TY - GEN
T1 - Ergodic exploration for adaptive sampling of water columns using gliding robotic fish
AU - Ennasr, Osama
AU - Mamakoukas, Giorgos
AU - Murphey, Todd
AU - Tan, Xiaobo
N1 - Funding Information:
∗This work was supported by the National Science Foundation (IIS 1319602, ECCS 1446793, IIS 1715714) †Address all correspondence to this author.
PY - 2018
Y1 - 2018
N2 - In recent years, gliding robotic fish have emerged as promising mobile platforms for underwater sensing and monitoring due to their notable energy efficiency and maneuverability. For sensing of aquatic environments, it is important to use efficient sampling strategies that incorporate previously observed data in deciding where to sample next so that the gained information is maximized. In this paper, we present an adaptive sampling strategy for mapping a scalar field in an underwater environment using a gliding robotic fish. An ergodic exploration framework is employed to compute optimal exploration trajectories. To effectively deal with the challenging complexity of finding optimum three-dimensional trajectories that are feasible for the gliding robotic fish, we propose a novel strategy that combines a unicycle model-based 2D trajectory optimization with spiral-enabled water column sampling. Gaussian process (GP) regression is used to infer the field values at unsampled locations, and to update a map of expected information density (EID) in the environment. The outputs of GP regression are then fed back to the ergodic exploration engine for trajectory optimization. We validate the proposed approach with simulation results and compare its performance with a uniform sampling grid.
AB - In recent years, gliding robotic fish have emerged as promising mobile platforms for underwater sensing and monitoring due to their notable energy efficiency and maneuverability. For sensing of aquatic environments, it is important to use efficient sampling strategies that incorporate previously observed data in deciding where to sample next so that the gained information is maximized. In this paper, we present an adaptive sampling strategy for mapping a scalar field in an underwater environment using a gliding robotic fish. An ergodic exploration framework is employed to compute optimal exploration trajectories. To effectively deal with the challenging complexity of finding optimum three-dimensional trajectories that are feasible for the gliding robotic fish, we propose a novel strategy that combines a unicycle model-based 2D trajectory optimization with spiral-enabled water column sampling. Gaussian process (GP) regression is used to infer the field values at unsampled locations, and to update a map of expected information density (EID) in the environment. The outputs of GP regression are then fed back to the ergodic exploration engine for trajectory optimization. We validate the proposed approach with simulation results and compare its performance with a uniform sampling grid.
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U2 - 10.1115/DSCC2018-9179
DO - 10.1115/DSCC2018-9179
M3 - Conference contribution
AN - SCOPUS:85057381776
T3 - ASME 2018 Dynamic Systems and Control Conference, DSCC 2018
BT - Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2018 Dynamic Systems and Control Conference, DSCC 2018
Y2 - 30 September 2018 through 3 October 2018
ER -