Model predictive control

Control (management)

Engineering

Extended Kalman filter

Control engineering

Unmanned underwater vehicle

Computer security

Real-time computing

Convergence (economics)

Archaeology

Economics

Computer science

Collision

Collision avoidance

Distributed computing

Sensor fusion

Geology

Oceanography

Geography

Artificial intelligence

Kalman filter

Underwater

Economic growth

Control theory (sociology)

Underwater glider

IFAC Proceedings Volumes

Coordinated fleet of Autonomous Underwater Gliders (AUGs) can provide significant benefit to a number of marine applications including ocean sampling, mapping, surveillance and communication. Traditional techniques for navigating underwater vehicles have been designed for single-vehicle operations and do not scale well to multi-vehicle operations and missions. In this paper a navigation system for a fleet of AUGs is developed based on Networked Decentralized Model Predictive Control (ND-MPC). The proposed approach coordinates a group of point-mass mobile agents to achieve a desired formation, while avoiding collisions between themselves. In order to obtain collision free paths, the approach integrates the required collision avoidance constraints. The fleet localization is performed by sensor fusion using adaptive extended Kalman filtering. The free collision and convergence properties are verified through simulations results. The proposed approach can be generalized to formation of heterogeneous autonomous agents.

Cooperative and decentralized navigation of autonomous underwater gliders using predictive control