DDDAMS-based Planning and Control - · PDF fileProgram Manager: Dr. Frederica Darema. Computer...

DDDAMS-based Planning and

Control

Young-Jun Son ([email protected])

Systems and Industrial Engineering

University of Arizona

Co-PIs: Jian Liu, Jyh-Ming Lien (George Mason)

Students: S. Minaeian, S. Lee, Y. Yuan, A. Khaleghi, D. Xu, Z. Wang, M. Li, N. Celik, K. Vasudevan

Sponsors:

AFOSR: FA9550-12-1-0238 (DDDAS); FA9550-17-

1-0075 (DDDAS); NSF-CNS: 0540212

Program Manager: Dr. Frederica Darema

mailto:[email protected]

Computer Integrated Manufacturing & Simulation Lab

Department of Systems and Industrial Engineering, The University of Arizona, Tucson

Theme and Topic

• (Workshop) Towards an Ecosystem of Simulation Models and Data

• (Topic) Online, data-driven simulations– Internet/Integration of things (data, model, embedded

algorithms, and hardware/human)



Simulation-Based Planning and Control

• Simulation-based planning and real-time control (shop floor control) (since late 1990’s)

• Extension to top level supply chain activities via DDDAMS

– Via an NSF project (2005~2007)

• UAV/UGV control via DDDAMS

– Via two AFOSR projects (2012~current)



Simulation-based Planning and Control

Y. Son, S. Joshi, R. Wysk, and J. Smith, Simulation Based Shop Floor Control, Journal of Manufacturing

Systems, 21(5), 2002.

Simulation-based Planning Simulation-based Control





Systems, 21(5), 2002.

Simulation-based Planning

Traditional simulation

(current status)

Reduction

of decision

space (NN)



Real-Time Decision Problems in a Flexible Shop

• Part releasing problem

• (Pull system) Part selection problem

• Machine selection problem

• Part buffering problem

• Robot location problem

• And, other problems

H. Cho, Y. Son, A. Jones, Design and Conceptual Development of Shop Floor Controllers through the

Manipulation of Process Plans, International Journal of Computer Integrated Manufacturing, 19(4), 2006.



Part Selection Problem in a Flexible Shop

Machine A Machine B Machine C

Cell-

dedicated

buffer

Robot

Part 1

Part 2

Part 4

o1

1

4

3 2

o2

o3

o7

o8

sa ja

To be processed

immediately

Part 3 o4

o5

sa

o6

ja

Part

A B

A

B A

B B

C





Machine Selection Problem in a Flexible Shop

Machine A Machine B Machine C

Cell-

dedicated

buffer

Robot 3

1

4 1 2

Part

Part 1

Part 2

Part 4

o1 o2

o3

o7

o8

sa ja

To be processed

immediately

Part 3 o4

o5

so

o6

jo

A B

C

B A

B B

C







Systems, 21(5), 2002.

Simulation-based Control

• Real-time clock

(parallelism)

• Task generation

• Message exchanges

with FSA




Formal model

Automatic Code Generation

for Controllers



Interactions among controllers

pick put open grasp.....

RT Simulation

(Task Generator)



Interaction between Planning and Control



• Based on same initial conditions (system status)



Hierarchical Process Plans (Cho et al, 2006)



Extension to Top Floor

• Analogies between shop floor and top floor in terms of system

components

– Physical entities and tasks

– Coordination model

– Simulation model

– Resource model

J. Venkateswaran and Y. Son, Hybrid System Dynamic – Discrete Event Simulation based

Architecture for Hierarchical Production Planning, International Journal of Production

Research, 43 (20), 2005.

S. Lee, Y. Son, and R. Wysk, Simulation Based Planning and Control: From Shop Floor to Top

Floor, Journal of Manufacturing Systems, 26 (2), 2007.



DDDAMS: Top Floor

• Simulation (fast-mode simulator, and task generator) for

a highly complex system

– Adaptive and localized abstraction during the simulation run

– Simulation steers the measurement process (affecting

model fidelity)

Traditional DDDAMSN. Celik, S. Lee, K. Vasudevan, and Y. Son, DDDAS-based Multi-fidelity Simulation Framework for

Supply Chain Systems, IIE Transactions on Operations Engineering, 42(5), 2010.



Sensory data

Predetermined

fidelity levelAssigned fidelity

level (δt)

Data filtering algorithm

Algorithm 1

Fidelity selection algorithm

Algorithm 2

Algorithm 4

Algorithm 3

Fidelity assigning algorithm

Real System

Machine 2Machine 1 Machine 3 Machine n

. . .

