Agent-based Simulation for UAV Swarm Mission Planning and Execution

Yi Wei, Greg Madey, University of Notre Dame M. Brian Blake, University of Miami

SpringSim’13, San Diego, CA April 2013

Outline

•  Introduction and Motivations

•  Problem Definition

•  Technical Approaches and Evaluations

•  Conclusion and Future Work

2

Introduction: Unmanned Airborne Vehicles

•  A UAV is an aircraft that do not require on-

board pilots.

•  Usually controlled remotely or by an

autonomous computer.

•  Cheaper than their piloted counterparts, but

also with limited capabilities. 3

Applications of UAVs

4

journalism

highway monitoring

hunting

real estate sale

From single UAV to a swarm

•  Prices decrease while capabilities increase

•  UAV swarms for future airborne operations

•  Current control approaches have limited

scalability

•  New models and approaches required to fly the

swarm, not individual UAVs 5

The Problem

Schedule missions onto a swarm of

UAVs, monitor their execution, and

make necessary adjustments.

6

Mission Planning Example

T1

T2

T3

Mission Swarm

V1

V2

V3

V4

Definitions

•  UAV: basic unit

•  Swarm: a set of UAVs

•  Task: simple, specific objective

•  Mission: set of interdependent tasks

8

Agent-based Approach

•  A single Swarm Control Agent (SCA) for

mission planning

•  Multiple UAV Agents (UAs) for mission

scheduling

•  Agents communicate through messages 9

Global-Local Hybrid Planning

•  SCA assigns tasks to different UAs based on

its global knowledge and other constraints

•  Each UA schedules new tasks based on local

state

•  UAs periodically update the SCA about their

status 10

Message Types

1.  New mission

2.  New task

3.  Status request/return

4.  Task completion

5.  Task reassignment 11

Different Scheduling Policies

•  First Come First Serve

•  Insertion

•  Traveling Salesman

•  Adaptive

12

Simulation Program

•  Implemented as a Java multi-thread application

•  The SCA and all UAs are represented as

threads

•  Missions and the swarm are visualized during

execution

13

14

Simulation Result

15

0

0.2

0.4

0.6

0.8

1

0 100 200 300 400 500

Rel

ativ

e Pe

rfor

man

ce

Task Arrival Rate (steps/task)

Insertion TSP Adaptive

Simulation Result

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0.05

0.1

0.15

0.2

0.25

0.3

50 100 150 200 250 300 350 400 450 500

Rel

ativ

e Pe

rfor

man

ce

Number of Tasks Insertion TSP Adaptive

Conclusion •  The adoption of swarm based UAV operations

require new control models and algorithms

•  An agent-based approach for swarm mission

planning is introduced

•  Global-local hybrid approach is employed to

facilitate planning process 17

Future Work

1.  Incorporate more realistic scenarios, such as

UAV losing contact to the ground station

2.  Incorporate more task types and task

dependency types

3.  Development of an expressive mission

specification language 18

Thank You!

Questions?

19