On-the-fly Route Planning for Mono-UAV Surveillance Missions

Transcription

1 On-the-fly Route Planning for Mono-UAV Surveillance Missions M. Soulignac1 1 UK PlanSIG'13 F. Gaillard1 ISEN-Lille 2 C. Dinont1 G. Marchalot2 THALES Airborne Systems Edinburgh, 29 January 2014

2 Mono-UAV Surveillance Mission Planning Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

3 A Mono-UAV surveillance mission Detected target Identified target UAV Non-detected target Targets (unknown position and route) Identification range Maximal targets velocity Detection range known a priori 3

4 Mission Planning pd : % of detected targets pi : % of identified targets Expected output pd near 100% and pi near 100% Beginning of the mission End of the mission 4

5 Our approach Projection on Pattern (POP) UAV route Sweeping Pattern Local deviations => pd high => maximize pi and maintain pd high 5

6 The Flight Pattern Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

7 Basic Sweeping Pattern (For static targets) repetitions of the pattern area to cover H backward rd flight pattern forward 7

8 Basic Sweeping Pattern (For static targets) backward Non-detected target T forward 8

9 Basic Sweeping Pattern (For static targets) backward T Detected target forward A target missed during the forward move is ensured be detected during the backward move 9

10 Enhanced Sweeping Pattern (For moving targets) backward Non-detected target T vt forward 10

11 Enhanced Sweeping Pattern (For moving targets) backward vt T Non-detected target forward 11

12 Enhanced Sweeping Pattern (For moving targets) Pattern narrowing backward T forward Detected target dt = f(αv ) with the velocity ratio between UAV and targets (see our paper for the expression of f and a sketch of proof) 12

13 Back to our example = 180 knots Situation at the beginning of the mission = 7 knots Critical targets 13

14 Back to our example Situation after executing one instance of the pattern Critical targets All the targets have been detected (and the targets Ti at a distance to the pattern have been identified) 14

15 POP : Projection On Pattern Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

16 Route Planning Problem T2 T1 How to investigate T1 and T2 while following the flight pattern? 16

17 Targets projection Target T1 Distance to the pattern Projected target P13 T T P P Arc length on the pattern 17

18 Targets projection Target T T T P

19 Targets projection Target T P T2 P T P

20 Targets projection Target T P T T

21 Targets insertion W3 P2 T2 W2 T1 P W Flight pattern (copy) Name Arc length W1 W2 W3 Projected targets increasing order of arc length P1 P

22 Targets insertion W3 P2 T2 W2 T1 P W Flight pattern (copy) Name Arc length W1 W2 W3 Projected targets increasing order of arc length insertion preserving order P1 P

23 Targets insertion W3 P2 T2 W2 T1 P W Updated UAV route P W1 W2 P2 W

24 Targets insertion Postponed identification task W3 P2 T2 W2 T1 P W Updated UAV route P W1 W2 P2 W

25 Result on an entire mission pd = 100 % pi = 99.1 % POP Beginning of the mission End of the mission 25

26 Result on an entire mission The UAV route can be improved 3 enhancements proposed End of the mission 26

27 Enhancements Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

28 Target move anticipation Without T is projected on the closest part of the pattern, regardless of its heading. T T resulting flight plan target projection 28

29 Target move anticipation Without Executed trajectory to identify T : B T = 180 knots = 6 knots A Elapsed time from A to B : 9753 s. 29

30 Target move anticipation With Estimated UAV position at interception time2 Estimated target position at interception time1 target projection resulting flight plan T T 1 assuming a rectilinear motion of 2 assuming a strict pattern following 30

31 Target move anticipation With Executed trajectory to identify T : B T = 180 knots = 6 knots A Elapsed time from A to B : 9638 s (previously 9753) Cost reduction : 1% 31

32 Amplified Pattern Narrowing Without Repeated deviations UAV delay Missed targets Missed target 32

33 Amplified Pattern Narrowing With No narrowing Normal narrowing Amplified narrowing Detected target (previously missed) 33

34 Amplified Pattern Narrowing With No narrowing Normal narrowing dt = f(α 'v ) Amplified narrowing Detected target (previously missed) α 'v = g(α v, D) ; g adjusts α v according to the UAV delay D (see our paper for the expression of g) 34

35 Local TSP Without Identifying targets by increasing arc length oscillations Wnext Distance to Wnext : 296 NM 35

36 Local TSP Without Identifying targets by increasing arc length oscillations Wnext TSP tour to improve Distance to Wnext : 296 NM 36

37 Local TSP With Distance to Wnext : 229 NM (previously 296) Wnext Tour improved by 2-opt Cost reduction : 23% 37

38 Demo Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

39 Demo POP within the multi-agent platform APM(Robot) 39

40 Simulation results Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

41 Experimental protocol L = 200 NM H= 200 NM Targets density about 40 targets about 120 targets Low density High density 41

