1. THESIS COLLOQUIUM Collision avoidance and coalition formation of multiple unmanned aerial vehicles in high density traffic environments Joel George M 1

2. “… it was nevertheless - the first time in the history of the world in which a machine carrying a man had raised itself by its own power into the air in full flight, had sailed forward without reduction of speed, and had finally landed at a point as high as that from which it started.” Details of first flight: Speed = 6.8 miles/hour Range = 120 feet Altitude = 10 feet Orville Wright 2

3. Faster, Farther, Higher (and Safer) Slogan of aircraft design industry Boundaries of speed, altitude, range, and endurance have been pushed further and further 3

4. Aircraft kept the tag “machine carrying a man” Presence of man in aircraft was always an important design consideration 4

5. “Elimination of pilot from a manned combat aircraft removes many of the conventional design constraints … This at once throws open the design parameter space and dramatic improvements in performance measures like increased speed, range, maneuverability, and payload can be achieved.” Late Dr. S Pradeep 5

6. Dull, Dirty, and Dangerous missions In some missions, human presence ‘need not’ be there In some other missions, human presence ‘should not’ be there Unmanned Aerial Vehicles find applications in Why Unmanned Aerial Vehicles (UAVs)? 6

7. Why UAVs? Factors compelling the use of Unmanned Aerial Vehicles (UAVs) Design freedom (mission specific designs) Dull, dirty, and dangerous missions Low cost, portability, absence to human risk, … 7

8. Why autonomous UAVs? UAVs can be remotely piloted However, desirable to make UAVs autonomous 8

9. Why multiple UAVs? Use of multiple UAVs leads to coordination problems UAVs are often small Collision avoidance, coalition formation, formation flying, … Some missions are more effectively done by multiple UAVs 9

10. This thesis addresses the problems of Collision avoidance, Coalition formation, and Mission involving collision avoidance and coalition formation of multiple UAVs in high density traffic environments 10

11. OUTLINE CHAPTER 1 Introduction CHAPTER 2 Collision avoidance among multiple UAVs CHAPTER 3 Collision avoidance with realistic UAV models CHAPTER 4 Coalition formation with global communication CHAPTER 5 Coalition formation with limited communication CHAPTER 6 Coalition formation and collision avoidance in multiple UAV missions CHAPTER 7 Conclusions 11

12. CHAPTER 1 Introduction 12

13. Collision avoidance Using information of positions and velocities of UAVs in the sensor range, a UAV needs to find an efficient safe path to destination A safe path means that no UAV should come within each others safety zones during any time of flight Efficiency  less deviation from nominal path 13

14. Have been looked at from the robotics and air traffic management points of view Ground based robots can stop to finish the calculations Collision avoidance algorithms addressing air traffic management problems consider only a few aircraft Collision avoidance literature 14

15. A situation requiring three dimensional collision avoidance 15

16. Coalition formation Multiple UAVs with limited sensor ranges search for targets A target found needs to be prosecuted A UAV that detected the target may not have sufficient resources ‘Need to talk’ to other UAVs to form a coalition for target prosecution Objective: To find and prosecute all targets as quickly as possible The algorithm should be scalable 16

17. Multi-agent coalition formation Can share resources Extensive communication Multi-robot coalition formation Resources do not deplete Multi-UAV coalition formation Resources deplete with use Need quick coalition formation algorithms Coalition formation literature 17

18. Multi-UAV rendezvous with collision avoidance Coalition formation with collision avoidance Collision avoidance and coalition formation in multiple UAV missions 18

19. CHAPTER 2 Collision avoidance among multiple UAVs 19

20. UAV kinematic model Limited sensor range Assumptions Constant speed Minimum radius of turn Further assumption 20

21. It suffices, in case of a multiple UAV conflict, for a UAV to avoid the most imminent near miss to obtain a good collision avoidance performance. 21

22. Lesser the deviation (higher efficiency), better the collision avoidance algorithm Objective is to reduce the number of near misses, as in a high density traffic case, it may not be possible to avoid near misses Lesser the number of near misses, better the collision avoidance algorithm Two UAVs within each others safety zones results in a ‘near miss’ Efficiency = Aircraft deviates from its nominal path due to collision avoidance maneuver. 22

