Underwater path planing using fast marching algorithms

In this paper, new tools for obstacle avoidance and path planning for underwater vehicles are presented. The authors' technique, based on a level set formulation of the path planning problem, extracts optimal paths from complex and continuous environments in a complete and consistent manner. Fast marching algorithm is known to be efficient for finding cost optimal path in mobile robotics because of its reliability, precision, and simple implementation. Fast marching algorithm originally propagates a wave front to isotropically explore the space. We propose an anisotropic version of fast marching by adding directional constraints in a cost function to minimize. We then propose a path planning method able to deal with vectorial fields of force for the first time. Furthermore we explore the relation between the curvature of the optimal path and the cost function generated from scalar and vectorial constraints. This a priori knowledge of the influence of the environment on the final path's curvature allows us to propose a solution to make sure a path is reachable by the vehicle according to its kinematics. A multi-resolution scheme based on an adaptive mesh generation is eventually introduced to speed up the overall algorithm. Results are shown computed from real and simulated underwater environments.