Information request

Dynamic simulation model reconstruction

algorithm

Operations scheduling

(Task generation)

Information update

Filtered data/

Detected abnormality

Available computational

resource

DDDAS

Data flow

Control flow

Assigned fidelity level (δt)

Embedded Algorithms in DDDAMS Simulation



Real System

RTdddasF12

C1 C2 C3 C4 C5

Communication Unit

F1

F2

Algorithms

Fidelity Info.=[23322]

Shop

CreatedAdvanced

Models (In different Arena files)

RSPr_RRSPh_RRSM_RRSE_RRSD_R

RSPr_SRSPh_SRSM_SRSE_SRSD_S

RTShop1_R

RTD_RRTE_R

RTM_RRTPh_R

RTPr_R

Information Request

Information Update

Simulation Results

Firing Up Advanced Models

RTdddasC1F3

RTdddasC2F3

RTdddasC3F3

RTdddasC4F3

RTdddasC5F3 RTF3Pr_R

RTF3M_R

RTF3Ph_R

RTF3E_R

RTF3D_R

F3 RTdddasF12

Interaction/Communication Instance



Surveillance via UAVs/UGVs (or Patrol Agent)

Goal: Develop a simulation-based planning and control system for

surveillance and crowd control via collaborative UAVs/UGVs (Border

Patrol Agent)

Motivation: TUS 1- Project (23-mile long area of southern border in Sasabe, AZ)

Problem: A highly complex, uncertain, dynamically changing border environment



DDDAMS-based Planning and Control Framework

Khaleghi, A. M., Xu, D., Wang, Z., Li, M., Lobos, A., Liu, J., & Son, Y. (2013). A DDDAMS-based Planning and Control

Framework for Surveillance and Crowd Control via UAVs and UGVs. Expert Systems with Applications, 40, 7168-7183.

20


Department of Systems and Industrial Engineering, The University of Arizona, Tucson 21

Multi-resolution Data (Cloud; Trees; Rocks)

Challenge: Aggregate Multi-resolution data

Opportunity: UAVs’ global perception and UGVs’ detailed perception



Crowd Representation

Next waypoints of UGV

Low density region

invisible by UAV

Representation of crowd individuals

Crowd view by UAV

Crowd view by UGV

Coarser visibility cell

View of dense crowd regions

Finer visibility cell

View of individuals

Next waypoints of UAVs

High density region

visible by UAV

High resolution

Low resolution

Hybrid

Control command generation

for UAV/UGV motion planning

8



Framework (Planning and Control)



DR(x)

DR(y)

FOV (x)FOV(y)

h

Detection Module Testbed

24

DR

min

G( )= 2 h

min

G( ) tan FOV / 2( ) £ DR

G( ) £ 2 h

max

G( ) tan FOV / 2( ) = DR

max

G( )

DR

( A) = 2h( A) tan FOV / 2( )

GoPro HERO 3- Tarot Gimbal Stabilizer

HD (16:9): 1280x720p @ 120 ~ 25 fps

FOV(x): 64.4 ; FOV(y): 37.2

EDD : UGV’s Effective Detection Depth

: Detection range for UGV

: Detection range for UAV

: Field of view

: Distance

: Altitude

DRG( )

DRA( )

FOV

h( A)

h(G )

ODROID USB-CAM 720P

HD (16:9): 1280x720p @ 30 fps

FOV: 72



1. Shi, J., & Tomasi, C. (1994, June). Good features to track. In Computer Vision and Pattern Recognition, 1994. Proceedings CVPR'94., 1994 IEEE Computer

Society Conference on (pp. 593-600). IEEE.

2. Lucas, B. D., & Kanade, T. (1981, August). An iterative image registration technique with an application to stereo vision. In IJCAI (Vol. 81, pp. 674-679).

Crowd Detection Module UAV

: Applying thresholds

to extract targets of our

interest

25

Optical-Flow-Based Motion Detection

Feature

extraction

Feature

tracking

Warp

successive

frames

Track general

movements

Detect the

target

: Good features to track 1

Extract key points

: Pyramidal Lucas-Kanade 2

Find optical flow vector

: Affine transformation

Warp successive frames

: Motion segmentation

Using the motion history

OpenCV



Individual Detection Module UGV

: Linear SVM

26

HOG1-Based Human Classification

: 3x3 derivative mask

[-1, 0 , 1]

: Weighted voting over cells

6x6 pixel cells

: Grouping cells into blocks

3x3 cell blocks

: L2-norm

Gradient

computation

Orientation

binning

HOG over des.

blocks

Block

Normalization

Classify the

target

1. Dalal, N., & Triggs, B. (2005, June). Histograms of oriented gradients for human detection. In Computer Vision and

Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 886-893). IEEE.

