Operateurassistenz und teil-automome Fähigkeiten als Schlüssel zur Führung mehrerer kooperierender UAVs

Transcription

1 Operateurassistenz und teil-automome Fähigkeiten als Schlüssel zur Führung mehrerer kooperierender UAVs Universität der Bundeswehr München (UniBwM) Institut für Systemdynamik und Flugmechanik (ISF) DGLR Workshop Führungsaspekte unbemannter Luftfahrzeuge: Technologien und die Einbeziehung des Operateurs Neubiberg, 14./15. Februar 2007 UniBwM / LRT

2 Different Approaches to Multiple UAV Guidance Artificial Cognitive Unit (ACU) Commands Tasks Data Results? UAV Ground Operators UAV Ground Operator 2

3 Contents Analysis of UAV Guidance Set-Ups by Using the Work System as Theoretical Framework Semi-Autonomous Systems Assistant Systems Experimental Facilities Summary 3

4 Contents Analysis of UAV Guidance Set-Ups by Using the Work System as Theoretical Framework Semi-Autonomous Systems Assistant Systems Experimental Facilities Summary 4

5 Work System Set-Up: Multiple UAV Guidance information & resources Human UAV Operator Multiple UAVs ns tio c tru ins mission order mission progress instructions ins tru ctio ns mission result ible! s s o Imp 5

6 Work System Set-Up: Multiple UAV Guidance information & resources Human UAV Operator AC U Semi-autonomous Systems ks tas mission order mission progress tasks tas ks mission result ing! d n a dem Very 6

7 Work System Set-Up: Multiple UAV Guidance information & resources Human UAV Operator mission order Cooperative ACU Semi-autonomous System mission progress tasks mission result ing! d n a Dem 7

8 Work System Set-Up: Multiple UAV Guidance information & resources Operating Team Semi-autonomous System assistant system Human-ACU Co-operation mission order Supervision ACU-ACU Co-operation mission progress tasks mission result! d o o sg Look 8

9 System of Work Systems pilot assistant system CASSY Cockpit Assistant System CAMA Crew Assistant Military Aircraft PILAS Piloten Assistenzsystem MiRA Military Rotorcraft Assosicate mission objective reconnaissance demands Co²SiMA Cognitive and Co-operative System for intelligent Mission Accomplishment MUM-T Manned-Unmanned Teaming operator assistant system mission objective COSYflight Cognitive System for the flight domain COSYteam Cognitive System for co-operative teaming Semiautonomous cooperative system 9

10 Assistant System vs. Semi-autonomous System Assistant System Pursues overall work objective Co-operates with other elements of the operating force Provides situation-dependent intervention (e.g. task adoption) => Is Operating Element (as part of the OF) Semi-autonomous System Needs to be tasked by supervising authority Is responsible for well-defined task Is able to perform that task with no or little human intervention => Is Automation (as part of the OSM) Artificial Cognition Interpretation Input Interface Belief Environment CP behaviour = ACU behaviour Input Data Goal Determination Environment Models Desires situational knowledge a-prioriknowledge Models of Action Alternatives Instruction Models Instructions Plan Goals Might be a realisation approach for both Planning Output Interface Plan Execution 10

11 The Cognitive Process Interpretation Input Interface Belief Environment observable behaviour of CP = ACU behaviour Input Data Goal Determination Environment Models Desires situational knowledge a-prioriknowledge Models of Action Alternatives Goals Instruction Models Instructions Plan Planning Output Interface Scheduling 11

12 The Cognitive Process body Environment body a-prioriknowledge body = contains the data a-priori knowledge situational knowledge (= cognitive yield) 12

13 The Cognitive Process transformer Transformer InputKnowledge OutputKnowledge Processing Knowledge Environment body a-prioriknowledge transformer = represent functions reading in whole body (mainly in one area) writing in designated areas use of models 13

14 The Cognitive Process Interpretation Input Interface Belief Environment CP behaviour = ACU behaviour Input Data Goal Determination Environment Models Desires situational knowledge a-prioriknowledge Models of Action Alternatives Goals Instruction Models Instructions Plan Planning Output Interface Plan execution 14

15 Contents Analysis of UAV Guidance Set-Ups by Using the Work System as Theoretical Framework Semi-Autonomous Systems Assistant Systems Experimental Facilities Summary 15

