| |
Abstract:
The problem that we address in this paper is how a mobile
robot can plan in order to arrive at its goal with minimum
uncertainty. Traditional motion planning algorithms often assume
that a mobile robot can track its position reliably, however, in
real world situations, reliable localization may not always be
feasible. Partially Observable Markov Decision Processes (POMDPs)
provide one way to maximize the certainty of reaching the goal
state, but at the cost of computational intractability for large
state spaces. The method we propose explicitly models the
uncertainty of the robot's position as a state variable, and
generates trajectories through the augmented pose-uncertainty
space. By minimizing the positional uncertainty at the goal, the
robot reduces the likelihood it becomes lost. We demonstrate
experimentally that coastal navigation reduces the uncertainty at
the goal, especially with degraded localization.
|
