POMDP Planning for Object Search in Partially Unknown Environment

Efficiently searching for target objects in complex environments that contain
various types of furniture, such as shelves, tables, and beds, is crucial for
mobile robots, but it poses significant challenges due to various factors
such as localization errors, limited field of view, and visual occlusion. To
address this problem, we propose a Partially Observable Markov Decision
Process (POMDP) formulation with a growing state space for object search
in a 3D region. We solve this POMDP by carefully designing a perception
module and developing a planning algorithm, called Growing Partially
Observable Monte-Carlo Planning (GPOMCP), based on online MonteCarlo tree search and belief tree reuse with a novel upper confidence bound.
We have demonstrated that belief tree reuse is reasonable and achieves
good performance when the belief differences are limited. Additionally, we
introduce a guessed target object with an updating grid world to guide the
search in the information-less and reward-less cases, like the absence of any
detected objects. We tested our approach using Gazebo simulations on four
scenarios of target finding in a realistic indoor living environment with the
Fetch robot simulator. Compared to the baseline approaches, which are
based on POMCP, our results indicate that our approach enables the robot
to find the target object with a higher success rate faster while using the
same computational requirements