里程计和超声波传感器的观测模型;利用扩展卡尔曼滤波(EKF)算法将里程计和超声波传感器采集的数据进行融合;最后,由匹配的环境特征对机器人的位置进行修正,得到精确的位置估计。实验结果表明:该算法明显地消除了里程计的累计误差,有效地提高了定位精度。 相似文献
10.
Localization is a fundamental operation for the navigation of mobile robots. The standard localization algorithms fuse external measurements of the environment with the odometric evolution of the robot pose to obtain its optimal estimation. In this work, we present a different approach to determine the pose using angular measurements discontinuously obtained in time. The presented method is based on an Extended Kalman Filter (EKF) with a state-vector composed of the external angular measurements. This algorithm keeps track of the angles between actual measurements from robot odometric information. This continuous angular estimation allows the consistent use of the triangulation methods to determine the robot pose at any time during its motion. The article reports experimental results that show the localization accuracy obtained by means of the presented approach. These results are compared to the ones obtained applying the EKF algorithm with the standard pose state-vector. For the experiments, an omnidirectional robotic platform with omnidirectional wheels is used. 相似文献
11.
Inexpensive ultrasonic sensors, incremental encoders, and grid-based probabilistic modeling are used for improved robot navigation in indoor environments. For model-building, range data from ultrasonic sensors are constantly sampled and a map is built and updated immediately while the robot is travelling through the workspace. The local world model is based on the concept of an occupancy grid. The world model extracted from the range data is based on the geometric primitive of line segments. For the extraction of these features, methods such as the Hough transform and clustering are utilized. The perceived local world model along with dead-reckoning and ultrasonic sensor data are combined using an extended Kalman filter in a localization scheme to estimate the current position and orientation of the mobile robot, which is subsequently fed to the map-building algorithm. Implementation issues and experimental results with the Nomad 150 mobile robot in a real-world indoor environment (office space) are presented 相似文献
12.
针对移动机器人在未知环境中的导航问题,提出并实现一个新的基于视觉显著区域的拓扑定位系统。首先采用中心—周围差方法在多尺度图像空间中计算颜色及纹理对比,根据检测出的显著线索构造适宜尺寸的显著区域。然后将这些场景中的视觉显著区域利用隐马尔科夫模型组织成为拓扑图中的一个顶点,从而将定位问题转化为隐马尔科夫模型(HMM)的估值问题。该系统支持机器人在线建立环境的拓扑模型,同时进行定位。实验结果表明,该方法能够在机器人移动过程中发生尺度、2维旋转、视角等变化时稳定地检测出显著视觉区域,场景识别率较高。实验证明该定位系统有能力保证机器人在未知环境中的安全导航。 相似文献
13.
A Neuro-Fuzzy Assisted Extended Kalman Filter-Based Approach for Simultaneous Localization and Mapping (SLAM) Problems 总被引:3,自引:0,他引:3
Extended Kalman filter (EKF) has been a popular choice to solve simultaneous localization and mapping (SLAM) problems for mobile robots or vehicles. However, the performance of the EKF depends on the correct a priori knowledge of process and sensor/measurement noise covariance matrices (Q and R, respectively). Imprecise knowledge of these statistics can cause significant degradation in performance. The present paper proposes the development of a new neurofuzzy based adaptive Kalman filtering algorithm for simultaneous localization and mapping of mobile robots or vehicles, which attempts to estimate the elements of the R matrix of the EKF algorithm, at each sampling instant when a ldquomeasurement updaterdquo step is carried out. The neuro-fuzzy based supervision for the EKF algorithm is carried out with the aim of reducing the mismatch between the theoretical and the actual covariance of the innovation sequences. The free parameters of the neuro-fuzzy system are learned offline, by employing particle swarm optimization in the training phase, which configures the training problem as a high-dimensional stochastic optimization problem. By employing a mobile robot to localize and simultaneously acquire the map of the environment, under several benchmark environment situations with varying landmarks and under several conditions of wrong knowledge of sensor statistics, the performance of the proposed scheme has been evaluated. It has been successfully demonstrated that in each case, the neuro-fuzzy assistance is able to improve highly unpredictable, degrading performance of the EKF and can provide robust and accurate solutions. 相似文献
14.
Li Maohai Wang Han Sun Lining Cai Zesu 《Engineering Applications of Artificial Intelligence》2013,26(8):1942-1952
Robust topological navigation strategy for omnidirectional mobile robot using an omnidirectional camera is described. The navigation system is composed of on-line and off-line stages. During the off-line learning stage, the robot performs paths based on motion model about omnidirectional motion structure and records a set of ordered key images from omnidirectional camera. From this sequence a topological map is built based on the probabilistic technique and the loop closure detection algorithm, which can deal with the perceptual aliasing problem in mapping process. Each topological node provides a set of omnidirectional images characterized by geometrical affine and scale invariant keypoints combined with GPU implementation. Given a topological node as a target, the robot navigation mission is a concatenation of topological node subsets. In the on-line navigation stage, the robot hierarchical localizes itself to the most likely node through the robust probability distribution global localization algorithm, and estimates the relative robot pose in topological node with an effective solution to the classical five-point relative pose estimation algorithm. Then the robot is controlled by a vision based control law adapted to omnidirectional cameras to follow the visual path. Experiment results carried out with a real robot in an indoor environment show the performance of the proposed method. 相似文献
15.
Avishek Chatterjee Olive Ray Amitava Chatterjee Anjan Rakshit 《Expert systems with applications》2011,38(7):8266-8274
Developing real-life solutions for implementation of the simultaneous localization and mapping (SLAM) algorithm for mobile robots has been well regarded as a complex problem for quite some time now. Our present work demonstrates a successful real implementation of extended Kalman filter (EKF) based SLAM algorithm for indoor environments, utilizing two web-cam based stereo-vision sensing mechanism. The vision-sensing mechanism is a successful development of a real algorithm for image feature identification in frames grabbed from continuously running videos on two cameras, tracking of these identified features in subsequent frames and incorporation of these landmarks in the map created, utilizing a 3D distance calculation module. The system has been successfully test-run in laboratory environments where the robot is commanded to navigate through some specified waypoints and create a map of its surrounding environment. Our experimentations showed that the estimated positions of the landmarks identified in the map created closely tallies with the actual positions of these landmarks in real-life. 相似文献
16.
组合导航技术是解决地面机器人自主导航的一个有效途径,其中GPS/DR是一种典型的组合方式。常用的卡尔曼滤波主要用于处理线性问题,针对该导航系统非线性的特点,对Unscented卡尔曼滤波(UKF)与分散式滤波技术相结合的方法进行了研究,建立了用于GPS/DR导航系统的联邦UKF算法。数值仿真实验表明,联邦UKF比联邦EKF有更好的滤波精度,同时有更高的稳定性和容错性,是一种理想的GPS/DR导航非线性滤波方法。 相似文献
17.
18.
19.
20.
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。 相似文献