Multi-Sensor Systems For Robot Navigation In Partially-KnownTerrains

对于几何模型仅在某些区域已知的地形，考虑了使用多传感器系统的导航方法。研究了精确导航算法，以及在牺牲最优性的情况下以较少的计算时间获得近似路径的算法。研究了基于分形插值法和近似路径规划的地形导航方法。导航过程涉及通过适当地组合传感器输出来提取关于地形几何形状的信息的问题。研究了用于(A)提取集成信息和(B)将新信息融合到现有地形模型中的技术。提出了两种新的感知范例：(A)主动感知，其中传感器操作使用顺序统计方法重复；(B)从示例中学习融合规则，其中系统使用经验风险最小化和结构风险最小化的方法与示例进行训练。这两种方法与现有的基于贝叶斯的方法一起，为多传感器系统提供了设计范型。为了计算的目的，提出了一个由一阶命题语句集描述的通用分布式传感器系统。作为特例，该系统包括贝叶斯推理的分布式版本，以及用逻辑和代数进行几何推理。在传感器的输出是实值向量的情况下，整个传感器集成过程被证明是可以使用组合电路和比较器在硬件中实现的。这种实现特别适合于实时应用。此外，还研究了在其他几个计算系统上的实现。

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.