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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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Cooperative control of uavs l.jpg

Cooperative Control of UAVs

A mixed-initiative approach

Ltn. Elói Pereira

Portuguese Air Force Academy

E-mail: etpereira@emfa.pt


Summary l.jpg

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.


Antex portuguese air force project on uavs l.jpg

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)


Cooperative control of uavs4 l.jpg

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]


Mixed initiative l.jpg

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]


Military and civil applications l.jpg

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;


Example strike enemy air defenses sead mission l.jpg

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


Example l.jpg

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


Example9 l.jpg

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


Example10 l.jpg

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


Example11 l.jpg

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


Formalism for allocation and exchange vehicles within teams l.jpg

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


Load balancing algorithm l.jpg

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.


Example load balancing l.jpg

Example: Load-balancing

  • Five teams with different necessities;

  • Fuel constraints;


Actual uav system configuration l.jpg

Actual UAV system configuration

Autopilot

Avionics

Sensors

Ground

Station

Payload

Devices

Servos

Neptus Command and Control Interface (FEUP)

Autopilot

Avionics

Sensors

Payload

Devices

Servos


Advanced configuration work in progress l.jpg

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


Vehicles l.jpg

Vehicles

ANTEX-X02 (AFA)

Silver Fox (ACR)

NOVA (AFA)

Flying Wing (AFA)

ANTEX-X03 (AFA)

Lusitânia (FEUP)


Operation of uavs and cooperative control simulation l.jpg

Operation of UAVs and Cooperative control simulation


Conclusions and future work l.jpg

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).


Questions l.jpg

Questions?

Thank you for your attention


Vehicles characteristics l.jpg

Lusitânia UAV (FEUP)

Maximum Take Off Weight10 kg

Wing Span2.4 m

Payload5 kg

Endurance 0.75h

On board payload: wireless video camera

Nova UAV (AFA)

Maximum Take Off Weight4 kg

Wing Span1.6 m

Payload0.5 kg

Endurance 0.75h

Flying Wing UAV (AFA)

Maximum Take Off Weight3 kg

Wing Span1.6 m

Payload0.2 kg

Endurance 0.3h

ANTEX-M X03 (AFA)

Wing Span6 m

Maximum Speed130 km/h

Stall Speed40 km/h

Maximum Take Off Weight100 kg

Payload30 kg

Engine22 hp

Endurance/Fuel Capacity0.3h/4L

ANTEX-M X02 (AFA)

Maximum Take Off Weight10 kg

Wing Span2.4 m

Maximum Speed151 km/h

Payload4 kg

Endurance/Fuel Capacity0.3h/0.2L

Silver Fox (ACR)

Maximum Take Off Weight 12.2 kg

Wing Span2.4 m

Maximum Speed 203 km/h

Payload 2.27 kg

Endurance/Fuel Capacity 10h/2.6L

Vehicles Characteristics


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