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

DLR.de • Chart 1 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 RPAS Swarms in Disaster Management Missions Efficient Deployment through Optimized Mission Planning Julia Zillies, Dagi Geister

DLR.de • Chart 2 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Introduction RPAS Deployment in Disaster Management Missions  Data collection and processing  Prompt situation assessment  Optimized mission planning Airborne Situation Assessment

> ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 DLR.de • Chart 3 Background HALE/MALE Remotely Piloted Aircraft Systems (RPAS) HALE Systems: High Altitude Long MALE Systems: Medium Altitude Endurance Long Endurance RQ-4 Global Hawk HERON RQ-1/MQ-  Capable to fly up to 35 hours 9 Predator without a break  Has an endurance of over 27 hours  Payload capacity of up to 1360 kg  1746 kg payload capacity HALE/MALE systems are capable to carry multiple sensors including electro optical, or infrared camera systems

DLR.de • Chart 4 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Introduction Examples of RPAS Use in Disaster Management Natural Disaster RQ-1/MQ-9 Predator/ Predator B RQ-4 Global Hawk − Operated over 157 hours − Hight quality images of hot spots Wildfires, Southern California (2007) − Infrared imagery − Thermal and infrared equipment − Flew 6 missions − Several Predator RPAS operated − Images on, ID (Internally Displaced from Puerto Rico Earthquake, Haiti People) Settlements − real-time full motion videos (2010) − Images of places where soldiers are to be − 24 hour a day coverage deployed [11]. − Over 20 missions in over 500 hours Damage assessment and nuclear power Earthquake and plant status - Tsunami, Japan (2011) − Synthetic Aperture Radar (SAR) − Infrared and electro optical camera systems [9].

DLR.de • Chart 5 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Working Environment Data Visualization and Mission Planning Data Processing U-Fly Research Ground Control Station Mission 3D Terrain Operation Visualization

DLR.de • Chart 6 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Methodology Swarming Unmanned Aircraft  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

DLR.de • Chart 7 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Methodology Database Incorporation  Up-to-date information  Availability and quality of already collected data  Coverage awareness  Include pre-disaster information Sensor Footprint

DLR.de • Chart 8 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Methodology Mission Planning A general Problem Formulation Given : 𝑊 non-identical RPAS ( 𝑤 𝑙 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 Find : Necessary number of vehicles, their payload profile, and feasible tours of minimal travel to all targets, respecting capacity constraints on targets.

DLR.de • Chart 9 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Area Search Methodology Mission Planning Optimization Problems  Task assignment  Payload planning Tracking  Path planning Desired Outcome  4D Trajectory Person Detection Task identification and allocation

DLR.de • Chart 10 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Methodology Mission Planning 4D Trajectory Generation Optimization Problems  Task assignment  Payload planning  Path planning Desired Outcome  4D Trajectory How to execute a task in a most efficient way? Scan Pattern Generation

DLR.de • Chart 11 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Methodology System Architecture Set of requests known at mission planning stage  Mission Planning Stage − All tasks are known a priori Mission Planning − Stochastic programming Cost Estimator Available RPA fleet approaches Task Assignment  Mission Execution Stage − Deal constantly with newly arising tasks Cost Path Estimator Planning − Online optimization approaches Retasking Request at mission Mission Execution execution stage

DLR.de • Chart 12 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Methodology Formation Flight Close Formation Flight  Potential to increase the operator vehicle ratio  Potential to reduce fuel consumption by drag reduction  Airspace integration Formation Landing Procedures UAV 1003 RQ-1 Global Hawk UAV 1002 RQ-1 Global Hawk UAV 1001 RQ-1 Global Hawk

DLR.de • Chart 13 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Summary Research Objectives Aim: Development of a set of optimization techniques for the cooperative mission planning and control of multiple RPAS.  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

DLR.de • Chart 14 > ICRAT 2014 > Julia Zillies • Doctorial Symposium > 30 May 2014 Thank You!