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Shape Adapting Underwater Vehicles
The long term goal of this project is to design, model, and construct a novel class of underwater vehicle capable of adapting its shape configuration for optimal performance in a dynamic and diverse underwater environment. Autonomous underwater vehicles (AUVs) have shown great promise in fulfilling the surveillance, scavenging, and monitoring needs of the underwater domain. However, underwater autonomy is often hindered in expansive, cluttered, obstacle ridden, or sensitive environments that are information rich but inaccessible due to vehicle design or control constraints. Traditional gliders or streamlined AUVs are designed for long term operational efficiency in expansive environments, but are hindered in cluttered spaces due to their design and control authority; agile AUVs can penetrate cluttered or sensitive environments but are limited in operational endurance. This research focuses on the development, modeling, and control of a bio-inspired shape adapting vehicle leveraging techniques from continuum and soft robotics.
Using tools from dynamics and generative modeling we can model shape parameters as control inputs. This approach allows us to design control algorithms that actuate the shape of the vehicle to safely navigate through diverse underwater environments.
Project Collaborators
Project Alumni
- A. Bass, USNA 2016
- R. Richmond, USNA 2016
- W. Satre, USNA 2016
- T. Guinan, USNA 2018
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Event and Self-triggered Control of Multi-vehicle Systems
The expanded role of unmanned systems in military and civilian applications has introduced interesting new questions in multi-vehicle coordination and communication. Specifically, in communication-limited or denied environments, there exists a need for control algorithms that drive agents to a desired formation in space while coordinating, minimizing, or optimizing the time instances in which they communicate.
Project Alumni
A. Sims, USNA 2016
My research interests lie at the intersection of control and estimation theory, atmospheric and oceanic science, and biology. I am interested in solving challenging problems associated with open questions in autonomous multi-vehicle control, data driven adaptive sampling of environmental processes, and bio-inspired sensing and control. My focus is on the design of decentralized multi-vehicle sensing and control strategies to improve autonomy in multi-agent systems.