AUV real-time path planning in partly unknown environment through the Local-RRT

The partly unknown environment of the vast seas and the lack of prior accurate map of the workspace is one of the current challenges facing the autonomous underwater vehicle (AUV) in carrying out missions. Real-time path planning for an AUV from an initial position and velocity to a target position and velocity in a partly unknown environment by considering the pre-unknown obstacles is the main motivation of this research. For this purpose, the local rapidly-exploring random tree (L-RRT) algorithm is proposed for the AUV. This L-RRT consists of three tightly coupled components of: a path planning module (PPM), a local real-time path planning module (LRPPM) and an obstacle detection module (ODM). Each of a random vertices and related branch are generated through the PPM and then from the perspective of the kinodynamic constraints are evaluated by the low-level controller and nonlinear AUV model. If the generated vertex and related branch in the considered time satisfy the kinodynamic constraints in simultaneous manner, these and in addition the horizontal and vertical control signals are recorded through the path planner algorithm. If a pre-unknown obstacle is detected by the ODM, path is re-planed through the LRPPM based on the current position and orientation of AUV to the nearest vertex. The L-RRT is implemented on the single board computer (SBC) through the xPC-Target builder and then the ability of this algorithm is evaluated through the processor-in-the-loop (PIL) test. The results of the PIL tests indicate that the AUV through the proposed L-RRT not only plans the path by avoiding the pre-unknown obstacles in a cluttered environment, but also due to the random nature of this method the path is planned in a real-time manner.