Mobile robot defensive wayfinding for incomplete and ambiguous route instructions

Humans typically convey route instructions as a sequence of locations where actions need to be executed, e.g., turn left at the second crossing. It is not uncommon that humans generalize or forget parts of the environment or route instructions. Consequently, route instructions can be incomplete as decision points, in the form of action-location descriptions, are omitted. In addition, omitted location descriptions may be similar to one already present in the route instructions, thus making it ambiguous to the wayfinder at which specific location to execute the action. Defensive wayfinding then characterizes the procedure to deal with such uncertainties in the route instructions. The state of art is not capable of performing defensive wayfinding for mobile robots with route instructions that are incomplete and ambiguous regarding the specific location for action execution. This work tackles this problem by taking inspiration from practices of human wayfinding literature and incorporating it into the robotics context. More in particular by adding three types of knowledge to the route instructions: (1) the types of locations that can be encountered by the robot, (2) the action models to leave those locations, and (3) the temporal and spatial constraints on the (topological) sequence of encountered locations. (In the context of human defensive wayfinding, this additional information is “background knowledge”, or it is provided in the form of a rough sketch.) Our defensive wayfinding approach relies on a hypothesis tree that associates (parts of) the executed robot path to the route instructed path. We experimentally validate defensive wayfinding in simulation with a mobile robot equipped with a 2D laser range finder in a corridor-junction environment that can be topologically inconsistent with the provided route instructions. • Successful mobile robot wayfinding via topologically inconsistent prior knowledge. • Action, perceptual, and topological knowledge to resolve human route instructions. • A multiple hypotheses tree to facilitate defensive wayfinding and error recovery.