Mission and Motion Planning for Autonomous Underwater Vehicles Operating in Spatially and Temporally Complex Environments

Abstract

This paper seeks to enhance the autonomy of underwater vehicles. The proposed approach takes as input a mission specified via a regular language and automatically plans a collision-free, dynamically feasible, and low-cost trajectory which enables the vehicle to accomplish the mission. Regular languages provide a convenient mathematical model that frees users from the burden of unnatural low-level commands and instead allows them to describe missions at a high level in terms of desired objectives. To account for the constraints imposed by the mission, vehicle dynamics, collision avoidance, and the complex spatial and temporal variability of the ocean environment, the approach tightly couples mission planning with sampling-based motion planning. A key aspect is a discrete abstraction obtained by combining the finite automaton representing the regular language with a navigation roadmap constructed by probabilistic sampling. The approach searches the discrete abstraction to compute low-cost and collision-free navigation routes that are compatible with the mission. Sampling-based motion planning is then used to expand a tree of dynamically feasible trajectories along the navigation routes. The approach is validated both in simulation and field experiments. Results demonstrate the efficiency and the scalability of the approach and show significant improvements over related work.