OpenCV



Different Crowd Detection Scenarios

27

1. Crowd Joining 2. Crowd Splitting

3. Out of Detection Range 4. Random Movements



Framework of Proposed Methodology

28



Crowd Tracking Module

UGV Negligible observation

error

With

observation error

Low

resolution

UGV autoregressive model

-> forecasting

UAV aggregation model ->

prediction

UGV state space model ->

filtering

UAV aggregation model ->

prediction

High

Resolution

UGV autoregressive model

-> forecasting

UAV state space model ->

filtering

UGV state space model ->

filtering

UAV state space model ->

filtering

• Crowd Dynamics Identification

• Crowd Motion Prediction

UAV

7



Crowd representation by UAV

Crowd Center Individual Representation Grid

Representation

• Information

loss

• Computationally

intensive

• Require high resolution

• Comprehensive

• Flexible



UAVs’ crowd dynamic model

？？？？

The probability of occupancy depends on neighborhoods:

31

?t=t1+4τ

Neighborhood

Dependency

?Estimate by matching

history neighborhood

pattern

𝑷𝒍,𝒎𝑨 𝒕 + 𝝉Pattern to Cell

Occupancy

Cell

Occupancy

Prediction



UGVs’ crowd dynamic model

• High-resolution data, Auto-regressive model

Individual

Location

Prediction

𝑷𝒍,𝒎𝑮 𝒕 + 𝝉

Cell

Occupancy

Prediction

Threshold λ



Information aggregation

33

𝑃𝑙,𝑚𝑆 𝑡 + 𝜏 = 𝑤𝑙,𝑚

𝑃𝑙,𝑚𝐴 𝑡 + 𝜏 + (1 − 𝑤𝑙,𝑚) 𝑃𝑙,𝑚

𝐺 𝑡 + 𝜏

Prediction based on

UAVs’ dataPrediction based on

UGVs’ data

Aggregated Prediction

𝑷𝟒,𝟒𝑨 𝒕 + 𝝉 =0.33 𝑷𝟒,𝟒

𝑮 𝒕 + 𝝉 =0.95

𝑷𝟒,𝟒𝑺 𝒕 + 𝝉 =0.76 (In this example 𝑤3,3 = 0.3)



Case Study (Testbed)



Case Study

35



Framework of Proposed Methodology

36



Motion Planning Module• Input:

- Destination: predicted crowd location at time t+Δt

- Map (grid): constructed by discretizing the environment based on terrain elevation

• Output: optimal trajectory (GPS waypoints) to the destination for UAVs and UGVs

Graph Search Algorithms Method Complexity Features

Dijkstra Exact cost from start point to any vertex n high Not fast enough

Best-First SearchEstimated cost from vertex n to the

destionationLow Not optimal

A* Total cost from start point to destinationt lowGuarantees the shortest

path in a reasonable time

• Implementation: Graph search algorithm (A*) with 8-point connectivity and multi objectives

37



Control Strategies associated with Motion Planning

• Given selected destination of UAV/UGV, find the path that

optimizes a certain combination of criteria

• The generic normalization formula

• Weighted average of the multiple objectives

(c) minimize the weighted

average of (a) and (b)

38

(a) minimize travel distance (b) minimize elevation

penalty (fuel consumption)



System Implementation

• Agent-based HIL simulation

• UAVs and UGVs

• Social force model and GIS



Agent-based Hardware-in-the-loop SimulationAgent-based Simulation

Repast Simphony

with 3D GIS

Wi-Fi / XBee PRO 900HP; APM one

Assembled UAV

(APM:Copter / Arducopter)

Assembled UGV

(APM:Rover / Ardurover)

Sensory Data

(e.g. GPS)

Control Commands

(MAVLink Messages)

Hardware Interface:

MAVproxy

Khaleghi, A. M., Xu, D., Lobos, A., Minaeian, S., Son, Y. -J., & Liu, J. (2013). Agent- based hardware-in-the-loop simulation for modeling UAV/UGV

surveillance and crowd control system. In Proceedings of the winter simulation conference 2013, Washington, DC, USA.



