The Spatial Semantic Hierarchy (SSH) provides a hierarchy of
abstractions for reasoning about large-scale space. It assumes that
the large-scale environments in which the agent operates have a
certain structure, specifically that the environments can be described
as collections of places connected by paths. The Hybrid Spatial
Semantic Hierarchy (HSSH) is a more specific framework of spatial
knowledge that differs from the SSH in several key ways---most
importantly, it incorporates knowledge of small-scale space.
The HSSH allows a robot to describe the world using qualitatively
different representations, each with its own ontology. The hierarchy
of connected representations is useful for the many tasks of
navigation: safe motion, localization, map-building, and route
planning. Equally important, since the multiple representations are
motivated by human cognitive abilities, they provide a "natural" way
for a robot to interact with a human.
To solve the mapping problem, an autonomous robot explores an unknown
environment and uses its own observations to build a useful map. Maps
have a variety of different uses, including route planning, local
motion control with hazard avoidance, estimating distances and
directions, localization, and place recognition. Although important
progress has been made on the SLAM (simultaneous localization and
mapping) problem within a single global frame of reference, metrical
uncertainty can still accumulate over time, making it difficult to
close large loops with confidence.
In recent work to address this problem, we have revised the basic
Spatial Semantic Hierarchy (SSH) to become the Hybrid SSH, by defining
a clean interface to the local perceptual map. We exploit the
strengths of three different map representations to factor the mapping
problem into three distinct sub-problems that can be solved reliably:
local metrical mapping in the scrolling local perceptual map; global
topological mapping given the local metrical maps, generating a tree
of all possible maps to resolve structural ambiguities; a global
metrical map, given the local metrical and global topological
maps.
By factoring the problem in this way, we can build an accurate
global metrical map on the skeleton provided by the accurate global
topological map. The factored problem also leads to a robust and
useful map: the local metrical map is useful for place recognition and
local motion control with hazard avoidance; the global topological map
is useful for localization and route planning; and the global metrical
map is useful for estimating distances and directions.
| The Intelligent Wheelchair |
Smart wheelchairs that can detect and avoid obstacles have been
developed with the goal of serving as mobility aids for persons with
disabilities who find standard power wheelchairs difficult or
impossible to use. By improving mobility and autonomy, they have the
potential to improve the health of populations ranging from the
severely disabled to the growing numbers of aging people.
We propose to create and evaluate an Intelligent Wheelchair that
represents a qualitative increase in capability, based on
state-of-the-art methods for robot exploration, map-building,
navigation, and direction following. The Intelligent Wheelchair acts
under the direction of its human driver, but it is also an intelligent
robot, sensing its local surroundings and maintaining a .cognitive
map. of its environment. The proposed research is driven by the
structure and requirements of the human-robot interface by which the
human driver instructs the Intelligent Wheelchair where it should go.