39 resultados para Unstructured Toys


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传感器是移动机器人认识和了解外部环境的重要途径。在导航过程中,移动机器人要对当前环境进行实时感知和快速理解,并加以识别从而准确避开障碍物。论文提出一种适用于复合机构移动机器人的红外阵列和超声阵列传感器信息采集和处理方法。结合笔者研制的“基于复合机构的非结构环境移动机器人”,给出了系统具体的软、硬件的设计和局部路径规划实现方法。实验验证了该方法的可靠性和有效性。

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基于墙角、房门和通路等高级环境特征的辨识与提取,依据几何和拓扑环境模型完成混合地图的构建,并根据混合地图的特点,提出在局部几何环境采用扩展卡尔曼滤波算法实现移动机器人的位姿跟踪,而在拓扑地图的节点位置则根据绑定的高级环境特征进行位姿再校正的混合定位方法.将该方法应用于实际移动机器人平台,所得结果证明了方法的有效性和实用性.

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冗余度机构具有高度的灵活性和避障能力,利用冗余自由度可以改善仿人机器人手臂的运动性能。以实现人体手臂的运动特性和操作灵活性为目标,研制了一种7DOF冗余仿人机器人手臂,根据冗余手臂的机构学特点,研究了手臂的运动学和动力学性能,并在工作空间、肘部关节的自运动能力以及手臂自重引起的关节重力矩等方面与其它两种典型的冗余手臂构形进行了比较分析。

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针对非结构环境的特点 ,提出一种适用于非结构环境的新型履带腿移动机器人 .这种移动机器人结构简洁 ,实用 ,具有很好的环境适应能力 .本文主要分析该移动机器人在非结构环境下的斜坡行走、越障、跨沟、阶梯地形行走、自动复位等功能 .

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介绍了一类建筑领域等非结构环境下使用的由6个转动关节和4个直动关节组成的轮式机器人,其特殊的结构加上重心控制,能使机器人在不平整地面上行走而不倾倒。绘制了机器人重心控制原理,推导出使机器人不倾倒的重心控制运动方程,给出了实验用的部分软硬件原理框图,并通过实验验证了方案的可行性。

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随着智能机器人系统的发展,机器人的在线感知能力日益受到重视。障碍物检测能力是机器人在线感知能力的一个重要组成部分。因视觉传感器具有独特优势,基于视觉的障碍物检测方法成为目前关注的重点。 室外非结构化环境因结构复杂,机器人缺乏可有效利用的先验知识描述,导致众多障碍物检测系统在该环境中不能有效工作。本文采用全局-局部策略对场景进行由粗到精的分析,弥补室外非结构化环境先验知识不足的难题,提高机器人的在线感知能力。根据该策略,本文在基于视差图的障碍物检测系统框架中,引入视差投影图模块,提出了基于视差投影图的障碍物检测系统框架。该框架在视差投影图模块中全局分析场景视差分布水平,在立体匹配模块中局部分析场景前景目标的几何轮廓信息。依据该框架,针对实际应用中遇到的各种问题,提出了工作于室外非结构化环境的障碍物检测算法。该算法具有如下特点: 1、通过分析视差投影图的地面关联线信息,获得场景的视差分布水平。该信息一方面用来动态更改匹配算法的视差搜索范围,增强算法的实时性和鲁棒性;另一方面用来移除背景地表,简化障碍物分割过程; 2、采用双域滤波抑制噪声,获得清晰的边缘特征,降低立体匹配算法在深度不连续性区域的匹配难度; 3、借助逆向重投影的思想重采样扫描图像,在立体匹配前等效地实现了立体匹配过程中动态变更视差搜索范围的操作; 4、采用基于连通成分的扩散方法替代传统的SAD局部匹配算法,获得高质量的视差图,最终改善障碍物检测的精确性。 在室外非结构化环境中,本文对该算法进行了实验验证。通过设置不同的基线长度,验证了算法在不同的感知距离内的有效性。经实验证明,本算法在一定距离范围内能够有效的检测出障碍物。

