RPAS Swarms in Disaster Management Missions

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1 DLR.de Chart 1 RPAS Swarms in Disaster Management Missions Efficient Deployment through Optimized Mission Planning Julia Zillies, Dagi Geister

2 DLR.de Chart 2 Introduction RPAS Deployment in Disaster Management Missions Data collection and processing Prompt situation assessment Optimized mission planning Airborne Situation Assessment

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

4 DLR.de Chart 4 Introduction Examples of RPAS Use in Disaster Management Natural Disaster RQ-4 Global Hawk RQ-1/MQ-9 Predator/ Predator B 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]. -

5 DLR.de Chart 5 Working Environment Mission Planning Data Visualization and Data Processing U-Fly Research Ground Control Station Mission Operation 3D Terrain Visualization

6 DLR.de Chart 6 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

7 DLR.de Chart 7 Methodology Database Incorporation Up-to-date information Availability and quality of already collected data Coverage awareness Include pre-disaster information Sensor Footprint

8 DLR.de Chart 8 Methodology Mission Planning A general Problem Formulation Given: V non-identical RPAS (v k for k {1,, V} ) of limited capacity initially located at depot S sources (for i {1,, S} ), with limited resources G targets (goal points) (g i for i {1,, G}), 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.

9 DLR.de Chart 9 Methodology Mission Planning Area Search Optimization Problems Task assignment Payload planning Path planning Tracking Desired Outcome 4D Trajectory Task identification and allocation Person Detection

10 DLR.de Chart 10 Methodology Mission Planning Optimization Problems 4D Trajectory Generation Task assignment Payload planning Path planning Desired Outcome 4D Trajectory How to execute a task in a most efficient way? Scan Pattern Generation

11 DLR.de Chart 11 Methodology System Architecture Mission Planning Stage All tasks are known a priori Stochastic programming approaches Mission Execution Stage Deal constantly with newly arising tasks Online optimization approaches Set of requests known at mission planning stage Available RPA fleet Mission Planning Cost Estimator Retasking Cost Estimator Task Assignment Path Planning Request at mission execution stage Mission Execution

12 DLR.de Chart 12 Methodology Formation Flight Close Formation Flight Potential to increase the operator vehicle ratio Potential to reduce fuel consumption by drag reduction Airspace integration UAV 1003 RQ-1 Global Hawk UAV 1002 RQ-1 Global Hawk Formation Landing Procedures UAV 1001 RQ-1 Global Hawk

13 DLR.de Chart 13 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

14 DLR.de Chart 14 Thank You!