Methods for Coordination and Communication in Mixed Teams of Humans and Automata
Kristi A. MorgansenDepartment of Aeronautics and Astronautics
University of Washington
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Modeling Estimation
Control
Heterogeneous coordinated control with limited communication
Bioinspired system modeling for coordinated control
Integrated communication and control
Modeling and control of shape-actuated immersed mechanical systems
Nonlinear Dynamics and Control Lab
Cognitive dynamics models for human-in-the-loop systems
Coordinated control with communication
for UXVs
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Outline
• Research overview• Coordinated control• Integrated
communication and control
• Ongoing and future directions
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Modeling and control of fin-actuated underwater vehicles
Tail locomotion and pectoral fin maneuverability
NSF CAREERUW RRFNSF BE (with Parrish and Grunbaum, UW)
Goals
•Agile maneuverability•Analytical control theoretic models of immersed shape-actuated devices•Underwater localization•Nonlinear control•Coordinated control
Challenges
•Small size•Coriolis effects•Unmodeled or approximated fluid dynamics elements•Communication and sensing limitations
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Coordinated Control with Limited Communication
Goals
•Control in the presence of communication and sensing constraints•Control over networks•Deconfliction•Schooling/swarming group behavior
Challenges
•Managing time delays in local control•Definition of attention•Allocation of resources•Construction of stabilizing controllers•Modeling
NSF CAREERAFOSR (with Javidi, UCSD)AFOSR (with The Insitu Group, Inc.)The Boeing CompanyNSF (with Javidi, UCSD and Scaglione, Cornell)
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Hierarchical Integrated Communication and Control
NSF CAREERAFOSR (with Javidi, UCSD)AFOSR (with The Insitu Group, Inc.)NSF (with Javidi, UCSD and Scaglione, Cornell)
Goals
•Coordinated tracking of objects or boundaries•Non-separated design of communication and control algorithms•Data quantization•Cooperative task management•Control over networks
Challenges
•Managing time delays in local control•Allocation of resources•Construction of stabilizing controllers•Modeling for both communication and control
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Bioinspired Coordinated Control
•Models of social aggregations
•Effects of heterogeneity (levels of hunger, familiarity)
•Relation to engineered systems
•Application to fishery management, population modeling
NSF BE (with Parrish and Grunbaum, UW)
Murdock Trust
Goals
Challenges
•Tracking of objects•Data fusion•Model representation
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Cognitive Dynamics for Human-in-the-Loop
Challenges
•Model representation•Heterogeneity•Information flow•Levels of autonomy
Goals
•Coordinated control for heterogeneous multivehicle system with human interaction•Cognitive models and social psychology•Dynamics and control
AFOSR MURI (with J. Baillieul (BU), F. Bullo (UCSB), D. Castanon (BU), J. Cohen (Princeton), P. Holmes (Princeton), N. Leonard (Princeton), D. Prentice (Prentice), J. Vagners (UW))
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Outline
• Research overview• Coordinated control• Integrated communication
and control• Ongoing and future directions
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Planar Frenet-Serret
Simplified Model
Coordinated controlNonholonomic kinematics (UAV, UGV, USV, UUV)
x
y
r
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Coordinated control
Goal: Maintain sensor coverage of a desired object or set of objects
Given– Homogeneous group of
constant speed vehicles– All-to-all communication– One target vehicle
Extensions− Heterogeneous agents− Stochastic/hybrid dynamics− Dynamic communication
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Coordinated control
Goal: Match the velocity of the group centroid a given reference velocity.
Group centroid:
Centroid velocity:
Extensions:
− More generic tracking goals
50%
90%
Matching a reference velocity
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Coordinated control
K = -0.1, N = 10, sref = 0.5, tmax = 100
Matching a reference velocity
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Coordinated control
Question
What if the reference velocity is non-constant?
In particular, such a result is relevant to biological aggregates for which data has not shown strong tendencies toward alignment or splay, but rather a moving group centroid.
