Path planning has the task of planning a path from the current position of the AUV to the next waypoint specified by the decision making. The path is then passed to a path-following controller, which passes appropriate actuation signals to the actuators.
One challenge in trajectory planning is to consider constraints in the maneuverability of the vehicle. For example, minimum curve radii must be maintained at various speeds. Actuator constraints must also be met when accelerating in the depth direction so that the planned path can be followed. For example, the buoyancy engine cannot be filled or emptied immediately. Furthermore, trajectory planning must take into account obstacles that have already been mapped, and it can incorporate information on flow conditions. The goal is to select the path in such a way that, taking into account the aforementioned constraints, the expected energy consumption is minimized.
To achieve the requirements, the current plan is to use a graph-based approach. For this purpose, possible path segments are formed with a local planner using so-called Dubins paths. These already take into account the approximate maneuverability boundary conditions of the nanoAUV and estimate the energy consumption. A higher-level graph search algorithm assembles the local path segments into a global overall path. The so-called A* search algorithm is used here to search the possible overall paths.