Multistep dual control for exploration and exploitation in autonomous search with convergence guarantee

Inspired by the concept of recently proposed dual control for exploration and exploitation, this paper presents a multi-step dual control for exploration and exploitation with guaranteed convergence in the search for autonomous sources. To deal with an unknown source position and environment, the proposed dual control algorithm faces significant challenges in demonstrating its recursive feasibility and convergence. With the help of the properties of Bayesian estimators, we redesign a multi-step dual control for exploitation and exploration algorithm with necessary terminal ingredients, and show that the recursive feasibility and the convergence of the modified dual control algorithm are guaranteed. Two simulation scenarios are conducted, which demonstrate that the proposed algorithm outperforms the stochastic model predictive control approach and the informative path planning approach in terms of searching successful rates and efficiency.