Path planning for Unmanned Underwater Vehicles

Efficient path planning algorithms for embedded systems are a crucial issue for modern unmanned underwater vehicles. This paper proposes a method which is able to find paths from continuous environments prone to fields of force in a reliable and efficient manner. Classical path planning algorithms in artificial intelligence have limited performance and they are not designed to cope with real-time constraints of systems moving in a hostile underwater environment. We present a novel approach based on an advanced numerical technique called the Fast Marching algorithm to solve the following three issues. First, we extract a continuous path in an environment evenly mapped to a discrete grid. Secondly, the vehicle kinematics is introduced as a constraint on the optimal path curvature, and thirdly we take underwater currents into account thanks to an efficient extension of the original Fast Marching algorithm. Finally, a multiresolution scheme based on adaptive mesh generation is compared to incremental search techniques to speed up the overall process. A flexible platform is eventually presented to simulate path searching behaviors in real-time.