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 for Exploration and Exploitation (CLEE). In this setting, the control action not only minimises the tracking error between future agents position and estimated source location, but also the uncertainty of predicted estimation. More importantly, the rigorous proven properties such as the convergence of CLEE algorithm are established under mild assumptions on sensor noises, and the impact of noises on the search performance is examined. Simulation results are provided to validate the effectiveness of the proposed CLEE algorithm. Compared with the information-theoretic approach, CLEE not only guarantees convergence, but produces better search performance and consumes much less computational time.
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