To see all Research Highlights of the Navy Center for Applied Research in Artificial Intelligence, select "Research Highlights" in the tab menu above.

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.

A Tactical Action Officer (TAO) interacts with computer displays and audio inputs. Chat Attention Management for Enhanced Situational Awareness

This applied research project focuses on novel techniques and learning algorithms for (1) classifying chat messages, 2) summarizing chat room situations, and (3) 3D audio and visual cueing techniques for intelligently cueing watchstanders in a Navy Combat Information Center (CIC).

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).

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.

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.