Multi-Sensor Systems For Robot Navigation In Partially-KnownTerrains

用于部分已知地形机器人导航的多传感器系统

• 批准号: 9108610
• 负责人: Nageswara Rao
• 金额: $ 7.4万
• 依托单位: Old Dominion University Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-06-15 至 1993-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9108610&HistoricalAwards=false
• 关键词: Multi; Sensor; Systems; Robot; Navigation

Navigation methods using multi-sensor systems are considered for terrains whose geometric models are known only in certain parts. Exact navigation algorithms, as well as those that obtain approximate paths with lesser computation time while compromising the optimality are studied. Navigational methods for terrains, where polygonal approximations are inefficient, are explored based on fractal interpolation and approximate path planning. The navigation process involves the problem of extracting information about the geometry of the terrain by suitably combining the sensor outputs. Techniques for (a) extracting integrated information, and (b) fusing the new information into the existing terrain model, are investigated. Two new paradigms for sensing: (a) active sensing where sensor operations are repeated using sequential statistical methods, and (b) learning fusion rules from examples where the system is trained with examples using the methods of empirical and structural risk minimization, are proposed. These two methods, together with the existing Bayesian-based methods, provide the design paradigms for the multi-sensor system. For computational purposes, a general distributed sensor system specified by a set of first order propositional sentences is proposed. This system encompasses, as special cases, the distributed versions of Bayesian inference, and geometric reasoning with logic and algebra. In the case of sensors whose outputs are real-valued vectors, the entire process of sensor integration is shown to be implementable in hardware using combinational circuits and comparators. This implementation is particularly suitable for real-time applications. Also implementations on several other computational systems are studied.

使用多传感器系统的导航方法被考虑用于仅在某些部分已知几何模型的地形。 研究了精确导航算法，以及那些在牺牲最优性的情况下以更少的计算时间获得近似路径的算法。 基于分形插值和近似路径规划，探索了多边形近似效率低下的地形导航方法。 导航过程涉及通过适当组合传感器输出来提取有关地形几何形状的信息的问题。 研究了（a）提取综合信息和（b）将新信息融合到现有地形模型中的技术。 提出了两种新的传感范式：（a）主动传感，其中使用顺序统计方法重复传感器操作；（b）从示例中学习融合规则，其中系统使用经验和结构风险最小化方法进行示例训练。 这两种方法与现有的基于贝叶斯的方法一起提供了多传感器系统的设计范例。 出于计算目的，提出了由一组一阶命题句指定的通用分布式传感器系统。 作为特殊情况，该系统包含贝叶斯推理的分布式版本以及逻辑和代数的几何推理。 对于输出为实值矢量的传感器，传感器集成的整个过程可以使用组合电路和比较器在硬件中实现。 这种实现特别适合实时应用。 还研究了其他几种计算系统上的实现。