This is a partial list of research highlights for the Laboratory for Autonomous Systems Research. Click on a highlight title to see more details about the research.

Picture of concept Flimmer Flying-Swimmer (Flimmer) UAV/UUV

Flying-Swimmer (Flimmer) UAV/UUV investigates integrating an air delivery method with NRL’s finned swimmer UUV technologies. The aim is for fast and long-range deployment into hard-to-reach locations. Flimmer will demonstrate airborne delivery of a small UUV and examine the overlap between using fins for both underwater propulsion and for aero stability and control surfaces. The Flimmer program will investigate this trade space.

Octavia depicted in maintenance task where  good cognitive skills are important Cognitive Robotics and Human Robot Interaction

More capable and intelligent robots and autonomous systems will require more human-like cognitive abilities. The hypothesis is that systems that use human-like representations, strategies, and knowledge will enable better collaboration between these autonomous systems and the people who use them. An autonomous system must be able to perceive its environment, explain its decisions, and predict a person's needs,which should lead to more trust in the autonomous system. In this line of research, computational cognitive models, such as ACT-R, are used to build process models of human cognitive skills, and those models are then used as as reasoning mechanisms on the autonomous systems.

Four-fin UUV, WANDA-II (Wrasse-inspired Agile Near-shore Deformable-fin Automaton). Bio-Inspired UUV for Near-Shore Missions

The objective of this applied research project is to develop an unmanned underwater vehicle (UUV) capable of superior low-speed maneuverability and hover for operations in near-shore environments. To achieve the propulsion and control authority required for precise positioning in these dynamic environments we have taken inspiration from fish and are developing a vehicle prototype that employs robotic fins. For improved environmental awareness in potentially cluttered environments, we are developing an artificial lateral line of pressure sensors to detect near-field obstacles as well as current magnitude and direction.

Octavia the robot assisting in extinguishing a fire. Damage Control for the 21st Century

The DC-21 project is developing advanced technologies for shipboard damage control. The project includes the development of advanced autonomous systems to assist in damage control and discovery and control of incipient fires. Our focus is specifically on the human-robot interaction technology that will allow a Navy firefighter to interact peer-to-peer, shoulder-to-shoulder with a humanoid robotic firefighter.

A picture of the Ion Tiger in flight and a 550 W fuel cell Ion Tiger Fuel Cell Powered UAV

The Naval Research Laboratory's research on fuel cell systems and UAVs converge successfully in the Ion Tiger to enable long-endurance missions, allowing a large cruise range and reducing the number of daily launches and landings. The electric power system has the additional features of instant starting, near-silent operation, and low thermal signature. Fuel cell technologies can deliver energy savings and increased capabilities across the operational spectrum, to ground, air, and undersea vehicles and for man-portable power generation.

Diagram showing interaction on platforms, mission and algorithms Mobile Autonomous Navy Teams for Information Surveillance and Search

The focus of this effort is to adapt techniques inspired by nature to information gathering in complex Navy environments. This requires search techniques that are efficient when possible and robust when necessary, a level of adaptability that is common in nature and thus suggests a biologically-inspired approach. We refine the assumptions the algorithms make about the mission environment and platforms they direct in order to take a systems engineering approach to advancing the state of the art of robotic information gathering.

Picture of simulation Adaptive Testing of Autonomous Systems

The objective of this project is to develop technologies for advanced test and evaluation of the control software for intelligent autonomous systems. Machine learning techniques in the form of evolutionary algorithms and reinforcement learning are applied to learn the minimal number of faults that cause minimal or failed behavior of an autonomous system which is under the control of autonomy software -- the autonomy software is the system under test. The method uses high fidelity simulations of the vehicle and the environment, and tests are performed in that simulation.

Processing and Characterization of Lithium-ion Batteries

Lithium-based batteries are an important source of energy for autonomous systems and sensors. The objective of this research is to perform experiments necessary to understand the mechanisms responsible for lithium-ion battery safety. Recent NRL research shows temperature dependent morphologies of metallic lithium with implications for improving lithium-ion battery safety.

Picture of a 500-Watt hydrogen fuel cell stack designed, developed, integrated and tested at the Naval Research Laboratory. Hydrogen Fuel Cell Stack and Systems

The Chemistry, Tactical Electronic Warfare Divisions with the Computational Physics and Fluid Dynamics at the Naval Research Laboratory are pursing using hydrogen fuel cells as high-efficiency, high-energy, low-signature propulsion systems for unmanned systems. The team has developed a fuel cell stack made by 3D metal printing, and operated it effectively in a brass board system.

ROC Curve for error model Predicting and Preventing Errors

Even when people know how to do a task very well, errors still occur: People forget to check their blind spot when driving or they leave their original on the glass after making a copy. Our goal is three-fold: to understand why people make errors, to build models that take that understanding and predict when someone is going to make an error, and then to prevent that error before it happens.

Goal-Directed Autonomy Goal Reasoning

Autonomous agents should be able to identify what goals they should pursue at any time during their execution by reasoning about possible objectives, their observations, and their environment model. Our investigations on goal reasoning concerns the design, implementation, and evaluation of agents with this capability (e.g., for controlling an unmanned vehicle).

Tactile sensors attached to an MDS robot Robotic Touch Sensing, Manipulation, and Fault Detection

The objective of the Robotic Touch Sensing project is to develop an artificial sensate skin for robots to extend the perceptual capabilities of robotic manipulators to include touch. Under this effort we are developing tactile sensor arrays using piezoresistive sensing elements and have demonstrated a method for determining the location and magnitude of a contact (or contacts) for a multi-touch artificial skin by analyzing the responses from the sensors embedded within the skin.

Diagram of physicomimetic control law based on gravity. Swarm Control using Physicomimetics

Swarm intelligence is characterized by the emergence of collective capabilities from simple autonomous agents resulting from local interactions between the agents and their environment. Natural examples of swarm intelligence (e.g., colonies of ants) have led to the development of a number of distributed approaches to controlling agents. The method of swarm control we will use in this project is called physicomimetics. This method is based on an artificial physics representation in which agents behave as point-mass particles and respond to artificial forces generated by local interactions with nearby particles. We have developed a generalized form of physicomimetics that supports heterogeneous agents through multiple particle types and multiple force laws.