42 Experimental protocol Parameter variants 42

43 Computation time Sensibility to density Low density High density 43

44 Computation time Sensibility to density Parameter variants Computation time under 10 ms in 99.9% of simulations Computation time under 10 ms in 98.1% of simulations Computation time mostly under 10 ms even with : - a high density of targets - all enhancements enabled 44

45 Highest values for pd + pi And corresponding parameter variants Low density High density 45

46 Values of vt and ri leading to pd + pi > 190% pd + pi > 190% pd + pi < 190% ri (NM) vt (knots) Low density vt (knots) High density 46

47 Conclusions and perspectives Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

48 Presented work POP (Projection On Pattern) = Enhanced (i.e. narrowed) sweeping pattern + Target projections on this pattern + Target identification tasks ordered by increasing values of arc length 3 enhancements : target move anticipation amplified narrowing local TSP 48

49 Conclusions of simulation results The best combination of enhancements depends on the context. Enabling all enhancements does not necessarily lead to the best performances. High detection and identification performances can be obtained for reasonable targets speeds and identification range Computation time is mostly under 10ms, allowing on-thefly replanning 49

50 Ongoing works Extension to multiple cognitive UAVs Dynamic identification tasks allocation Encouraging results with mtsp algorithms 50

51 End of talk Thanks for your attention. Slides available on my webpage. 51

52 Appendices Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

53 Enhanced Sweeping Pattern Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

54 Enhanced Sweeping Pattern Computation of dt Detected target 54

55 Enhanced Sweeping Pattern Computation of dt Detected target with 55

56 Enhanced Sweeping Pattern Computation of dt Root 2 Root 1 Keep the smallest root Root 1 Downward move Root 2 Upward move 56

57 Enhanced Sweeping Pattern Lower bound for Maximal narrowing No narrowing (basic pattern) The UAV must be faster than the targets 57

58 Amplified Pattern Narrowing Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

59 Amplified Pattern Narrowing Principle overcost Estimated arrival at T' = T + vuav Arrival at T l3 l2 overcost l1 Situation 1 : Strict pattern following Situation 2 : Pattern following + local deviations The situation 2 is equivalent to the situation 1 with a UAV speed v'uav < vuav such that : v'uav = l1 + l2 + l3 T' 59

60 Amplified Pattern Narrowing Principle Amplified pattern narrowing consists in using the velocity ratio : estimated end of the mission with deviations estimated end of the mission with deviations instead of in the pattern narrowing process. ( dt is computed by dt = f(α 'v ) ) simulates a slower UAV relatively to targets (or faster targets relatively to the UAV). 60

61 Local TSP Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

62 Local TSP Tour improver Small TSP tours (about 10 cities in average for a high density of targets) The choice of the tour improver has no significant impact on the solution quality Our choice : 2-opt - Easy to implement and customize - Excellent response time (to be seen in the simulation results) - Solution at 0.007% of optimal* * measured on cities TSP tours. The optimal solutions have been computed using branch and bound with the Held-Karp relaxation. 62

63 Local TSP 2-opt 2-opt improves a given tour by performing 2-edge flips. The resulting tour is guaranteed to be non-self-crossing. Nearest Neighbor Tour 63

64 Local TSP 2-opt 2-opt improves a given tour by performing 2-edge flips. The resulting tour is guaranteed to be non-self-crossing. Step 1 64

65 Local TSP 2-opt 2-opt improves a given tour by performing 2-edge flips. The resulting tour is guaranteed to be non-self-crossing. Step 2 65

66 Local TSP 2-opt 2-opt improves a given tour by performing 2-edge flips. The resulting tour is guaranteed to be non-self-crossing. (... ) Step 9 66

67 Local TSP 2-opt 2-opt improves a given tour by performing 2-edge flips. The resulting tour is guaranteed to be non-self-crossing. Step 10 67

68 Target move anticipation Interpretation Projection without anticipation (projection on pattern) Projection with anticipation (spatio-temporal projection) Pattern waypoint 68

69 Simulation results Soulignac et al. UK PlanSIG'13 Edinburgh, 29 January 2014

70 Experimental protocol Context : targets speed & identification range Targets speed vt (knots) 4.5 vt / vuav 2.5% 2.8% 3.3% % 5% 6.6% 10% vuav = 180 knots Identification range ri (NM) ri / r D % 28.5% 42.8% 57.1% 71.4% 85.7% 100% rd = 35 NM 70

71 Experimental protocol Measures For each 4-uplet (density, vt, ri, parameter variant), we measured : - the computation time - the sum pd + pi (a way to check that both pd and pi are high) on the same 30 randomly generated environments, and computed some statistics (mean, standard deviation, percentiles, etc.) The simulations required 10.9 CPU-days with a low density context and 18.9 CPU-days with a high density context. 71

72 Computation time No rerouting Rerouting without local TSP Rerouting with local TSP Sensibility to parameters 75th 50th Low density 25th percentiles High density 72