Reactive Informative Planning for Mobile Manipulation Tasks under Sensing and Environmental Uncertainty

In this paper we address mobile manipulation
planning problems in the presence of sensing and environmental
uncertainty. In particular, we consider mobile sensing manipulators operating in environments with unknown geometry
and uncertain movable objects, while being responsible for
accomplishing tasks requiring grasping and releasing objects
in a logical fashion. Existing algorithms either do not scale
well or neglect sensing and/or environmental uncertainty. To
face these challenges, we propose a hybrid control architecture,
where a symbolic controller generates high-level manipulation
commands (e.g., grasp an object) based on environmental
feedback, an informative planner designs paths to actively
decrease the uncertainty of objects of interest, and a continuous
reactive controller tracks the sparse waypoints comprising the
informative paths while avoiding a priori unknown obstacles.
The overall architecture can handle environmental and sensing
uncertainty online, as the robot explores its workspace. Using
numerical simulations, we show that the proposed architecture
can handle tasks of increased complexity while responding to
unanticipated adverse configurations.