USV Obstacle Avoidance Using A Novel Local Path Planner and Novel Global Path Planner With r-PRM

Abstract:
This paper addresses the problem of real-time obstacle avoidance for an unmanned surface vehicle (USV) in an operational environment that has both fixed and moving obstacles. The solution approach uses a combination of a global path planner, which finds a path from a start point to a goal point while avoiding fixed obstacles, and a local path planner, which can circumscribe a moving obstacle. The global planner is novel in that it employs a combination of three path planners known as Grassfire (GF), Modified Grassfire (MGF), and Recursive Probabilistic Roadmap (r-PRM). The latter two are due to the authors, with r-PRM being a recursive version of the well-known PRM. This combination is guaranteed to find the path from any given start point to any given goal point, as long as such a path is possible. The local planner is novel in that it employs a decision logic to determine the best strategy for avoiding a moving obstacle, in particular, always routing the USV behind the obstacle rather than in front of or parallel to it. Simulations are provided exhibiting the acclaimed behavior. For comparison with other systems, the simulations include an implementation of the well-known D* algorithm, and the discussion considers additional dynamic path planning systems, which, like D*, do not necessarily route the USV behind the moving obstacle.