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Cooperative Control of UAVs. A mixed-initiative approach. Ltn. Elói Pereira Portuguese Air Force Academy E-mail: etpereira@emfa.pt. Summary. AFA project on UAVs; Cooperative Control of UAVs in mixed initiative environments; Military and Civil applications;

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Cooperative Control of UAVs


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    1. Cooperative Control of UAVs A mixed-initiative approach Ltn. Elói Pereira Portuguese Air Force Academy E-mail: etpereira@emfa.pt

    2. Summary • AFA project on UAVs; • Cooperative Control of UAVs in mixed initiative environments; • Military and Civil applications; • Formalism for Allocation and Exchange vehicles within teams; • Example: Load Balancing between teams; • Testbed description; • Conclusions and future work.

    3. ANTEX – Portuguese Air Force Project on UAVs • Development of UAVs platforms to use as technologies demonstrators in several fields as: • Scientific Research; • Defense; • Civil applications… • Give Air Force know how in operation of UAVs; • To promote R&D initiatives with others organizations: • Faculty of Engineering of Porto University; • Technical Lisbon University; • University of California at Berkeley; • University of Victoria; • University FAF Munich; • Ecoles d'officiers de l'Armée de l'air (internship of two cadets)

    4. Process Communicate Sense Execute Kill Plan Cooperatively Assess Kill Process Sense Cooperative Control of UAVs • Vehicles exchange information and commands in a network, changing their dependencies, states and mission roles to achieve a common goal; Source: [MICA Project]

    5. Sensors Mixed Initiative • Planning procedure and execution control must allow intervention by experienced human operators. • Essential experience and military insight of these operators cannot be reflected in mathematical models • It is impossible to design vehicle and team controllers that can respond satisfactorily to every possible contingency. In unforeseen situations, these controllers ask the human operators for direction. [Pravin et al.] The Commander is an actuator Plant Better Performance Better Decisions Better Info Decision & Control Commander/ Operator Battlespace Decision Aids Courses of Action Embedded Hierarchy Better status knowledge Measured status Estimation Source: [MICA Project]

    6. Military and Civil Applications • Research topic that has been attracting the attention of control, communications and computer science researchers; • Possible applications with large societal impact are raising interest outside the scientific community; • Military missions: • Combat; • Reconnaissance; • Surveillance; • Patrol; • Civil missions: • Forest inspections; • Security; • Environmental applications;

    7. Example: Strike Enemy Air Defenses (SEAD) mission • MICA – Mixed-Initiative Control of Automata Teams (DARPA); • Mission: Attack of the Blue force of UAV against Red's ground force of SAM sites and radars Primary targets sms14 sls7 sls8 sms11 Maneuvers: • Follow_path • Loitter • Attack_jam sms17 sls6 sms15 sms12 sls5 sms13 Blue base J. Borges de Sousa, T. Simsek e P. Varaiya, “Task planning and execution for UAV teams”, Proceedings of the Decision and Control Conference, Bahamas, 2004

    8. Example • Execution control Primary targets Team A sms14 Leg 6 sls7 sls8 sms11 Leg 1 sms17 sls6 sms15 Team B sms12 sls5 sms13 Blue base J. Borges de Sousa, T. Simsek e P. Varaiya, “Task planning and execution for UAV teams”, Proceedings of the Decision and Control Conference, Bahamas, 2004

    9. Example • Execution control Primary targets Attack segment sms14 Leg 6 sls7 sls8 sms11 Leg 1 sms17 sls6 sms15 sms12 sls5 sms13 Attack segment Blue base J. Borges de Sousa, T. Simsek e P. Varaiya, “Task planning and execution for UAV teams”, Proceedings of the Decision and Control Conference, Bahamas, 2004