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海洋环境通常是不确定、非结构化和未知的 ,而远程AUV(LAUV)的避障声纳对环境感知有一定的局限 ,因此很难建立起精确、完整和统一的三维环境模型 .LAUV实时避障是一个实时性很强的动态过程 ,它不但与环境有关 ,而且还与LAUV的运动学约束、动力学特性和操纵性有关 .针对上述问题本文提出了一种基于复合控制的三维实时模糊避障方法

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This dissertation presents a series of irregular-grid based numerical technique for modeling seismic wave propagation in heterogeneous media. The study involves the generation of the irregular numerical mesh corresponding to the irregular grid scheme, the discretized version of motion equations under the unstructured mesh, and irregular-grid absorbing boundary conditions. The resulting numerical technique has been used in generating the synthetic data sets on the realistic complex geologic models that can examine the migration schemes. The motion equation discretization and modeling are based on Grid Method. The key idea is to use the integral equilibrium principle to replace the operator at each grid in Finite Difference scheme and variational formulation in Finite Element Method. The irregular grids of complex geologic model is generated by the Paving Method, which allow varying grid spacing according to meshing constraints. The grids have great quality at domain boundaries and contain equal quantities of nodes at interfaces, which avoids the interpolation of parameters and variables. The irregular grid absorbing boundary conditions is developed by extending the Perfectly Matched Layer method to the rotated local coordinates. The splitted PML equations of the first-order system is derived by using integral equilibrium principle. The proposed scheme can build PML boundary of arbitrary geometry in the computational domain, avoiding the special treatment at corners in a standard PML method and saving considerable memory and computation cost. The numerical implementation demonstrates the desired qualities of irregular grid based modeling technique. In particular, (1) smaller memory requirements and computational time are needed by changing the grid spacing according to local velocity; (2) Arbitrary surfaces and interface topographies are described accurately, thus removing the artificial reflection resulting from the stair approximation of the curved or dipping interfaces; (3) computational domain is significantly reduced by flexibly building the curved artificial boundaries using the irregular-grid absorbing boundary conditions. The proposed irregular grid approach is apply to reverse time migration as the extrapolation algorithm. It can discretize the smoothed velocity model by irregular grid of variable scale, which contributes to reduce the computation cost. The topography. It can also handle data set of arbitrary topography and no field correction is needed.

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Six experiments tested how headings of objects in scenes influenced the construction for the intrinsic frame of reference under different structure and viewpoint amount conditions. In Experiment 1 and 2, participants stood at 0 degree and learned an asymmetrical scene and a symmetrical scene that were composed by balls with no apparent headings separately. In Experiment 3, 4, 5 and 6, toys with apparent headings were used and they all faced the 315 degree of the scene. In Experiment 3 and 4, participants stood at 0 degree and learned an asymmetrical scene and a symmetrical scene that were composed by toys separately. In Experiment 5 and 6, participants stood at 0 and 315 degree and learned an asymmetrical scene and a symmetrical scene that were composed by toys separately. After learning, participants needed to finish triplet recognition tasks in all the experiments. The dependent measures were response latency and accuracy. The correct response latencies to the targets were analyzed by ANOVA. Accuracy was used to filter data and analyzed in an ANOVA in some experiments as a reference. Results indicate that headings of objects in scenes influence the pattern for intrinsic frame of reference. The structure of scene affects the acting mechanism of heading, but the amount of viewpoints does not have this effect. If the objects in scenes have no apparent headings, there will be viewpoint dependent effect and the advantage of symmetry axis as intrinsic axis in triplet recognition tasks. If the objects in scenes have apparent headings, people’s spatial memory pattern will be affected by objects’ headings. If the heading of objects (315 degree) is not parallel to the viewpoint (0 degree) in an asymmetrical scene, people will be inclined to represent the scene from the heading of objects but not from the viewpoint. As a result, the viewpoint dependent effect will disappear, and there is significant advantage for the triplets presented from heading of objects. If the heading of objects is not parallel to the symmetry axis in a symmetrical scene, people will represent the scene not only according to the symmetry axis as intrinsic axis, but also according to the heading of objects. As a result, the significant advantage for symmetry axis as intrinsic axis in triplet recognition tasks will disappear but there will be still a tendency. By contrast, the effect for the headings of objects is more significant in asymmetrical scenes than that in symmetrical scenes.