16 COSYteam Scenario UAV-Team known threat target pop-up threat FLOT 16

17 Co-operative Mission Accomplishment single vehicle multiple vehicles / group Single Vehicle Mission Accomplishment Co-operative Mission Accomplishment Use of Onboard Automation Safe Flight behaviour control flight management 17

18 Co-operative Mission Accomplishment Common Objective Team Team member Resource Dialogue Capability Commitment Form a team Interpretation Input Messages from Interface team members Messages to team membersoutput observable behaviour of CP Environment Belief Input Data Goal Determination Environment Models Communicate Desires situational knowledge a-prioriknowledge Models of Action Alternatives Instruction Models Instructions Plan Interface Update of internal belief Achieve common objective Scheduling Dialogue management Goals Query informatio n Planning Request task accomplishment Propose task accomplishment Commit Inform Request cancel Propose cancel Drop commitment 18

19 Desires for Co-operation Common Objective Team Basic Desire Co-operation Messages from team members Team member Commitment Resource Dialogue Capability Interpretation Input Interface Messages to team membersoutput observable behaviour of CP Desires a-prioriknowledge Models of Action Alternatives Instruction Models Instructions Scheduling Dialogue management Plan Goals Support team members Planning Propose task accomplishment Request task accomplishment Query information Form a team Achieve common objective Complete tasks Goal common Determiobjective nation Environment Models situational knowledge Interface Update of internal belief Achieve Belief Input Data Environment Balance work Request cancel Drop commitment Inform Communicate Continue dialogue Know team members Support team members Know resources Know capabilities Know commitments Know opportunities Comply with commitments Keep team members informed Ensure task coverage Work efficiently Balance workload Avoid redundant task completion Do not have unachievable commitments Do not overcharge team member Do not have commitments to completed tasks Involve all team members Do not have commitments without reason Claudia Meitinger, Michael Kriegel & Axel Schulte Have spokesman 19

20 Action Alternatives for Co-operation Common Objective Team Messages from team members Team member Commitment Resource Dialogue Capability Interpretation Input Interface Messages to team membersoutput observable behaviour of CP Desires a-prioriknowledge Models of Action Alternatives Instruction Models Instructions Scheduling Dialogue management Query information Inform Request task accomplishment Request cancel Plan Complete tasks Goal common Determiobjective nation Environment Models situational knowledge Interface Update of internal belief Achieve Belief Input Data Environment Goals Support team members Planning Propose task accomplishment Request task accomplishment Query information Balance work Request cancel Drop commitment Inform Information Exchange Task Assignment Delegation Adoption Propose cancel Propose task accomplishment Drop commitment Commit Task Assignment missing Task Assignment unwanted 20

21 Example Attack of 1 SAM-site by 2 UAVs 21

22 Activation of Goal <belief> <belief> commitment actor actor-self responsible commitment responsible task responsible commitment responsible task task task 0 self <belief> avoid-redundanttask-completion avoid-redundanttask-completion task actor <belief> task-destroy task-destroy object <goal> actor-0 sam-site object sam-site id = 0 task actor 22

23 Action Alternatives for Redundant Task Completion <goal> avoid-redundanttask-completion <plan> propose-cancel avoid-redundanttask-completion task effects actor propose-cancel task to task actor task to <plan> request-cancel effects request-cancel task from selected-option = t from task <belief> <belief> task-destroy actor <belief> actor-0 actor-self <belief> distance task-destroy object sam-site object sam-site id = 0 distance actor object distance-2d = 40. distance actor object distance-2d =

24 After Goal Achievement <belief> <belief> commitment actor actor-self commitment responsible task responsible task <belief> <belief> task-destroy f sel 0 sam-site task-destroy object object sam-site id = 0 24

25 Mission know n th reat t Targe pop-up threat Atta ck U AV SEA D SEA UAV 0 D SEA UAV 1 DU AV 2 SEA DU AV 3 25

26 Mission Demo im Foyer! 26

27 Contents Analysis of UAV Guidance Set-Ups by Using the Work System as Theoretical Framework Semi-Autonomous Systems Assistant Systems Experimental Facilities Summary 27

