基于灰色定性理论的移动机器人地图创建

针对移动机器人自主导航地图创建中超声波信息存在不确定性的问题,提出一种新的基于灰色定性理论的超声波信息解释和融合的方法,并用于处理超声波传感器信息和移动机器人创建环境地图．首先,引入概率灰数对超声波信息的不确定性进行描述,以获得栅格单元和传感器的概率灰数模型;然后,设计超声波传感器新旧信息的融合方法,从而得到环境地图的整体表示;最后通过地图创建仿真实验结果表明了这种方法具有良好的鲁棒性和准确度．

     

一种基于声纳信息的地图创建方法

地图创建是实现机器人在未知环境中自主导航的关键。该文对移动机器人在地图创建中所收集的不确定传感信息进行研究,分析声纳传感器的散射和镜面反射特性,提出一种改进的概率栅格的地图创建方法。该方法将距离信任因子引入到声纳传感器模型。利用该模型,实现移动机器人的自主地图创建,并有效地减少由于声纳传感器所引起的不确定性。通过机器人平台上进行的实验表明该方法的有 效性。     

基于主动探测的室内未知环境下移动机器人地图创建算法

主要研究室内未知环境下移动机器人基于超声波传感器的地图创建问题.针对超声波传感器角分辨率低、可能发生镜面反射等固有缺陷,通过分析相邻传感器信息间的相互关系建立精确的局部环境模型,并以此为基础提出一种用于地图创建的主动探测策略.利用移动机器人在室内环境下进行地图创建实验,并将实验结果与其他常用的基于超声波传感器的地图创建算法进行比较.实验结果表明,基于主动探测策略的地图创建算法能够克服超声波传感器的固有缺陷,以较为准确、高效的方式建立室内环境的完整地图.     

一种融合激光和深度视觉传感器的SLAM地图创建方法

针对移动机器人的不确定复杂环境,一般采用单一传感器进行同时定位和地图创建(SLAM)存在精度较低,并且易受干扰,可靠性不足等问题,本文提出一种基于Bayes方法的激光传感器和RGB-D传感器的信息融合SLAM方法,利用Bayes方法通过概率启发式模型提取光束投影到栅格地图单元,充分利用激光与视觉信息中的冗余信息,提取一致性特征信息,并进行特征级的信息融合;在地图更新阶段,本文提出一种融合激光传感器和视觉传感器的贝叶斯估计方法,对栅格地图进行更新。在使用ROS(移动机器人操作系统)的实验平台上实验表明,多传器信息融合可以有效提高SLAM的准确度和鲁棒性。     

移动机器人地图创建中的不确定传感信息处理

该文研究移动机器人自主创建地图中的不确定传感信息处理问题,基于灰色系统理论 提出了一种新的对传感信息进行解释和融合的方法用于声纳信息的处理,并以此建立环境的栅 格地图.声纳的传感信息存在较大的不确定性,这里引入灰数的概念来表示和处理这种不确定 性,对于机器人在不同位置的测量结果,根据灰色系统理论对信息的理解方式设计融合方法,得 到一个对环境的整体表示.通过仿真环境和真实机器人平台上进行的创建地图实验,表明这种 方法具有良好的鲁棒性和准确度.     

基于不确定网格地图的移动机器人导航

研究了在未知环境下的移动机器人导航问题．在分析超声传感器不确定性模型的基础上,根据模糊集理论创建网格地图来描述机器人工作环境,使用模糊隶属度表示网格占用状态．通过网格信息融合来减弱传感器测量误差,提高网格地图的精度．提出基于模糊网格地图的路径规划算法,利用重复局部优化路径搜索来实现全局路径规划．机器人通过交替进行创建地图和路径规划两个基本过程来完成导航任务．仿真结果表明创建的地图能较精确地表示环境信息。规划的路径可以使机器人安全地到达目的地．     

基于超声波的移动机器人的同时定位和地图构建

为了在移动机器人SLAM过程中得到更精确的定位和二维地图构建,对一种利用超声波传感器信息进行栅格地图创建的方法提出了改进;该方法利用Bayes法则对信息进行融合,利用粒子滤波和航位推算相结合的方法对机器人进行精确定位和创建地图,然后利用移动的栅格法进行地图的全局更新,提出了一种地图的校验方法;通过实验,在粒子数为200的情况下分别得到了算法改进前和改进后的地图构建结果,通过比较,证明了使用该算法进行移动机器人定位和地图构建更加精确。     

基于声纳传感器的移动机器人地图创建方法研究*

提出了一种改进的基于声纳传感器信息进行栅格地图创建的方法。将Bayes法则用于移动机器人地图创建,对多个声纳传感器信息进行融合,解决信息间的冲突问题,并根据声纳模型将测量数据集成到局部地图中,改变栅格被障碍物占有的概率。经过坐标变换后,利用Bayes法则更新全局地图中的栅格信息,实现从局部地图到全局地图的更新。实验验证了该算法的可行性与有效性。     

