Liam Paull
This work presents an overview of our research on accounting
for robot pose uncertainty in area coverage applications. In the
vast majority of existing literature on robotics area coverage, the
location uncertainty of the robot is not considered. An uncertain
robot pose results in an uncertain sensor swath, which in turn
creates uncertainty about the achieved coverage. Here, we present a
general framework where pose estimates are mapped through the
coverage sensor model to obtain a probability of coverage over the
discretized workspace. This probabilistic representation can then be
used to adaptively plan paths for coverage based on an entropy
reduction formulation.
This framework is particularly well-suited to autonomous
underwater vehicles (AUVs) performing seabed surveying operations.
The AUV position estimate diverges from the actual AUV position
while submerged due to the lack of a global position reference. This
discrepancy can result in parts of the seabed being missed, which is
unacceptable in safety-critical missions
such as mine countermeasures. The proposed information-based path
planning approach is able to guarantee area coverage even in the
case of severe AUV position estimate drift. In-water experiments
with an AUV show the effectiveness of the method.
[C12] Liam Paull,
Mae Seto, Howard Li. "Area coverage that accounts for pose
uncertainty with an AUV surveying application." International
Conference on Robotics and Automation (ICRA). 2014. [pdf]
[bibtex]