RI: Small: Qualitative Relational Navigation using Minimal Sensing

这项研究的目标是开发一种使用关系几何模型的机器人导航方法，这些关系几何模型是使用最小感知构建和评估的。定性状态的使用不仅对人类的感知是直观的，而且由于其固有的可扩展性和简单性而易于在机器人中进行长期操作。技术方法由四项任务组成。第一个任务是创建支持关系感知/映射/导航的数学表示。第二个任务是扩展表示和方法，以包含不确定性，从而使工作具有实际意义。第三个任务是开发一种基于假设的导航方法，该方法自然能够对最小的、不确定的关系地图和传感器数据进行规划。最后，这一理论将通过一系列日益复杂的实验验证测试来验证。这项研究使机器人能够在自动驾驶和个人机器人等应用中进行强大的导航，以及在非结构化环境中无法访问GPS的应用，如探索(水下、行星、洞穴)、救灾以及搜索和救援。这项研究特别适用于具有传感和计算限制的机器人。从长远来看，预计算法和软件将使机器人能够终身学习，因为它们能够适应长期运营的需要。

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.