This article solves the planar navigation problem by recourse to an online reactive scheme that exploits recent advances in simultaneous localization and mapping (SLAM) and visual object recognition to recast prior geometric knowledge in terms of an offline catalog of familiar objects. The resulting vector field planner guarantees convergence to an arbitrarily specified goal, avoiding collisions along the way with fixed but arbitrarily placed instances from the catalog as well as completely unknown fixed obstacles so long as they are strongly convex and well separated. We illustrate the generic robustness properties of such deterministic reactive planners as well as the relatively modest computational cost of this algorithm by supplementing an extensive numerical study with physical implementation on both a wheeled and legged platform in different settings.

本文通过求助于在线反应方案来解决平面导航问题，该方案利用同步定位和映射 (SLAM) 和视觉对象识别方面的最新进展，根据熟悉对象的离线目录重铸先验几何知识。由此产生的向量场规划器保证收敛到任意指定的目标，避免沿途与目录中固定但任意放置的实例以及完全未知的固定障碍物发生碰撞，只要它们是强凸的并且分离良好。