Dynamic reference velocity
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Coordinated controlAutomatic transition in behavior
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Coordinated control
• Want: Additive control term to keep individuals near the centroid.
• Analogous to the splay state.• Have two constraints already.• More than two vehicles are required.• Matched set and tangent:
Centroid spacing control
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Coordinated controlSpacing control (N=3)
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Coordinated controlSpacing control in 3D
• Desired acceleration
• Control
• Composed of four terms:Helix, Beacon, Speed, Plane
Given: A group of N identical constant-speed non-holonomic vehicles and a single target vehicle
Goals: The collective centroid should track the target; Individuals should “stay near” the collective centroid; Formal analysis
Assumptions: SE(3); no collisions; all-to-all comm
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Coordinated control
Because communication events are discrete time, the controller will employ a zero order hold. The resulting system kinematics are governed by the discrete time Kuramoto model.
Question: When is the model asymptotically stable to either the synchronized or balanced sets?
Discrete-time Kuramoto model
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Coordinated control
Answer: – Convergence to
synchronized set
– Convergence to balanced set
Asymptotic Stability
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Coordinated control
Define the order parameter
When r=0, the vehicle headings are aligned and when r=1, the headings are in the balanced state.
Motivating the Lyapunov function
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Coordinated controlAsymptotic synchronization: T=1.0, K=-0.05
Given: A group of N identical constant-speed non-holonomic vehicles and either all-to-all communication or one-to-all random broadcast.
Goals: Find a range of gains to guarantee stability to a common heading and evaluate performance based on settling time.
Results: • Stability in either case can be guaranteed for
-2 ≤ KΔT ≤ 0.• Settling time is minimal for K ΔT =-1.• Settling time increases as K ΔT becomes
near zero (loss of control authority).• Settling time increases as K ΔT becomes
near -2 (near stability limit, increasing oscillations).
Challenges: Restriction of controllers to guarantee communication QoS; Task complexity
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Outline
• Research overview• Coordinated control• Integrated communication
and control• Ongoing and future directions
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Integrated communication and control
Propose a (suboptimal) decomposition
• Coordinated control of nonlinear systems over a sequence of logical communication graphs G = {G0,G1, . . .}.
– Focus on initial task of target tracking with centroid of group
– Parameterized nonlinear control as sum of spacing and heading
• Energy optimal realization of logical communication graph Gn with strict time bound of .
Loss of optimality is in demanding a “perfect” behavior from network with over-design of a robust controller.
Coordinated control over a wireless network
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Integrated communication and controlMain result: Logical communication graph Gn with strict deadline
Given: Communicating the state variables every seconds (one-all) guarantees control objectives
Goal: What is the most energy efficient communication scheme achieve one-all communication?
• Simplest routing/relaying strategy is a single-hop wireless broadcast
• Other options include multi-hop gossiping (relaying)
Results: For most practical applications, the simple single-hop broadcast is optimal
Challenges: Inclusion of control performance in explicit optimization
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Integrated communication and controlMain result
Integration of Communications and ControlThe normalized total communication energy consumption of vehicles to reach an aligned state is a non-monotonic function of discretization time step, for various controllers (parameterized by K).
Conclusion: A trade off exists between desired control performance and network realization energy:
• As increases, the energy consumption of transmitting vehicles per decreases but large slows convergence
• Beyond some slow convergence dominates per-slot efficiency
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Conclusions and Ongoing Work
Discrete Time Systems with Delay• Time constants must be representative of physical scenarios
Tracking Control• Extend tracking to more generic scenarios than centroid
tracking of single target
Dynamic Communication• Realistic models and effective designs
Heterogeneous Systems• Appropriate models for human interaction
Biological Connections • Cognition, interfacing, data representation
http://vger.aa.washington.edu
This work was supported in part by the National Science Foundation, AFOSR and the University of Washington.