Time and Energy Optimal Path Planning in General Flows

Autonomous surface and underwater vehicles (ASVs and AUVs) are increasingly being used for persistent monitoring of ocean phenomena. Typically, these vehicles are deployed for long periods of time and must operate with limited energy budgets. As a result, there is increased interest in recent years on developing energy efficient motion plans for these vehicles that leverage the dynamics of the surrounding flow field. In this paper, we present a graph search based method to plan time and energy optimal paths in a flow field where the kinematic actuation constraints on the vehicles are captured in our cost functions. We also use tools from topological path planning to generate optimal paths in different homotopy classes, which facilitates simultaneous exploration of the environment. The proposed strategy is validated using analytical flow models for large scale ocean circulation and in experiments using an indoor laboratory testbed capable of creating flows with ocean-like features. We also present a Riemannian metric based approximation for these cost functions which provides an alternative method for computing time and energy optimal paths. The Riemannian approximation results in smoother trajectories in contrast to the graph based approach while requiring less computational time.