The goal of this work was to develop algorithms and software to generate a path that takes into account the direction of waves and wind as much as possible in order to mitigate potential damage to an autonomous underwater vehicle. A risk-based path planning algorithm to analyze real-world sensory data is combined with an enhanced sea surface model to generate a safe path.
The path planning method for sea surface vehicles prevents capsizing and bow-diving in a high sea state. A key idea is to use response amplitude operators (RAOs) or, in control terminology, the transfer functions from a sea state to a vessel’s motion, in order to find a set of speeds and headings that results in excessive pitch and roll oscillations. This information is translated to arithmetic constraints on the ship’s velocity, and are passed to a model predictive control (MPC)-based path planner to find a safe and optimal path that achieves specified goals. An obstacle avoidance capability is also added to the path planner.
A RAO is essentially a Bode plot that describes the frequency response of each of the six ship motions (i.e., roll, pitch, yaw, heave, sway, and surge) to a sea state (i.e., wave spectrum). A RAO is obtained from a linearized model of ship motions of a specific vessel hull shape at a specific speed and wave heading. Commercial software is available to compute RAOs based on computational fluid dynamics.
In order to enable online path planning, RAOs are pre-computed for all combinations of speed and wave heading at a certain interval, and are stored in memory. Then, during the online path planning, the RAOs are used to evaluate the maximum pitch and roll angles that are expected to occur when moving at a given speed and heading. This process is repeated on a discrete set of velocities, which is classified into safe and unsafe velocities by checking if the maximum pitch and roll angles are within pre-specified thresholds. Unsafe velocities are considered as obstacles in the velocity space, or velocity obstacles (VOs).VOs are translated into arithmetic constraints in the velocity space. The resulting constraints are typically non-convex.
The path planner is built upon the MPC. An optimal path planning problem with the non-convex velocity constraints obtained from the previous step is encoded into a mixed-integer linear programming (MILP). The MILP is solved online at each time step using the latest observation of the sea state in order to compute an optimal path.
This work was done by Masahiro Ono, Terrance L. Huntsberger, and Marco B. Quadrelli of Caltech for NASA’s Jet Propulsion Laboratory.
This software is available for commercial licensing. Please contact Dan Broderick at
This Brief includes a Technical Support Package (TSP).
Safe Maritime Autonomous Path Planning in a High Sea State
(reference NPO49304) is currently available for download from the TSP library.
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Overview
The document titled "Technical Support Package for Safe Maritime Autonomous Path Planning in a High Sea State" outlines the development of an autonomous algorithm aimed at enhancing the operational capabilities of unmanned surface vehicles (USVs) in challenging maritime environments. The primary focus is on ensuring continuous operation of USVs for various applications, such as patrolling and environmental monitoring, regardless of adverse sea conditions.
Key objectives of the project include creating an algorithm that can effectively manage the risks associated with capsizing and bow-diving while adapting to dynamically changing sea states in real time. The algorithm is designed to achieve high-level mission objectives, which are tested through simulations involving scenarios like target tracking, patrolling, and minimum-time transit.
The proposed approach integrates two main components: a Risk Manager and a Path Planner. The Risk Manager operates at approximately 0.1 Hz and is responsible for assessing the sea state, including wave height, frequency, and direction, to evaluate risks. The Path Planner, functioning at around 1 Hz, calculates an optimal path that adheres to velocity constraints and fulfills mission objectives while considering the assessed risks.
The document emphasizes the importance of real-time sea state estimation and incorporates COLREG-based collision avoidance strategies to enhance the safety and effectiveness of USV operations. The developed algorithm has been tested through simulations, demonstrating its capability to avoid risks and adapt to changing conditions while achieving mission goals.
In conclusion, the research highlights significant advancements in the field of maritime autonomous navigation, showcasing the potential for USVs to operate safely and efficiently in high sea states. The findings and methodologies presented in this document contribute to the broader understanding of autonomous systems in maritime environments and have implications for various applications in both scientific and commercial domains. The work is supported by NASA's Innovative Technology Assets Management and aims to facilitate the transfer of technology developed for aerospace applications to wider technological and commercial uses.
