  Assisting individuals in their daily activities through autonomous mobile
robots, especially for users without specialized knowledge, is crucial.
Specifically, the capability of robots to navigate to destinations based on
human speech instructions is essential. While robots can take different paths
to the same goal, the shortest path is not always the best. A preferred
approach is to accommodate waypoint specifications flexibly, planning an
improved alternative path, even with detours. Additionally, robots require
real-time inference capabilities. This study aimed to realize a hierarchical
spatial representation using a topometric semantic map and path planning with
speech instructions, including waypoints. This paper presents Spatial
Concept-based Topometric Semantic Mapping for Hierarchical Path Planning
(SpCoTMHP), integrating place connectivity. This approach offers a novel
integrated probabilistic generative model and fast approximate inference across
hierarchy levels. A formulation based on control as probabilistic inference
theoretically supports the proposed path planning algorithm. We conducted
experiments in home environments using the Toyota Human Support Robot on the
SIGVerse simulator and in a lab-office environment with the real robot, Albert.
Users issued speech commands specifying the waypoint and goal, such as "Go to
the bedroom via the corridor." Navigation experiments using speech instructions
with a waypoint demonstrated a performance improvement of SpCoTMHP over the
baseline hierarchical path planning method with heuristic path costs (HPP-I),
in terms of the weighted success rate at which the robot reaches the closest
target and passes the correct waypoints, by 0.590. The computation time was
significantly accelerated by 7.14 seconds with SpCoTMHP compared to baseline
HPP-I in advanced tasks.