28 How shall an Assistant System behave? Basic Requirements for Human-Automation Co-operation 1st Basic Requirement Automation needs to obtain situation awareness for itself first Within the presentation of the full picture of the flight situation it must be ensured that the attention of the cockpit crew is guided towards the objectively most urgent task or subtask of that situation. Automation needs to be interactive and communicate with the human operator Automation needs to know high-level goals 2nd Basic Requirement Automation needs capability to recognise when human operator is overcharged A situation with overcharge of the human operator might come up even when situation awareness has been achieved. In this case the automation has to transfer the situation into a normal one which can be handled by the operator in a normal manner. Automation needs to feature co-operative behaviours of co-ordination & task allocation Automation needs capabilities to autonomously perform mission tasks 28

29 Fundamental Questions of Adaptive Automation Question 1 How can the workload of the human operator, which determines the allocation of functions between human and machine, be perceived by an assistant system? Question 2 Which alternatives does an assistant system have to support the operator in case critical workload has been observed? 29

30 Q1: How can workload be perceived? (1) External events new mission order / contingencies / threats / hazards (2) Observations & Measurements + - Task Performance (tracking accuracy / response times / errors) - Psycho-physiological Measurements (EEG, eye movements) (3) Models & Predictions + - Task Models (anticipation of objective work demands) - Workload Models (estimation of subjective workload) 30

31 Q2: How can automation adapt? Solution 1 Increase available resources of operator at constant workload available resources required resources t identified, critical behaviour Possible areas of application: Monotonous Tasks short-term: activating stimulus long-term: variation of task Fatigue short-term: activating stimulus long-term: not possible break is necessary 31

32 Q2: How can automation adapt? Solution 2 Reduce workload posed on human operator available resources required resources t identified, critical behaviour Possible areas of application: long-term high workload high task complexity Reduction of workload by guidance of attention presentation of information adapted to available resources reduction of task complexity adoption of subtasks 32

33 Contents Analysis of UAV Guidance Set-Ups by Using the Work System as Theoretical Framework Semi-Autonomous Systems Assistant Systems Experimental Facilities Summary 33

34 Co2SiMA Cognit ive & Co-oper at ive Syst em 2 Co ima f or int elligent Mission Accomplishment ground control station design H/C UAV mobile ground control station Multiple UAV missions in surrogate scenario Operator assistance UAV guidance & control Machine-machine collaboration 34

35 Mobile Ground Control Station Operator workplace Vehicle transportation Onboard power supply 35

36 Control Station Flight control unit Touchscreen Displays Control & Display Unit Throttle Controls Stick Controls 36

37 Fixed Wing UAV Static pressure sensor Total pressure sensor Integrated flight control and flight management system Technical data: Wing span: Weight: Flight endurance: 3,20 m 14,8 kg (RC - Version) approx. 50min Length: Propulsion: Payload: 2,25 m King 100ccm Boxer ca. 10 kg 37

38 Rotorcraft UAV GPSantenna Camera Telemetryantennas Technical data: Rotor diameter: Weight: Flight endurance: Avionic Box AHRS DGPS FCC Telemetry 1,78 m 12,4 kg (incl. avionic box) approx. 30 min Demo Length: 1,63 mim Foyer! Propulsion: ZG 23ccm Payload: approx. 6 kg 38

39 Contents Analysis of UAV Guidance Set-Ups by Using the Work System as Theoretical Framework Semi-Autonomous Systems Assistant Systems Experimental Facilities Summary 39

40 Summary information & resources Human UAV Operator Work System Analysis of Multiple UAV Guidance Set-Up mission order Semi-autonomous System k tas task mission progress task mission result Semi-Autonomous Systems: Demonstration of Cooperative Capabilities Assistant Systems: Basic Questions of Adap-tive Automation and Approaches to Solution Experimental Facilities: Integration of Assistant Systems and Semi-Autonomous Systems 40

41 Desires for Co-operation Common Objective Team Messages from team members Team member Commitment Resource Dialogue Capability Interpretation Input Interface Form a team Know team members Support team members Know resources Continue dialogue Know capabilities Keep team members informed Know commitments observable behaviour of CP Basic Desire Co-operation Messages to team membersoutput Desires a-prioriknowledge Models of Action Alternatives Instruction Models Instructions Scheduling Dialogue management Plan Complete tasks Goal common Determiobjective nation Environment Models situational knowledge Interface Update of internal belief Achieve Belief Input Data Environment Goals Support team members Planning Propose task accomplishment Request task accomplishment Query information Balance work Request cancel Drop commitment Inform Achieve common objective Balance work Do not overcharge team members Involve all team members Complete tasks Comply with commitments Ensure task coverage Avoid redundant task completion Know opportunities 41