23. Reduce multiple conflicts to an ‘effective’ one-one conflict by finding the ‘most threatening’ UAV from among the ones in sensor range UAVs encounter multiple conflicts Most threatening UAV: A UAV U2 is the most threatening UAV for U1 at an instant of time, if 1)U2 is in the sensor range of U1 2) Predicted miss distance between U1 and U2 suggests the occurrence of a near miss 3)Out of all the UAVs in the sensor range of U1 with which U1 has a predicted near miss, the near miss with U2 is the earliest to occur 23

24. For collision avoidance, a UAV does a maneuver to increase the LOS rate Collision avoidance maneuver Each UAV does a maneuver to avoid collision with the most threatening neighbor A necessary condition for collision between two aircraft to occur is that the Line of Sight (LOS) Rate between them be zero 24

25. Two Dimensional Reactive Collision Avoidance: RCA-2D 25

26. Simple head-on collisions 26

27. High density traffic 27

28. Random flight test Aircraft fly from random points on outer circle to random points on inner circle Velocity: 500 miles per hour Turn rate: 5 degrees per second Radius of outer circle 120 miles Radius of inner circle 100 miles 28

29. Since the test case involves random flights, the simulations are run 20 times for each case, and the values presented are averaged over the results obtained from these runs Archibald, J. K., Hill, J. C., Jepsen, N. A., Strirling, W. C., & Frost, R. L. (2008). A satisficing approach to aircraft conflict resolution. IEEE Transactions on System, Man, and Cybernetics - Part C: Applications and Reviews, 38, 510–521. 29

30. Effect of noise in position measurement 30

31. Collision plane RCA-3D-I RCA-3D-O Three dimensional engagement Three dimensional collision avoidance algorithms 31

32. Comparison of the performance 2D and 3D algorithms for random flights 32

33. Case 1: h = 20 miles, r in = 100 miles, and r out = 120 miles Case 2: h = 60 miles, r in = 55 miles, and r out = 70 miles Case 3: h = 100 miles, r in = 40 miles, and r out = 50 miles Case Near MissesEfficiency (%) RCA-3D-IRCA-3D-ORCA-3D-IRCA-3D-O 12.62.498.6298.84 28.34.697.3096.96 311.86.196.17 Modified random flights (three dimensional) 33

34. Summary of Chapter 2 Developed conceptually simple collision avoidance algorithms For two and three dimensional conflicts For high density traffic environments Analyzed the performance of these algorithms 34

35. CHAPTER 3 Collision avoidance with realistic UAV models 35

36. Realistic UAV Model Stability and control derivatives from Aviones A UAV flight simulator developed by the Brigham Young University (an open source software) Available: http://aviones.sourceforge.net/ The Zagi Aircraft www.zagi.com Span = 1.5 m Mean Chord = 0.33 m Weight = 1.5 kg Picture courtesy: www.zagi.com UAV of span 1.4224 m, weighing 1.56 kg 36

37. PI controllers with parameters tuned manually Controllers designed through successive loop closure Separate controllers for holding altitude, attitude, and speed UAV control system 37

38. Controller design Altitude hold controller Similar controllers for attitude and speed holds are designed 38

39. Implementing the guidance commands Look-up graph for bank angle that will provide required turn rate 39

40. Test of collision avoidance A example of collision avoidance of 5 UAVs The test case is set-up such that the avoidance of one conflict will lead into another 40

41. Random flights test case inner circle radius 400 m outer circle radius 500 m velocity 12 m/s maximum turn rate 10 deg/sec. Any approach of two UAVs within 10 m is considered a near miss An approach within 2 m is a collision. Test case of random flights for dense traffic 41

42. Results of the random flight test case 42

43. Implementation of 3D collision avoidance algorithm Realization of pitch and turn rate commands 43

44. Pitch rate guidance and control loops 44

45. Results of the random flight test case for heterogeneous UAVs for homogeneous UAVs 45

46. Collision avoidance in presence of non-cooperating UAVs 46

47. Summary of Chapter 3 Implemented collision avoidance algorithms on 6 DoF UAV models Simulations with heterogeneous and non-cooperating UAVs 47

