Bayesian Learning Model Predictive Control for Process-Aware Source Seeking

Yingke Li, Tianyi Liu, Enlu Zhou, Fumin Zhang

Abstract: Classical source seeking algorithms aim to make the robot reach the source location eventually. This letter proposes a process-aware source seeking approach which finds an informative trajectory to reach the source location. A multi-objective optimization problem is formulated based on rewards for both the search process and the terminal condition. Due to the unknown source location, solutions are found through Bayesian learning model predictive control (BLMPC). The consistency of the Bayesian estimator, as well as the convergence of the proposed algorithm are proved. The performance of the algorithm is evaluated through simulation results. The process-aware source seeking algorithm demonstrates improvements over other classical source seeking algorithms.