RI: Small: Qualitative Relational Navigation using Minimal Sensing

本研究的目标是开发一种导航方法的机器人使用关系的几何模型，构建和评估使用最小的传感。定性状态的使用对于人类感知是直观的，并且由于传感中固有的可扩展性和简单性而适合于机器人中的长期操作。 技术方法包括四项任务。第一个任务是创建一个支持关系感知/地图/导航的数学表示。第二个任务是扩展的表示和方法，包括不确定性，这使得工作具有实际意义。第三个任务是开发一个基于假设的导航方法，自然使规划最小的，不确定的关系地图和传感器数据。最后，该理论将使用越来越复杂的实验验证测试进行验证。这项研究使机器人在自动驾驶和个人机器人等应用中实现强大的导航，以及在没有GPS访问的非结构化环境中的应用，如探索（水下，行星，洞穴），救灾和搜索和救援。这项研究特别适合于具有传感和计算限制的机器人。从长远来看，由于能够扩展到长期操作，因此可以设想算法和软件能够实现机器人的终身学习。

The goal of this research is to develop a navigation methodology for robots using relational geometric models that are constructed and evaluated using minimal sensing. The use of qualitative states is both intuitive to human perception, and amenable to long-term operations in robotics because of the inherent scalability and simplicity in sensing. The technical approach consists of four tasks. The first task is to create a mathematical representation that supports relational sensing/maps/navigation. The second task is to extend the representation and methods to include uncertainties, which allows the work to be of practical significance. The third task is to develop a hypothesis based navigation methodology that naturally enables planning over minimal, uncertain relational maps and sensor data. Finally, the theory will be validated using an increasingly complex set of experimental validation tests. This research enables robust navigation of robots in applications such as autonomous driving and personal robotics, as well as applications that have unstructured environments with no GPS access, such as exploration (under water, planetary, caves), disaster relief and search and rescue. The research is particularly well suited to robots that have sensing and computational constraints. In the long term, it is envisioned that the algorithms and software enable life-long learning in robotics because of the ability to scale to long-term operations.