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Formation Flying: a Transition Opportunity for Blakchawks and Other Rotorcrafts

Formation Flying: a Transition Opportunity for Blakchawks and Other Rotorcrafts. Hoam Chung and Shankar Sastry Robotics and Intelligent Machines Laboratory University of California, Berkeley. Overview. Formation flight is the primary movement technique for helicopter teams

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Formation Flying: a Transition Opportunity for Blakchawks and Other Rotorcrafts

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  1. Formation Flying: a Transition Opportunity for Blakchawks and Other Rotorcrafts Hoam Chung and Shankar Sastry Robotics and Intelligent Machines Laboratory University of California, Berkeley ACCLIMATE

  2. Overview • Formation flight is the primary movement technique for helicopter teams • Formation flight automation can achieve: • Diminishing aircrew stress • Maintaining a formation under harsh battlefield conditions • Organized management of • Formation joining/break up • In-flight formation change • Scheduling of flight course • Emergency break up ACCLIMATE

  3. Overview • The algorithm for an autonomous helicopter formation should be able to deal with • Maintaining a stable formation under disturbances • Mixed vehicle formations • Rejoin/breakup with guaranteed safety ACCLIMATE

  4. Review: Mesh Stability • Motivation • Small disturbance can be amplified through the formation if information of neighboring vehicles is solely used • In order to allow large formations, this algorithm uses “leader information” as well as information from neighbors • Mesh Stability • Damping out error/disturbance propagation in the formation using leader information • Mesh stability is guaranteed for homogeneous formations ACCLIMATE

  5. Review: Mesh Stability • Structure of mesh controller Controller gain K(s) Spacing error w.r.t neighbors + + Controller gain K(s) Controller gain K(s) Spacing error w.r.t leader Autonomously controlled unmanned helicopter This achieves: ACCLIMATE

  6. neighbor neighbor Review: Mesh Stability Virtual leader running on a laptop Virtual followers running on laptops Real RUAVs ACCLIMATE

  7. Review: Mesh Stability Animation by A. Pant and X. Xiao ACCLIMATE

  8. Review: Mesh Stability • Gap errors are not damped out due to heterogeneity • The vehicle 33 is more agile than others • Using ‘leader information’ results in a dilemma e33 ACCLIMATE

  9. Review: Mesh Stability • The use of leader information improves the performance of the autonomous formation flight • For a heterogeneous mesh, an extension of mesh stability theory should be considered • The proposed mesh stability scheme has only one directional information flow • It is not easy to incorporate various objectives in mesh stability scheme • “Mesh Stability” does not mean the “Safety” It’s a starting point for autonomous formation flight ACCLIMATE

  10. Inputs from 160th SOAR • In many practical situations, a helicopter team is formed by heterogeneous vehicles (ex. Little Birds + Blackhwaks + Chinooks) • In-flight manual formation joining process is extremely dangerous • Autonomous aerial refueling will be a great help • The effects of battlefield stress exerted on aircrew increases dramatically under tight formations and in adverse circumstances ACCLIMATE

  11. Model Predictive Control • Computes control inputs using real-time optimization • Shows better performance than non-predictive controls • Can consider input/state constraints in on-line manner • Easily incorporates various control objectives • Relative gap maintenance • Tracking reference velocities and heading ACCLIMATE

  12. x x t t y y n n Decentralized MPC • Gap error definitions: constant gap vs. varying gap ACCLIMATE

  13. Decentralized MPC • Finite-horizon optimal control (FHOC) problem gap error tracking state input ACCLIMATE

  14. Decentralized MPC • Simulation setups • Right-echelon 8-vehicle-formation • Initial gaps are 30 ft. in x,y and z • Yamaha R-50 linear dynamics plus nonlinear kinematics model used • 17 state variables, 4 inputs • forward cruise condition, 30mi/h • DynOpt package is used for solving FHOC problem • Only current neighbors’ state variables are interchanged • Extrapolate neighbors’ states for prediction wind gust on vehicle 0 ACCLIMATE

  15. Decentralized MPC The maximum relative gap errors are damped out successfully 0 wind gust induced acceleration (ft/s^2) -2 ACCLIMATE

  16. Decentralized MPC • Scaling using Froude number (Mettler 2003) • Ratio of inertia to gravitational forces • Dynamically similar if models have close Froude numbers V: characteristic velocity (rotor tip speed) L: characteristic length (rotor radius) Scale factor ACCLIMATE

  17. Decentralized MPC Vehicles 1,2,4,5,and 6 are replaced with scaled-up virtual model The maximum relative gap errors are damped out successfully also in the case of heterogeneous formation ACCLIMATE

  18. Formation Manager • Autonomous formation should be reactive • During a formation flight, each vehicle may faces various situations • A vehicle in a formation have multiple modes • A high level agent on top of MPC can make autonomous formation safer and more flexible • Formation Manager • Scheduling normal breakup/rejoin • Managing course changes and in-flight reconfiguration • Dealing with emergency situations • Managing communication channels ACCLIMATE

  19. Formation Manager • Overall system structure References Relative gaps Modified Reference MPC Controller control input Helicopter Navigation info from neighbors Formation Manager Mode Operator Commands Vehicle states ACCLIMATE

  20. Formation Manager • Inside of the formation manager Trajectory Interpolator MPC Trajectory Generator Modified Reference Waypoints Reference velocity/heading values Navigation info from neighbors Mode Finite State Machine Operator commands Vehicle states ACCLIMATE

  21. Formation Manager • Simple FSM for emergency break up/rejoin Single Escape Formation Away from the formation Normal Sufficient spacing Normal As the last follower Normal Rejoining requested Approach to the formation Gap error small enough ACCLIMATE

  22. Formation Manager a. Normal formation b. Proximity warning c. Escape from the formation Simulations d. Fly away from the formation e. Ready to approach f. Rejoin the formation ACCLIMATE

  23. Conclusions • Without any explicit disturbance rejection algorithm, the proposed MPC based formation shows mesh stable property even for heterogeneous formation • MPC with the proposed formation manager can deal with various formations and many practical issues • Break up/rejoin • In-flight reconfiguration • For safer and more versatile autonomous formation, a formation manager should be implemented as a high level agent ACCLIMATE

  24. Future Works • Currently, this research is supported by Phase I of ARO STTR with Scientific Systems Company, Inc. • In Phase II, the proposed MPC-based formation flight will be implemented on our BEAR fleet and a series of flight experiments will be performed Ursa Major 1 Ursa Magna 1 ACCLIMATE

  25. Future Works • The formation manager will be modeled as a hybrid system using hybrid CAD tools like HyVisual • (Part of) formation manager functionality will be implemented on-board and in-flight reconfiguration will be demonstrated • Technology transition to Blackhawk: Human operator interaction is another future research subject ACCLIMATE

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