Title:
Autonomous Surface Vehicle Model-Based Motion Planning
Poster
Preview Converted Images may contain errors
Abstract
A fundamental aspect of autonomous surface vehicle (ASV) operation is safe path planning. Due to a wide range of contingency scenarios, a priori path planning methods must be supplemented with real-time onboard path deviation capabilities for a fully autonomous system. The dynamics of high inertia underactuated surface vehicles further require path planning that accounts for the vehicle’s hydrodynamics. To meet this need, a model-based motion planning algorithm is proposed. The algorithm utilizes an online motion model and learned knowledge of the vehicle’s maneuvering characteristics to explore hydrodynamically feasible path deviations and select a solution. The algorithm is applied to an obstacle avoidance scenario with three maneuver parameterization schemes. It is analyzed utilizing an exemplar one-meter ten-kilogram microASV similar to various commercial-off-the-shelf uncrewed systems currently in use. Simulations demonstrate the system's ability to plan a hydrodynamically feasible and kinematically continuous path when faced with a static obstacle avoidance scenario. Results indicate an opportunity to improve ASV performance by generating a hydrodynamically feasible and effectively controllable reference trajectory, along with detailed maneuver dynamics that can be used to evaluate the candidate maneuver and by the control layer during execution.
Authors
First Name |
Last Name |
Nicholas
|
Custer
|
Leave a comment
Submission Details
Conference GRC
Event Graduate Research Conference
Department Ocean Engineering (GRC)
Group Poster Presentation
Added April 10, 2023, 12:22 p.m.
Updated April 10, 2023, 12:23 p.m.
See More Department Presentations Here