48. CHAPTER 4 Coalition formation with global communication 48

49. Destroy the target is minimum time Coalition should have minimum number of UAVs Rendezvous on target to inflict maximum damage Search targets and destroy them The targets may have different requirements Objectives: Coalition formation for search and prosecute mission 49

50. Limited sensor radius Target locations are not know a priori Limited resources that deplete with use Stationary targets Global communication Assumptions 50

51. Theorem: The minimum time minimum member coalition formation for a single target is NP-hard 51

52. Coalition leader initiates the coalition formation process UAV that detects the target – Coalition leader Deadlocks are handled by rules/protocols 52

53. Communication protocol for coalition formation process 53

54. Stage II Find a minimum member coalition Two stage algorithm for coalition formation Stage I Find a minimum time coalition 54

55. Stage I: Minimum time coalition Theorem: Finding minimum member coalition is NP-hard 55 Recruit members to coalition in the ascending order of their ETA to target

56. Theorem: Stage I gives a minimum time coalition Theorem: Stage I has polynomial time complexity 56

57. Stage II ‘Prune’ the coalition formed in stage I to form a reduced member coalition 57

58. Coalition formation examples 58

59. Solution using Particle Swarm Optimization (PSO) Global solution of the search and prosecute problem using PSO Target locations known a priori 59

60. Comparison of solutions 60

61. Summary of Chapter 4 Coalition formation algorithm for search and prosecute mission Two stage polynomial time algorithm Efficacy of the algorithm demonstrated via simulations 61

62. CHAPTER 5 Coalition formation with limited communication 62

63. Dynamic network over which coalition formations should take place UAVs have limited communication ranges 63

64. Log of messages kept to avoid duplication Every UAV acts as a relay node Each hop of message has an associated lag Time-to-live for a message Network properties 64

65. Works well as coalition formation period is much shorter than the time scale in which network connection varies Coalition formation over dynamic network Find a sub static coalition formation period A UAV accepts to be a relay node only if sub-network that is over the UAV it is in communication range for the entire coalition formation period 65

66. Example of stationary and constant velocity target 66

67. A coalition member prosecutes the target and continues to track it until the target is within the sensor range of the next coalition member Prosecution sequence for maneuvering target Rendezvous at a maneuvering target is difficult  sequential prosecution Coalition leader tracks the maneuvering target and broadcast this information until the target is in the sensor range of one of the coalition members 67

68. Example 68

69. Performance of coalition formation algorithm with increase in number of UAVs 69

70. Performance of coalition formation algorithm with increase in communication range 70

71. Performance of coalition formation algorithm with increase in communication delay 71

72. Summary of Chapter 5 Coalition formation of UAVs with limited communication ranges Prosecution of stationary, constant velocity, and maneuvering targets 72

73. CHAPTER 6 Collision avoidance and coalition formation in multiple UAV missions 73

74. Rendezvous – meeting at a pre-planned time and place Rendezvous of multiple UAVs For simultaneous deployment of resources To exchange resources or critical information 74

75. Multiple UAV Rendezvous Uses a consensus on Estimated Time of Arrival (ETA) at target Rendezvous under collision avoidance Rendezvous of multiple UAVs when some of the UAVs have to do collision avoidance maneuvers en route 75

76. Multiple UAV Rendezvous Algorithm If velocity hits lower bound, then ‘wander away’ from the rendezvous point Consensus in ETA achieved using Velocity control within bounds Wandering maneuver Change in velocity proportional to (average ETA – ETA) 76

77. Solution Approach In principle, any consensus protocol can be used. Average consensus protocol is used for the purpose of illustration Linear average consensus 77

78. Rendezvous: Simulation Results Rendezvous of 5 UAVs (3 of them do collision avoidance on the way) 78

79. Target tracking 79

80. Coalition formation with collision avoidance 80

81. Summary of Chapter 6 Multiple UAV rendezvous with collision avoidance Coalition formation with collision avoidance 81

82. CHAPTER 7 Conclusions 82

83. Algorithms for collision avoidance and coalition formation and their applications Algorithms are conceptually ‘simple’ scalable 83

84. Better controller implementations possible for collision avoidance Better communication protocols possible for coalition formation Possible extensions of present work 84

85. 85