Online Navigation Planning for Long-term Autonomous Operation of Underwater Gliders

Abstract

Underwater glider robots have become an indispensable tool for ocean sampling. Although stakeholders are calling for tools to manage increasingly large fleets of gliders, successful autonomous long-term deployments have thus far been scarce, which hints at a lack of suitable methodologies and systems. In this work, we formulate glider navigation planning as a stochastic shortest-path Markov Decision Process and propose a sample-based online planner based on Monte Carlo Tree Search. Samples are generated by a physics-informed simulator that captures uncertain execution of controls and ocean current forecasts while remaining computationally tractable. The simulator parameters are fitted using historical glider data. We integrate these methods into an autonomous command-and-control system for Slocum gliders that enables closed-loop replanning at each surfacing. The resulting system was validated in two field deployments in the North Sea totalling approximately 3 months and 1000 km of autonomous operation. Results demonstrate improved efficiency compared to straight-to-goal navigation and show the practicality of sample-based planning for long-term marine autonomy.

Figures (10)

• Figure 1: Slocum glider being deployed during a preliminary experiment. The proposed Glider Long-term Autonomy Engine (GLAE) system was used to pilot the glider autonomously in two long-term deployments totalling approximately $3$ months and $1000$ km.
• Figure 2: An illustration of the key inputs and outputs for the glider dive simulator, which is used to generate stochastic MDP transitions.
• Figure 3: An illustration of a search tree built by the planner. Circles represent state nodes and squares represent action nodes. Action nodes can have mulitple children as MDP transitions $\mathcal{T}$ are stochastic. $Q$ is the expected cost of an action. At the root, diving at $-30$ degrees relative to the goal is estimated to lead to the lowest expected cost. The search tree is several levels deep and explored asymmetrically by the planner to balance exploration and exploitation. Starred nodes denote terminal states in which the glider reaches the goal.
• Figure 4: Illustration of the proposed action space formulation (left), which uses bearings $\alpha$ that are relative to the goal. This leads to a lower branching factor than deciding headings $\psi$ directly (right) while retaining relevant actions.
• Figure 5: Visualisation of waypoint list generation. The first waypoint corresponds to the planner action (here, $\alpha=-30$). The first backup waypoint is set to be the current goal as otherwise the goal would be overshot. The second backup waypoint is aimed straight at the next goal.