Smart COordination of UAV Teams (SCOUT): Intelligent Swarm Management. Dick Stottler,

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1 Smart COordination of UAV Teams (SCOUT): Intelligent Swarm Management Presented by: Dick Stottler,

2 Overview Stottler Henke Background Project Objectives SCOUT Description Prototype Domains Prototyping Activities Prototype Demonstration Description Prototype Demonstration Videos 2

3 Stottler Henke Background AI R&D Consulting Firm, founded in 1988 AI = Mimicking human thought processes in software 40 Top-Notch Professionals (All AI Degrees) Exclusively AI Projects - over 600 Clients: NASA, DoD, Commercial Hundreds of FIELDED Systems Variety of AI Techniques Problem-Solvers Tibbetts Award Winner World Class Intelligent Planning (Resource Allocation, Route Planning) page 3

4 Stottler Henke Related Projects Real-Time Aggressive Aircraft Maneuver Planning Aurora General Intelligent Planning and Resource Allocation Framework Numerous NASA and related domains (Shuttle/SLS Processing, Crew, 787 Dreamliner Production, Airborne SIGINT, AFSCN, SSN, missile defense (sensors and interceptors)) UAV ID Scheduling (Aurora) Wild Fire-Fighting Vehicles and Crew (Aurora) SimBionic/Graphical Behavior Transition Networks (BTNs) / High-Speed Intelligent Behaviors Real-time decision-making, Very large # of diverse domains Air Campaign Strategy, Naval Air Warfare Various Case-Based Reasoning Projects (Military Tactics, Enemy Tactical Behavior Explanation) page 4

5 SCOUT Project Objectives Ultimately, allow intelligent UAV team coordination and control Predictable, Robust, 1: Many (vs Many : 1), Contingencies Intelligent real-time planning, role allocation, path planning, adaptive behavior Intelligent UAV team coordination and control framework Explore and Understand SCOUT NASA Applications/Requirements Develop general, powerful, easy-to-apply SCOUT architecture Prototype SCOUT and an application (Search and X) Test prototype on 3 small UAVs (plus several virtual ) Note: Work transitioned from NASA to Air Force page 5

6 SCOUT On- Board Architecture page 6

7 SCOUT Overview 2017 Graphical Behavior Editor Graphical Sketch Editor (Optional) Create Plays (Different Nominals and Contingencies) Both Actions and Cognition (e.g. explicit planning steps) Optional Applicability constraints (for auto-reused plays) High-Level and UAV-Level Contingencies Loss of Engine Power -> Glide to safest landing location Loss of a vehicle -> replan (SAX: re-allocate, Network of sensors:?) Human or SCOUT can select play(s) Case-Based Reasoning (CBR, Applicability Constraints) Aurora to Allocate Roles Probabilistic Road Map Planning for 3-D routes Periodic Re-evaluation, Re-allocation, Re-route-planning page 7

8 SimBionic Experts & Developers Authoring Simulation SimBionic Editor Behavior Library SimBionic Engine Predicate & Action Vocabulary Interface Predicate & Action Code Simulation Engine page 8

9 SCOUT BTN Examples page 9

10 Example BTN: Search and Follow page 10

11 Handle Contingencies / Edited page 11

12 SCOUT s Role Allocation Architecture page 12

13 Prototype Domains Search (or Map) and X (when found) Sensor Network Air Force A2AD Multi-UAV SAR/EO/IR/Passive RF Autonomous SEAD page 13

14 Prototyping Activities Initial Testing: 3 UAVs under SimBionic Control, receiving sensor data, feedback loop Simple 3 UAV Search and X (inspect); No effort on smoothing UAV fight control Develop simple NASA World Wind display to view real-time GPS coordinates over planned routes Develop simple simulation in display; simulated UAVs have identical interface to real ones Very useful for safely testing More complex Search (Moving Target), Divvy up Sensor Network / Add Taxiing, Simultaneous Take-offs/Landings, Approvals; No HF/UI Design! page 14

15 Prototype Demo 2017 Multi-UAV SAR Search and X Static and/or Moving Targets Automatic Search Area Allocation / Route Planning Heterogeneous Search Speeds and Swaths Sensor Network / Progression (Approval) 2-D Array in 3-D 3-D Cross Challenges: Noisy Links (both ways), Noisy GPS and Altitude Sensor, Wind/Small, Short Comm Distances, Limited Battery, Noisy + Short Mitigations: Default Behavior; Sanity Checking; Smoothing; Low Maximum Velocity Contingencies Real: Link Loss, Noisy GPS and Altitude Sensor Simulated: Link Loss, Low Battery Levels page 15

16 Prototype Demo Videos Simple Display for Monitoring Simple Simulation with mix of real and simulated UAVs Multi-UAV SAR Search and X (Examine) Sensor Network / Progression (Approval) 3-D Cross 2-D Array in 3-D Taxi to Take-off, Simultaneous Take-off, Execute Flight Card(s), Simultaneous Land, Taxi In, Assignments based on drone constraints Contingencies to Simulated Problems (and edit BTN, replay) Link Loss Bingo Battery Level Emergency Battery Level Edit Contingencies Behavior and Rerun Simple Mixed (Real and Simulated Drones) Scenario page 16