Towards Combined Task and Motion Planning for Autonomous Underwater Vehicles

In oceanic research and development, autonomous underwater vehicles (AUVs) provide scientists with the ability to augment expensive manned operations at a lower cost while simultaneously exploring regions that were previously inaccessible to scientists. While the cost of these AUVs is often nontrivial, the ability to autonomously sample data from varying regions over extended time periods removes the necessity of human operations which require much higher overhead costs. Scientists are now leveraging the unique abilities of AUVs to explore new environments, scientists are now starting to use AUVs to perform sophisticated missions in deep ocean environments, under the polar ice caps, or throughout dangerous minefields in the littoral. The success of these missions, however, depends on the ability of the AUV to autonomously perform complex tasks.;Toward this goal, this dissertation seeks to enhance the capabilities of AUVs so that they are able to autonomously plan the high-level actions and the low-level motions needed to accomplish complex missions. A framework is developed which makes it possible to specify such missions in a structured language resembling English, and it automatically plans the actions and motions that the AUV needs to execute in order to accomplish the mission. The mission-specification language is grounded in well-established logical formalisms such as Regular Languages and Linear Temporal Logic. The inherent structure of the mission-specification language makes it possible to construct sophisticated mission such as exploring unknown areas, searching for objects of interest, or collecting data. In doing so, the framework alleviates the burden imposed on human operators who currently need to manually input highly detailed mission specifications into multiple configuration files, which increases the risk for mission failure due to human error. Instead, the framework makes it possible for the human operators to specify the missions in an easy-to-use, structured language.;The technical contribution of the dissertation stems from a novel treatment of the combined mission and motion-planning problem as a hybrid search over discrete and continuous layers. Leveraging advances in AI and Robotics, a hybrid-planning framework is developed which combines high-level AI mission planning with low-level sampling-based motion planning. High-level planning, which operates over a discrete and abstract layer, breaks down the overall mission into a sequence of tasks. Sampling-based motion planning conducts a search over the feasible motions of the AUV in order to compute a trajectory that enables the AUV to accomplish each task. When sampling-based motion planning fails to make progress it requests another high-level plan from the AI planning layer. This interplay between high-level discrete planning and sampling-based motion planning is crucial to the success of the framework.;The hybrid framework can be used with any AUV. Extensive experiments have been conducted with high-fidelity simulators and real AUVs, such as OceanServer Iver2 AUV and Reliant Bluefin-21 AUV. The experimental results show the ability of the approach to effectively plan collision-free and dynamically-feasible trajectories that enable the AUV to carry out sophisticated missions, such as inspection of numerous areas, data collection, and reacquisition and identification in Mine Countermeasures. The success of the hybrid framework highlight the potential of the approach to enhance the autonomy of AUVs, making it possible to carry out sophisticated missions at a lower operational cost.