The focus of this work is to build a process for translating biological capabilities for agile flight in dynamic, complex and unknown environments to appropriate designs and algorithms for engineered flight vehicles. In engineered systems, sensors are typically complex in terms of computational requirements, weight and physical design and have typically been designed to provide data on individual quantities with high density. Conversely, biological systems employ a high number of simple sensors that provide data for limited portions of a quantity of interest and which must be fused across both spatial and time scales. These biological systems demonstrate the ability to fly effectively in highly cluttered environments such as under the forest canopy, safely land on variable and moving terrain (e.g. branches or vertical walls), operate with highly variable lighting and acoustic conditions, and achieve desired behaviors in the presence of extreme environmental perturbations such as wind and adversaries.
Inspired by nature, our intent is to generate novel bio-inspired systems that can out-perform existing engineered systems in speed, agility and efficiency. We focus on bioinspired actuators (based on fish-fin type structures) to control fluid dynamic artifacts (both in and away from the boundary layer) that will ultimately affect speed, agility, and stealth of air and underwater autonomous vehicles. Our current work focuses on refining our mathematical models and extending them to more aggressive operation such as for flexible foils. Photo and Video Gallery >
We are interested in designing methods that are most relevant to multi-vehicle systems connected via communication that is over media that is extremely limited in bandwidth. Further, we are interested in vehicles whose dynamics incorporate realistic motion constraints such as not being able to move directly sideways or not being allowed to move in reverse. By modeling planar vehicle motion using Frenet-Serret formulas and 3D motion using natural Frenet frames, nonholonomic constraints and actuation bounds (forward velocity and turning rate) can be incorporated into vehicle dynamics.
The focus of the work in this area is to develop coordinated control algorithms for single and formations of UAVs (Unmanned Air Vehicles) that allow the formation to track single or multiple targets while preventing collisions. Successful tracking of a single target will be characterized by the centroid of the group tracking the position of the target, and tracking of multiple targets will be characterized by both the centroid of the group tracking the centroid of the set of targets and the footprint of the group covering the set of targets.
Three autonomous underwater vehicles (Seagliders) have been acquired and are being used to explore open ocean missions and operation of autonomous underwater vehicles. The vehicles are extremely efficient in individual operation. We are looking at optimizing performance of both individual and multiple glider systems for energy use, time to task completion and efficiency in sensing.
Schooling in Nature and Engineering
The work in this proposal focuses on a novel development of systems-level biomimetic control-theoretic models and motion control algorithms for embedded, emergent coordinated behavior of multiple organisms. To obtain data for traffic rules used by fish operating under heterogeneous sensing and traffic rules, schooling experiments are being performed using giant danio with different internal states to impose varying sensing rules. Fish trajectories are tracked with cameras, and the data is then extracted for use in derived control theoretic algorithms to be applied to engineered systems. These algorithms are implemented on a testbed of free swimming remote-controlled robots. Experiments on the robot testbed are used to validate and refine models and motivate further testing criteria for the schools of fish.
This project is a five-year multi-university research effort aimed at understanding key aspects of cooperative distributed decision making, coordination, and distributed control of groups of humans and autonomous machines. The team is composed of psychologists, engineers, and applied mathematicians in a cross-disciplinary collaboration to develop models of human elationships in organizational, command, and social structures and in human-machine interactions in tactical operations. The goal of the research is to develop new methods to capture, model, represent, and ultimately understand human behavior in military tactical scenarios involving autonomous and semi-autonomous vehicles. Principles and models of cognitive and social psychology inform the work.