Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes

Ramirez, Wilmer Ariza; Kocijan, Juš; Leong, ZQ; Nguyen, Hung D.; Jayasinghe, Shanti D. Gamini

doi:10.1007/s11633-021-1308-x

Cited by 11 publications

(1 citation statement)

References 33 publications

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“…The authors propose a symbolic regression method to automatically construct a parametric model through genetic programming from simple mathematical components. A multi-output Gaussian process regression (GPR) was used in [54] to identify the dynamics of a simulated 6-DOF AUV. The authors showed that the multi-output GPR manages to outperform a RNN-based approach.…”

Section: Model Learningmentioning

confidence: 99%

Recent Advances in AI for Navigation and Control of Underwater Robots

et al. 2022

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Purpose of Review The goal of this paper is to review current developments in the area of underwater robotics regarding the use of AI, especially in model learning, robot control, perception and navigation as well as manipulation. Recent Findings AI technologies and advanced control techniques are finding their way into robotics systems to deal with complex and challenging conditions and to equip them with higher levels of autonomy. Summary Although AI techniques and concepts are already a focus area in research on autonomous underwater systems, broad adoption to commercial systems is still in its infancy. Nonetheless, major advances have been done in recent years, especially on integrating different capabilities (perception, navigation, advanced control) in a single system and with first approaches on interaction and autonomous manipulation.

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Section: Model Learningmentioning

confidence: 99%