    10. Example • Execution control Primary targets Attack segment sms14 Leg 6 Leg 7 sls7 sls8 Precedes Safe path sms11 Leg 1 sms17 sls6 Leg 2 sms15 sms12 Safe path sls5 sms13 Blue base Attack segment J. Borges de Sousa, T. Simsek e P. Varaiya, “Task planning and execution for UAV teams”, Proceedings of the Decision and Control Conference, Bahamas, 2004

    11. Example • Execution control Primary targets Subtask 2 Leg 8 sms14 Leg 6 Leg 7 sls7 sls8 Precedes sms11 Leg 1 Subtask 1 Leg 5 sms17 sls6 Leg 4 Leg 2 sms15 Leg 3 sms12 sls5 sms13 Blue base

    12. Formalism for Allocation and Exchange vehicles within teams teams vehicles • Matrix formalism: • Initial Allocation of vehicles to teams • Transition-vehicle incident matrix • Final Allocation of vehicles to teams • The formalism could be used to design high level controllers in mixed-initiative environments Decision variables Team-transition incident matrix

    13. Load-balancing algorithm • Load-balance the number of vehicles within teams; • Heterogeneous vehicles • Different fuel reserves; • Different number of weapons; • Different types of payloads; • … • Performance Measure: Difference between the number of vehicles in the team and the number of vehicles initially planned for that team; • Problem is solved as a Binary Integer Programming (BIP) optimization problem.

    14. Example: Load-balancing • Five teams with different necessities; • Fuel constraints;

    15. Actual UAV system configuration Autopilot Avionics Sensors Ground Station Payload Devices Servos Neptus Command and Control Interface (FEUP) Autopilot Avionics Sensors Payload Devices Servos

    16. Advanced Configuration - Work in progress • Autopilot manages low-level flight control • PC-104 for higher-level tasks (vision processing, trajectory planning, coordinated between UAVs) Sensors Servos Autopilot Avionics Payload Devices PC-104 Ground Station Sensors Servos Autopilot Avionics Payload Devices PC-104 Aircraft Low level control and logging Payload High level Control and logging

    17. Vehicles ANTEX-X02 (AFA) Silver Fox (ACR) NOVA (AFA) Flying Wing (AFA) ANTEX-X03 (AFA) Lusitânia (FEUP)

    18. Operation of UAVs and Cooperative control simulation

    19. Conclusions and future work • Cooperative control of UAVs is a research field with large margin of progression and with possible applications with societal impact (dull, dirty and dangerous missions); • The intervention of the operator in the planning and execution control (mixed-initiative) is crucial in missions with large uncertainty, namely in military operations; • ANTEX developments in a near short term: • Operation with several UAVs; • Track and follow structures (rivers, roads…) based on vision payloads; • Autonomous landing; • Mid-term objective: • Operation with others types of unmanned vehicles (underwater, surface).

    20. Questions? Thank you for your attention

    21. Lusitânia UAV (FEUP) Maximum Take Off Weight 10 kg Wing Span 2.4 m Payload 5 kg Endurance 0.75h On board payload: wireless video camera Nova UAV (AFA) Maximum Take Off Weight 4 kg Wing Span 1.6 m Payload 0.5 kg Endurance 0.75h Flying Wing UAV (AFA) Maximum Take Off Weight 3 kg Wing Span 1.6 m Payload 0.2 kg Endurance 0.3h ANTEX-M X03 (AFA) Wing Span 6 m Maximum Speed 130 km/h Stall Speed 40 km/h Maximum Take Off Weight 100 kg Payload 30 kg Engine 22 hp Endurance/Fuel Capacity 0.3h/4L ANTEX-M X02 (AFA) Maximum Take Off Weight 10 kg Wing Span 2.4 m Maximum Speed 151 km/h Payload 4 kg Endurance/Fuel Capacity 0.3h/0.2L Silver Fox (ACR) Maximum Take Off Weight 12.2 kg Wing Span 2.4 m Maximum Speed 203 km/h Payload 2.27 kg Endurance/Fuel Capacity 10h/2.6L Vehicles Characteristics