Cooperative Active Learning based Dual Control for Exploration and Exploitation in Autonomous Search

Zhongguo Li, Wen Hua Chen, Jun Yang, Cunjia Liu

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In this paper, a multi-estimator based computationally efficient algorithm is developed for autonomous search in an unknown environment with an unknown source. Different from the existing approaches that require massive computational power to support nonlinear Bayesian estimation and complex decision-making process, an efficient cooperative active learning based dual control for exploration and exploitation (COAL-DCEE) is developed for source estimation and path planning. Multiple cooperative estimators are deployed for environment learning process, which is helpful to improving the search performance and robustness against noisy measurements. The number of estimators used in COAL-DCEE is much smaller than that of particles required for Bayesian estimation in information-theoretic approaches. Consequently, the computational load is significantly reduced. As an important feature of this study, the convergence and performance of COAL-DCEE are established in relation to the characteristics of sensor noises and turbulence disturbances. Numerical and experimental studies have been carried out to verify the effectiveness of the proposed framework. Compared with existing approaches, COAL-DCEE not only provides convergence guarantee, but also yields comparable search performance using much less computational power.
Original languageEnglish
Pages (from-to)1-13
JournalIEEE Transactions on NEural Networks and Learning Systems
Early online date10 Jan 2024
Publication statusE-pub ahead of print - 10 Jan 2024


  • Autonomous search
  • active learning
  • dual control
  • exploration and exploitation
  • goal-oriented control systems


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