基于声纳的移动机器人环境建图的设计与实现*

针对未知环境中声纳传感器定位与地图创建时传感数据不确定性高、可靠性低的问题,提出了一种新的室内环境建图方法。该方法建立容忍函数以判断噪声和镜面反射,同时借鉴了ArcTransversal Median Algorithm的思想和栅格概率估计并采用贝叶斯法则进行两次数据融合以减小声纳传感器信息的不确定性。在MORCS2机器人平台上实时创建地图实验表明,这种方法能快速实现从局部地图到全局地图的更新且有较好的精确性与鲁棒性。     

基于激光测距与双目视觉信息融合的移动机器人SLAM研究

未知复杂环境中不规则的障碍物使传感数据具有不确定性,单依靠激光测距仪进行移动机器人的定位与自主导航可靠性不足;针对此问题,提出了一种基于激光测距仪和双目视觉传感器信息融合的精确定位方法,利用加权最小二乘拟合方法和尺度不变特征变换(SIFT)算法分别从对激光信息与视觉信息中提取直线和点特征,进行特征级的信息融合。通过对实验结果和数据分析,多传感器信息融合可以有效提高移动机器人SLAM(即时定位与地图构建)的精度和鲁棒性。     

Improved occupancy grid mapping in specular environment

This paper addresses the improved method for sonar sensor modeling which reduces the specular reflection uncertainty in the occupancy grid. Such uncertainty reduction is often required in the occupancy grid mapping where the false sensory information can lead to poor performance. Here, a novel algorithm is proposed which is capable of discarding the unreliable sonar sensor information generated due to specular reflection. Further, the inconsistency estimation in sonar measurement has been evaluated and eliminated by fuzzy rules based model. To achieve the grid map with improved accuracy, the sonar information is further updated by using a Bayesian approach. In this paper the approach is experimented for the office environment and the model is used for grid mapping. The experimental results show 6.6% improvement in the global grid map and it is also found that the proposed approach is consuming nearly 16.5% less computation time as compared to the conventional approach of occupancy grid mapping for the indoor environments.     

Occupancy grids building by sonar and mobile robot

In this paper, a modified method for occupancy grid map building by a moving mobile robot and a scanning ultrasonic range-finder is proposed. The map building process consists of two phases: (1) gleaning of information from environment, and (2) sonar data processing. For sonar data processing the proposed modified method combines: (1) statistical approach for probability sonar model building; and (2) application of fuzzy logic theory for sonar data fusion. It is experimentally shown that, in some applications, the proposed modified method has advantages over other well-known methods.     

Autonomous topological modeling of a home environment and topological localization using a sonar grid map

This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.     

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。     

Sonar mapping of a mobile robot considering position uncertainty

This paper suggests a new sonar mapping method considering the position uncertainty of a mobile robot. Sonar mapping is used for recognizing the unknown environment for a mobile robot during navigation. Usually accumulated position error of a mobile robot causes considerable deterioration of the quality of a constructed map. In this paper, therefore, a new Bayesian probability map construction method is proposed, which considers estimation of the position error of a mobile robot. In this method, we applied approximation transformation theory to estimate the position uncertainty of a real mobile robot, and introduced cell ordering uncertainty caused by the position uncertainty of a robot in cell-based map updating. Through simulation we showed the effect of a robot's position uncertainty on the quality of a reconstructed map. Also, the developed methods were implemented on a real mobile robot, AMROYS-II, which was built in our laboratory and shown to be useful enough in a real environment.     

Enhancement of Probabilistic Grid-based Map for Mobile Robot Applications

In this paper, a novel approach for fine-tuning of the grid-based map-building algorithm is reported. The traditional occupancy grid-based map-building algorithm uses a fixed probability distribution function of the sonar readings and disregards the information from the environment. In our approach, the probability distribution function is tuned by fuzzy rules formed from the information obtained from the environment at each sonar data scan. A Bayesian update rule is then used to update the occupancy probabilities of the grid cells. The proposed map-building algorithm is compared with other grid-based map-building methods through simulations and experiments. The simulation and experimental studies suggest that sharp grid maps can be obtained by incorporating fuzzy rules during the grid-based map generation. In comparison with other algorithms, improved convergence has also been noted.     

Effective Maximum Likelihood Grid Map With Conflict Evaluation Filter Using Sonar Sensors

In this paper, we address the problem of building a grid map using cheap sonar sensors, i.e., the problem of using erroneous sensors when seeking to model an environment as accurately as possible. We rely on the inconsistency of information among sonar measurements and the sound pressure of the waves from the sonar sensors to develop a new method of detecting incorrect sonar readings, which is called the conflict evaluation with sound pressure (CEsp). To fuse the correct measurements into a map, we start with the maximum likelihood (ML) approach due to its ability to manage the angular uncertainty of sonar sensors. However, since this approach suffers from heavy computational complexity, we convert it to a light logic problem called the maximum approximated likelihood (MAL) approach. Integrating the MAL approach with the CEsp method results in the conflict evaluated maximum approximated likelihood (CEMAL) approach. The CEMAL approach generates a very accurate map that is close to the map that would be built by accurate laser sensors and does not require adjustment of parameters for various environments.