Abstract
This paper presents an approach using a hybrid modelling technique known as Manoeuvre Automaton (MA) to capture the key dynamics of a nonlinear autonomous underwater vehicle (AUV) in such a way that high-level tasks such as optimal motion planning can be computationally simplified, while still allowing it to perform complicated manoeuvres when the situation arises. With respect to motion planning in an obstacle filled environment, an incremental stochastic technique derived from the Rapid-exploring Random Tree (RRT) algorithm is applied. This paper proposes a multiple nested node version of RRT and also addresses the case of a time varying final state. Simulation results as presented, using a 3 degree-of-freedom (DOF) nonlinear AUV model in order to prove the viability of the concept.
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