  This paper proposes an optimal autonomous search framework, namely Dual
Control for Exploration and Exploitation (DCEE), for a target at unknown
location in an unknown environment. Source localisation is to find sources of
atmospheric hazardous material release in a partially unknown environment. This
paper proposes a control theoretic approach to this autonomous search problem.
To cope with an unknown target location, at each step, the target location is
estimated by Bayesian inference. Then a control action is taken to minimise the
error between future robot position and the hypothesised future estimation of
the target location. The latter is generated by hypothesised measurements at
the corresponding future robot positions (due to the control action) with the
current estimation of the target location as a prior. It shows that this
approach can take into account both the error between the next robot position
and the estimate of the target location, and the uncertainty of the estimate.
This approach is further extended to the case with not only an unknown source
location, but also an unknown local environment (e.g. wind speed and
direction). Different from current information theoretic approaches, this new
control theoretic approach achieves the optimal trade-off between exploitation
and exploration in a unknown environment with an unknown target by driving the
robot moving towards estimated target location while reducing its estimation
uncertainty. This scheme is implemented using particle filtering on a mobile
robot. Simulation and experimental studies demonstrate promising performance of
the proposed approach. The relationships between the proposed approach,
informative path planning, dual control, and classic model predictive control
are discussed and compared.
