In this paper, a concurrent learning framework is developed for source search
in an unknown environment using autonomous platforms equipped with onboard
sensors. Distinct from the existing solutions that require significant
computational power for Bayesian estimation and path planning, the proposed
solution is computationally affordable for onboard processors. A new concept of
concurrent learning using multiple parallel estimators is proposed to learn the
operational environment and quantify estimation uncertainty. The search agent
is empowered with dual capability of exploiting current estimated parameters to
track the source and probing the environment to reduce the impacts of
uncertainty, namely Concurrent Learning based Dual Control for Exploration and
Exploitation (CL-DCEE). In this setting, the control action not only minimises
the tracking error between future agent's position and estimated source
location, but also the uncertainty of predicted estimation. More importantly,
the rigorous proven properties such as the convergence of CL-DCEE algorithm are
established under mild assumptions on noises, and the impact of noises on the
search performance is examined. Simulation results are provided to validate the
effectiveness of the proposed CL-DCEE algorithm. Compared with the
information-theoretic approach, CL-DCEE not only guarantees convergence, but
produces better search performance and consumes much less computational time