Robot path planning using imprecise and sporadic advisory information from humans

Abstract

In environments featuring hazards (e.g., debris, holes in the ground), robot navigation can be challenging. Robot's sensors alone might be not able to guarantee timely detection of the threats. In such situations, the presence of nearby humans can be exploited to support safe robot navigation. The human can proactively provide advisory information and issue warnings. Unfortunately, verbally expressed human's inputs are usually quite imprecise or ambiguous when referring to spatial elements. We consider how to model the inherently imprecise and sporadic "human sensor" by using the formalism of imprecise probabilities, and how to use the model to build maps fusing robot sensor data and human inputs. Map information is used for path planning, searching for paths that balance survivability and efficiency (e.g., time). In a number of simulation scenarios we study the effectiveness of our approach compared to standard ways to build the map and perform path planning.