The core focus of the current work in our lab is the development of analytical control and estimation methods for integrated sensing and actuation in nonlinear systems, the use of these tools to query basic biological principles, and the translation of the results to both traditional and nontraditional engineering platforms.
To realize the capabilities of agile and high-performance systems, nonlinear and time-varying control methodologies must be considered. We consider stability and robustness in switched systems with delay, differential geometric methods, averaging methods, and incorporation of operational constraints such as communication delays in control of multi-vehicle systems. Applications include both traditional autonomous vehicle systems such as fixed-wing aircraft, underwater gliders and space launch vehicles as well as novel systems including bio-inspired underwater propulsion, bio-inspired agile flight, human decision making, and neural engineering.
While the coupling between actuation and sensing has long been a known characteristic in nonlinear systems, only recently has this coupling received attention with the purpose of exploiting its capabilities rather than mitigating its complexity. The results that come from the coupling of sensing and actuation in nonlinear systems and in the breadth of areas that can be addressed together with the tools being developed have led to means for asking questions of biology and engineering that have not previously been available.
The results of our work have been demonstrated in estimation and path planning in unmanned aerial vehicles with limited sensing, vorticity sensing and sensor placement on fixed wing aircraft, landing maneuvers in fruit flies, joint optimization of control and sensing in dynamical systems, control of space launch vehicles, and deconfliction and obstacle avoidance in autonomous systems and in biological systems including fish, insects, birds, and bats.
The focus of this work, in partnership with the Air Force Center of Excellence on Nature-Inspired Flight Technologies and Ideas (NIFTI) at UW, 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.
Autonomous agile flight of aircraft and spacecraft with dynamic payloads and interacting with the environment is a technological goal of high immediate interest in both commercial and military applications. We are exploring control methods for systems with shape actuation such as quadrotors with dynamically reconfigurable arms, underwater vehicles with fin actuation, and soft robotics with shape actuation. The end goal of these systems is to realize advances in design, modeling and control of vehicles with actuated shape and actuated and/or dynamic inertia for increased of agility in the sense of faster response times to unexpected obstacles and greater range of response capabilities.
Sparse, distributed and switched sensing
All control systems are subject to some degree of power and processing constraints, which can be limiting for all autonomous vehicles and particularly for small vehicles. All autonomous vehicles have a variety of onboard sensors and often these sensors provide more sensory information than is required for typical operations. Our research seeks to build on recent advances in empirical methods for nonlinear control systems to develop tools for assessing which sensory information is the most valuable to a system at any given time for completing a specific task, for maintaining situational awareness, and/or to compensate for sensor failure.
Model-based control and learning
The objective of the work in this proposal is to hybridize model-free Machine Learning (ML) with model-based planning, control, and state estimation to enable active perception for autonomous vehicles operating in complex environments and in complex mission scenarios. Active perception, meaning real-time decision-making and control for task-driven sensing and state estimation, provides a powerful force multiplier for situational awareness at all scales from tactical mapping to basin-scale monitoring, increasing temporal/spatial resolution and providing robustness.
To achieve these capabilities, machine learning methods will provide key insights enabling the high-level autonomy needed to handle complex stochastic environments, whereas model-based algorithms will bring formal robustness needed for reliable mission execution. Our framework decomposes the decision making hierarchically such that machine learning-based and model-based methods are applied where they are most effective via a systematic integration within the hierarchy.
Flight control systems for flexible aircraft
One of the central tradeoffs in aircraft design is the balance between the rigidity of an airframe and the associated structural weight. If an airframe lacks rigidity, i.e. is rather flexible, several issues ensue. Such issues include increased rates of fatigue, degradation of passenger comfort, and adverse interactions between structural deformations and flight dynamic behavior. Advances in actuation, sensing, and control algorithms have set the stage to enable advantageous design of less rigid aircraft by addressing these issues through automatic flight control systems.
The research efforts in this project contribute to that goal with emphasis on the design and analysis of multi-objective control laws with both classical and direct optimization approaches and with validation efforts in both simulation and actively controlled wind tunnel experiments. Some specific topics of interest include the use of Model Predictive Control (MPC) with Light Detection And Ranging (LiDAR) to actively reduce structural loads incurred by atmospheric gusts, measures of robustness for such MPC-based flight control features, and measures of robustness for classically designed multi-objective multi-controller flight control systems.
Control with limited communication
We are interested in question of how much information is necessary and how to provide it for multiagent systems. We are particularly interested in systems with sensing and communication capabilities that are limited by bandwidth, computational power, range and energy. Such scenarios are typical of multiple autonomous aircraft in urban settings, multiple autonomous underwater vehicles in shipyards, small autonomous vehicles, and space systems.
Autonomous Underwater Gliders
Schooling behavior in nature and engineering
The work in this proposal focused 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 were performed using giant danio with different internal states to impose varying sensing rules. Fish trajectories were tracked with cameras, and the data was then extracted for use in derived control theoretic algorithms to be applied to engineered systems. These algorithms were implemented on a testbed of free swimming remote-controlled robots. Experiments on the robot testbed were used to validate and refine models and motivate further testing criteria for the schools of fish.
Modeling and control in mixed human/robotic teams
This project was 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 was composed of psychologists, engineers, and applied mathematicians in a cross-disciplinary collaboration to develop models of human relationships in organizational, command, and social structures and in human-machine interactions in tactical operations. The goal of the research was 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 informed the work.