To perform tasks well in a new domain, one must first know something about it. This paper reports on a robot controller for navigation through unfamiliar indoor worlds. Based on spatial affordances, it integrates planning with reactive heuristics. Before it addresses specific targets, however, the system deliberately explores for high-level connectivity and captures that data in a cognitive spatial model. Despite limited exploration time, planning in the resultant model is faster and better supports successful travel in a challenging, realistic space.
翻译:要在新领域很好地执行任务,首先必须了解一些新领域。 本文报告了机器人控制器通过不熟悉的室内世界进行导航的情况。 基于空间条件, 它将规划与反应性超常结合。 然而, 在它处理具体目标之前, 系统有意探索高水平连通性, 并在认知空间模型中捕捉这些数据。 尽管探索时间有限, 但由此得出的模型的规划速度更快, 并更好地支持在一个富有挑战性、 现实的空间中成功旅行 。