Deliberate Exploration Supports Navigation in Unfamiliar Worlds
This addresses the problem of efficient robot navigation in unknown indoor settings, though it appears incremental as it builds on existing spatial affordance and planning methods.
The paper tackles robot navigation in unfamiliar indoor environments by developing a controller that integrates planning with reactive heuristics based on spatial affordances, and it shows that deliberate exploration for high-level connectivity leads to faster planning and improved travel success in a realistic space.
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.