  When performing visual servoing or object tracking tasks, active sensor
planning is essential to keep targets in sight or to relocate them when
missing. In particular, when dealing with a known target missing from the
sensor's field of view, we propose using prior knowledge related to contextual
information to estimate its possible location. To this end, this study proposes
a Dynamic Bayesian Network that uses contextual information to effectively
search for targets. Monte Carlo particle filtering is employed to approximate
the posterior probability of the target's state, from which uncertainty is
defined. We define the robot's utility function via information-theoretic
formalism as seeking the optimal action which reduces uncertainty of a task,
prompting robot agents to investigate the location where the target most likely
might exist. Using a context state model, we design the agent's high-level
decision framework using a Partially-Observable Markov Decision Process. Based
on the estimated belief state of the context via sequential observations, the
robot's navigation actions are determined to conduct exploratory and detection
tasks. By using this multi-modal context model, our agent can effectively
handle basic dynamic events, such as obstruction of targets or their absence
from the field of view. We implement and demonstrate these capabilities on a
mobile robot in real-time.
