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This paper presents a framework for optimization-based informative planning and control with applications to adaptive sampling with AUVs under sea ice. A spatial model of the information of interest is approximated as a Gaussian process (GP), which is learned online from in-situ sensor data. The planner uses a two-layer model predictive control (MPC) scheme on a low-fidelity model of the vehicle for exploration and exploitation of the GP, subject to safety constraints. The planner trajectories are then tracked using a constant bearing based guidance law, aligning the desired orientation of the AUV toward the planned trajectory. The proposed framework enables the vehicle to plan and replan its mission as new data is obtained, while ensuring tracking of the planned trajectories and safety constraint satisfaction. Simulation results of a case study are presented for demonstrating the performance of the proposed method. An AUV is tasked with finding and tracking concentrations of marine biomass in 3D under sea ice while avoiding collisions. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
