Methods for Coordination and Communication in Mixed Teams of Humans and Automata

Kristi A. MorgansenDepartment of Aeronautics and Astronautics

University of Washington

2

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

3

Outline

• Research overview• Coordinated control• Integrated

communication and control

• Ongoing and future directions

4

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

5

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)

6

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

7

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

8

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))

9

Outline

• Research overview• Coordinated control• Integrated communication

and control• Ongoing and future directions

10

Planar Frenet-Serret

Simplified Model

Coordinated controlNonholonomic kinematics (UAV, UGV, USV, UUV)

x

y

r

11

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

12

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

13

Coordinated control

K = -0.1, N = 10, sref = 0.5, tmax = 100

Matching a reference velocity

14

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

15

Coordinated controlAutomatic transition in behavior

16

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

17

Coordinated controlSpacing control (N=3)

18

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

19

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

20

Coordinated control

Answer: – Convergence to

synchronized set

– Convergence to balanced set

Asymptotic Stability

21

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

22

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

23

Outline

• Research overview• Coordinated control• Integrated communication

and control• Ongoing and future directions

24

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

25

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

26

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

27

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.