Date of Award


Degree Type


Degree Name

Master of Science (MS)


Mechanical Engineering and Applied Mechanics

First Advisor

David Chelidze


Path planning for unmanned ocean vehicles often neglects constraints arising from the dynamics of the vehicle. Therefore, this thesis is concerned with finding a method that can incorporate a dynamical vehicle model into the problem of timeoptimal path planning. This requirement makes the path planning task an optimal control problem. Based on a review of optimal control theory and possible solution procedures, sampling-based algorithms and especially the Sparse RRT algorithm are identified as promising means of solving the problem. At first, several benchmark tests are carried out to demonstrate that Sparse RRT is able to generate reliable results. Then, the algorithm is applied to a dynamic boat model with three degrees of freedom and two control inputs. Constraints on the states of the system as can arise from obstacles or energy constraints are accounted for as well as constraints on the control inputs. Also, the effect of ocean current fields is incorporated.

In all planning scenarios, the application of Sparse RRT yielded plausible results. In conclusion of this thesis, the Sparse RRT algorithm therefore can be rated as a very flexible tool due to its ability to solve optimal control problems like time-optimal path planning for dynamic vehicle models while accounting for several different types of constraints.

However, it also turned out that especially higher-dimensional models require long computation times to ensure good results. In order to make a result applicable to real technical systems, post-processing procedures might be necessary. Also, several parameters of the algorithm have to be chosen carefully.