RPAS Swarms in Disaster Management Missions Efficient Deployment - - PowerPoint PPT Presentation

RPAS Swarms in Disaster Management Missions

Julia Zillies, Dagi Geister

> ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 DLR.de • Chart 1

Efficient Deployment through Optimized Mission Planning

Introduction RPAS Deployment in Disaster Management Missions

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• Data collection and processing
• Prompt situation assessment
• Optimized mission planning

Airborne Situation Assessment

Background HALE/MALE Remotely Piloted Aircraft Systems (RPAS)

HALE Systems: High Altitude Long Endurance

• Capable to fly up to 35 hours

without a break

• Payload capacity of up to 1360 kg

> ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 DLR.de • Chart 3

MALE Systems: Medium Altitude Long Endurance

• Has an endurance of over 27 hours
• 1746 kg payload capacity

RQ-4 Global Hawk HERON RQ-1/MQ- 9 Predator

HALE/MALE systems are capable to carry multiple sensors including electro

• ptical, or infrared camera systems

Natural Disaster

Wildfires, Southern California (2007) − Operated over 157 hours − Infrared imagery − Hight quality images of hot spots − Thermal and infrared equipment Earthquake, Haiti (2010) − Flew 6 missions − Images on, ID (Internally Displaced People) Settlements − Images of places where soldiers are to be deployed [11]. − Several Predator RPAS operated from Puerto Rico − real-time full motion videos − 24 hour a day coverage Earthquake and Tsunami, Japan (2011) − Over 20 missions in over 500 hours Damage assessment and nuclear power plant status − Synthetic Aperture Radar (SAR) − Infrared and electro optical camera systems [9].

• Introduction

Examples of RPAS Use in Disaster Management

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RQ-4 Global Hawk RQ-1/MQ-9 Predator/ Predator B

Working Environment

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3D Terrain Visualization Mission Planning Mission Operation Data Visualization and Data Processing Research Ground Control Station U-Fly

Methodology Swarming Unmanned Aircraft

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• Centralized control means to

disseminate all information to a central decision-maker

• In decentralized or hierarchical

control schemes, team members can exchange directly state information and collected data

Methodology Database Incorporation

• Up-to-date information
• Availability and quality of

already collected data

• Coverage awareness
• Include pre-disaster information

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Sensor Footprint

Methodology Mission Planning

A general Problem Formulation Given: Find:

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𝑊 non-identical RPAS (𝑤𝑙 for 𝑙 ∈ {1, … , 𝑊} ) of limited capacity initially located at depot 𝑇 sources (for 𝑗 ∈ {1, … , 𝑇} ), with limited resources 𝐻 targets (goal points) (𝑕𝑗 for 𝑗 ∈ {1, … , 𝐻}), with known demand and specific service time window Necessary number of vehicles, their payload profile, and feasible tours of minimal travel to all targets, respecting capacity constraints on targets.

Methodology Mission Planning

Optimization Problems

• Task assignment
• Payload planning
• Path planning

Desired Outcome

• 4D Trajectory

Task identification and allocation

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Area Search Tracking Person Detection

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Methodology Mission Planning

Optimization Problems

• Task assignment
• Payload planning
• Path planning

Desired Outcome

• 4D Trajectory

How to execute a task in a most efficient way? 4D Trajectory Generation Scan Pattern Generation

Methodology System Architecture

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Available RPA fleet Mission Planning Task Assignment Path Planning Cost Estimator Mission Execution Request at mission execution stage Retasking Cost Estimator Set of requests known at mission planning stage

• Mission Planning Stage

− All tasks are known a priori − Stochastic programming approaches

• Mission Execution Stage

− Deal constantly with newly arising tasks − Online optimization approaches

Methodology Formation Flight

• Potential to increase the operator

vehicle ratio

• Potential to reduce fuel

consumption by drag reduction

• Airspace integration

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Close Formation Flight Formation Landing Procedures

UAV 1003

RQ-1 Global Hawk

UAV 1002

RQ-1 Global Hawk

UAV 1001

RQ-1 Global Hawk

• Analysis of past disastrous events
• Investigation of existing research and solution approaches in the field of RPAS

swarming

• Realistic scenario design to create a test-bed for the developed solutions
• Mathematical formulation of identified optimization problems
• Development and examination of different algorithms
• Report of simulation results

Summary Research Objectives

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Aim: Development of a set of optimization techniques for the cooperative mission planning and control of multiple RPAS.

Thank You!

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