Formation Guidance of AUVs Using Decentralized Control Functions

Abstract

Will-be-set-by-IN-TECHThe goal is to provide decentralized consensus-building resulting in synoptical situational awareness of, and coordinated manoeuvring in the navigated waterspace.The paradigm is formally developed and tested in a hardware-in-the-loop simulation (HILS) setting, utilizing a full-state hydrodynamical rigid-body dynamic model of a large, sea-capable, long-endurance Ocean-going vehicle.Existence of realistic, technically feasible sensors measuring proxy variables or directly the individual kinematic or dynamic states is also simulated, as is the presence of realistic, non-stationary plant and measurement noise. The cooperative paradigmSince 1970s, robotics and control engineers have studied the cooperative paradigm.Cooperative control is a set of complete, halting algorithms and machine-realized strategies allowing multiple individual agents to complete a given task in a certain optimal way.This optimality results from the agents' leveraging each other's resources (e.g.manoeuvring abilities) to more effectively minimize some cost function that measures a "budget" of the entire task, in comparison to what each agent would would be capable of on their own (without the benefit of the group).In the marine environment, such "social" leveraging is beneficial in several ways.Firstly, deployment of more AUVs significantly reduces the time needed to survey a given theater of operations.This has enormous economic repercussion in terms of conserved hours or days of usually prohibitively expensive ship-time (for the vessel that is rendering operational support to the AUV fleet).Secondly, deployment of a larger number of AUVs diversifies the risk to operations.In a group scenario, loss of a (small) number of AUVs doesn't necessarily preclude the achievement of mission goals.Lastly, if each of the group AUVs are furnished with adaptive-sampling algorithms, such as in the chemical plume-tracing applications (Farrell et al., 2005;Pang, 2006;Pang et al., 2003), deployment of multiple vehicles guarantees much faster convergence to the points of interest.Cooperative control frameworks are split into centralized and decentralized strategies.A centralized cooperative control system's task is to determine the actions of each agent based on a perfectly (or as near perfectly as possible) known full data-set of the problem, which consists of the state vectors of every agent for which the problem is stated.The centralized system instantiates a globally optimal solution based on the assessment of momentary resource-disposition of the entire ensemble, as well as based on the total, if possibly non-ideal knowledge of the environment.The state data are usually collected by polling all agents through a communication network.After the polling cycle, the centralized system communicates the low-level guidance commands back to individual agents.This approach allows for the emergence of a global optimum in decision-making on grounds of all obtainable information, but heavily depends on fault-intolerant, quality-assured, high-bandwidth communication.In a decentralized approach, such as we have chosen to present in this chapter, each agent possesses imperfect state and perception data of every other agent and of the observable portion of the environment, and locally decides its own course of action.The greatest issue in decentralized cooperative control is the achievement of a consensus between separately reasoning autonomous agents. The virtual potentials frameworkTo address the issue of reactive formation guidance of a number of AUVs navigating in a waterspace, a method based on virtual or artificial potentials is hereby proposed.The virtual 100 Autonomous Underwater Vehicles www.intechopen.