Reactive Navigation in Partially Familiar Planar Environments Using Semantic Perceptual Feedback
This work addresses navigation in partially familiar environments for robotic platforms, representing an incremental improvement by combining existing SLAM and object recognition techniques with a reactive planning approach.
This paper tackles planar navigation by using semantic perceptual feedback to incorporate prior knowledge of familiar objects into a reactive vector field planner, which guarantees convergence to the goal while avoiding both known and unknown obstacles. The method was validated through extensive numerical studies and physical implementations on wheeled and legged platforms, demonstrating robustness and modest computational cost.
This paper solves the planar navigation problem by recourse to an online reactive scheme that exploits recent advances in SLAM and visual object recognition to recast prior geometric knowledge in terms of an offline catalogue 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 catalogue 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.