Multi-Sensor Systems For Robot Navigation In Partially-KnownTerrains

考虑使用多传感器系统的导航方法， 其几何模型仅在某些部分已知的地形。 精确的导航算法，以及那些获得 计算时间更短的近似路径， 最优性进行了研究。 地形的导航方法， 多边形近似是低效的，探索 基于分形插值和近似路径规划。 导航过程涉及到提取 关于地形的几何形状的信息， 组合传感器输出。 （a）提取 （B）将新信息融合成 现有的地形模型进行了研究。 两个新的范例 对于感测：（a）主动感测，其中传感器操作是 使用顺序统计方法重复，以及（B）学习 融合规则的例子，其中系统是训练与 使用经验和结构风险方法的例子 最小化，提出。 这两种方法，加上 现有的基于贝叶斯的方法，提供了设计范例， 多传感器系统。 为了计算的目的，一般 分布式传感器系统由一组一阶 提出了命题句。 该系统包括，如 特殊情况下，分布式版本的贝叶斯推理， 和几何推理与逻辑和代数。 的情况下 输出为实值向量的传感器，整个过程 的传感器集成被证明是可实现的硬件 使用组合电路和比较器。 这 实施方式特别适合于实时 应用. 此外，在其他几个 计算系统进行了研究。

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.