Assembled UAV (Arducopter), AR.Drone, X8+ UAV, and UGV

Navigate GPS waypoints autonomously using APM autopilot set (Arduino-based Autopilot- APM 2.5)

Motion Processing Unit (MPU-6000)

3-Axis Gyro

3-Axis Accelerometer

Microcontroller(ATMEGA2560)

Low-power Atmel 8-bit AVR RISC-based

256KB ISP flash memory

8KB SRAM

4KB EPROM

Throughput: 16 MIPS at 16MHz

Barometric pressure sensor

(SM5611)

GPS update rate: 5hz (5X per second)

Using GPS unit, the UAV has an outdoor navigation accuracy of about +/- 5 meters

Global Positioning System (GPS)



Agent-based Simulation Features

“The Great Circle Distance” (the shortest distance over the earth’s surface) between

any two points using haversine distant equation (Sinnott, 1984): d = Rc

R : Earth's radius

c = 2arctan(hav(c)

1- hav(c))

hav(c) = sin2(f

2-f

1

2)+cosf

1cosf

2sin2(

l2- l

1

2)

(f,l,h) (x, y, z)

42

• Environment enhancement to generate a terrain elevation grid using GIS

• Implementation

• Toolkit: Repast Simphony (Open source, Java, NASA World Wind SDK)

• Mapping GIS Environment to 3D Cartesian (convert to



Social force model for crowd motion

43

• Calculation of velocity for each agent given 1) desired

destination and 2) force (Helbing et al., 2000)

0 0

( )

( ) ( ) ( )i i i ii i ij iW

j i Wi

d v t t tm m

dt

v e vf f f

Desired vs. current Interaction

with other

agent

Force

against wall

Xi, H., and Y. Son (2010), An integrated pedestrian behavior model

based on extended decision field theory and social force model, In

Human-in-the-loop simulation: Methods and practice. eds. L.

Rothrock, and S. Narayanan: Springer (accepted)



Current Extension

• New challenges



3-Level Surveillance Framework

High altitude

level

(HAL)

Low altitude

level (LAL)

Surface level (SL)



High altitude level (HAL)

Electro-Optical/Infrared

(EO/IR)

Synthetic Aperture Radar

(SAR)

Low altitude level (LAL)

Remote Sensing

Surface level (SL)

Mobile Sensors

Fixed Sensors

3-Level Measurement System in Border Surveillance

Surveillance Camera

3-Levels Types Sensors Measurement Data

SAR

Image

EO/IR

Image

Lidar

Image

Thermal

Images

Magnetic

Data

Spectral

Image



A generic modeling framework of sensors for the surveillance application

Adjust the Detection

range;

Set the threshold

Receive signals /

messages from the

controller

Change location

parameters of sensors

in order for chasing foe

targets

Target appears in the

system

Surveillance Behavior

Sub-Model

Availability Sub-

Model

Signal Processing

Sub-Model

Agent Model of Sensors (Generic)



Bratman, 1987

Rao and Georgeff, 1998

Zhao and Son, 2008

Extended Belief-Desire-Intention Framework

Lee, S., Y.-J. Son, and J. Jin (2010), Integrated human decision making and planning model under extended belief-desire-intention framework,

ACM Transactions on Modeling and Computer Simulation, 20(4), 23(1)~23(24).



Time

Location

Route Choice En-Route Planning

Fastest Way

Rugged Road

Selection at

Departure

Hiding/Stopping

Sudden Direction

Change

Decision 1 Decision 2 Decision 3

Model of Drug Traffickers based on BDI framework

Behavioral Models of Drug Traffickers

Behavior Models of

Border Patrols

Environment Conditions:

weather, terrains3-Level Sensor Networks

Developed behavior models of drug traffickers and ground patrol agents

together with environmental conditions will provide richer scenarios



Summary and Ongoing Works

• Dynamic Data Driven Simulation based

Planning and Control

– Flexible shop floor control

– Surveillance via UAVs/UGVs (or Patrol Agent)

• Ongoing/future Works

– Different control architectures (more centralized;

more decentralized)

– Extension to 3 level hierarchy (UAVs, UGVs,

Aerostat)

– Team formations (rainy days; more mountainous

terrains)

50



QUESTIONS

• Young-Jun Son; [email protected]

• http://www.sie.arizona.edu/faculty/son/index.html

• 1-520-626-9530

mailto:[email protected]

http://www.sie.arizona.edu/faculty/son/index.html

DDDAMS-based Planning and Control - · PDF fileProgram Manager: Dr. Frederica Darema. Computer...

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