Formation control of unmanned rotorcraft systems with state constraints and inter-agent collision avoidance

Abstract

We present in this paper a novel framework and distributed control laws for the formation of multiple unmanned rotorcraft systems, be it single-rotor helicopters or multi-copters, with physical constraints and with inter-agent collision avoidance, in cluttered environments. The proposed technique is composed of an analytical distributed consensus control solution in the free space and an optimization based motion planning algorithm for inter-agent and obstacle collision avoidance. More specifically, we design a distributed consensus control law to tackle a series of state constraints that include but not limited to the physical limitations of velocity, acceleration and jerk, and an optimization-based motion planning technique is utilized to generate numerical solutions when the consensus control fails to provide a collision-free trajectory. Besides, a sufficiency condition is given to guarantee the stability of the switching process between the consensus control and motion planning. Finally, both simulation and real flight experiments successfully demonstrate the effectiveness of the proposed technique.

Keywords

Multi-agent system / Unmanned systems / Rotorcraft systems / Formation control / Consensus control

References

[1] ChenB.M.. On the trends of autonomous unmanned systems research. Engineering, 2021, 12: 20-23

[2] ZhaoS.. Affine formation maneuver control of multiagent systems. IEEE Trans. Autom. Control, 2018, 63(12):4140-4155

[3] GeX., HanQ.L.. Distributed formation control of networked multi-agent systems using a dynamic event-triggered communication mechanism. IEEE Trans. Ind. Electron., 2017, 64(10):8118-8127

[4] LiY., FangH., ChenJ.. Anomaly detection and identification for multiagent systems subjected to physical faults and cyberattacks. IEEE Trans. Ind. Electron., 2019, 67(11):9724-9733

[5] WangM., SunJ., ChenJ.. Input-to-state stability of perturbed nonlinear systems with event triggered receding horizon control scheme. IEEE Trans. Ind. Electron., 2018, 66(8):6393-6403

[6] ZhouP., ChenB.M.. Semi-global leader-following output consensus of heterogeneous systems with position and rate-limited actuators via state feedback. Proceedings of the 40th Chinese Control Conference, Shanghai, China, July 26-28, 2021, 2021 5295-5302

[7] DongX., ZhouY., RenZ., ZhongY.. Time-varying formation tracking for second-order multi-agent systems subjected to switching topologies with application to quadrotor formation flying. IEEE Trans. Ind. Electron., 2016, 64(6):5014-5024

[8] DongX., HuG.. Time-varying formation tracking for linear multiagent systems with multiple leaders. IEEE Trans. Autom. Control, 2017, 62(7):3658-3664

[9] DingL., HanQ.L., GuoG.. Network-based leader-following consensus for distributed multi-agent systems. Automatica, 2013, 49(7):2281-2286

[10] ZhaoS., ZelazoD.. Translational and scaling formation maneuver control via a bearing-based approach. IEEE Trans. Control Netw. Syst., 2015, 4(3):429-438

[11] HuaY., DongX., HuG., LiQ., RenZ.. Distributed time-varying output formation tracking for heterogeneous linear multiagent systems with a nonautonomous leader of unknown input. IEEE Trans. Autom. Control, 2019, 64(10):4292-4299

[12] SuY., HuangJ.. Stability of a class of linear switching systems with applications to two consensus problems. IEEE Trans. Autom. Control, 2011, 57(6):1420-1430

[13] SuH., ChenM.Z.Q., WangX., LamJ.. Semiglobal observer-based leader-following consensus with input saturation. IEEE Trans. Ind. Electron., 2013, 61(6):2842-2850

[14] LvY., FuJ., WenG., HuangT., YuX.. Distributed adaptive observer-based control for output consensus of heterogeneous MASs with input saturation constraint. IEEE Trans. Circuits Syst. I, Regul. Pap., 2019, 67(3):995-1007

[15] ZhaoZ., ShiH.. Semi-global containment control for linear systems in the presence of actuator position and rate saturation. Int. J. Robust Nonlinear Control, 2019, 29(1):1-18

[16] KouL., ChenZ., XiangJ.. Cooperative fencing control of multiple vehicles for a moving target with an unknown velocity. IEEE Trans. Autom. Control, 2021, 67(2):1008-1015

[17] FalconiR., SabattiniL., SecchiC., FantuzziC., MelchiorriC.. Edge-weighted consensus-based formation control strategy with collision avoidance. Robotica, 2015, 33(2):332-347

[18] KouL., HuangY., ChenZ., HeS., XiangJ.. Cooperative fencing control of multiple second order vehicles for a moving target with and without velocity measurements. Int. J. Robust Nonlinear Control, 2021, 31(10):4602-4615

[19] HartP.E., NilssonN.J., RaphaelB.. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Cybern., 1968, 4(2):100-107

[20] KaramanS., FrazzoliE.. Incremental sampling-based algorithms for optimal motion planning. Robotics Science and Systems VI, 2010 267-274 104(2)

[21] GammellJ.D., SrinivasaS.S., BarfootT.D.. Batch informed trees (BIT*): sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs. Proceeding of the 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015 3067-3074

[22] W. Wu, Temporal scheduling and optimization for multi-mav planning. PhD thesis (2019)

[23] ArulS.H., ManochaD.. DCAD: decentralized collision avoidance with dynamics constraints for agile quadrotor swarms. IEEE Robot. Autom. Lett., 2020, 5(2):1191-1198

[24] SuY., HuangJ.. Cooperative output regulation of linear multi-agent systems. IEEE Trans. Autom. Control, 2011, 57(4):1062-1066

[25] DongY., LinZ.. An event-triggered observer and its applications in cooperative control of multi-agent systems. IEEE Trans. Autom. Control, 2022, 67(7):3647-3654

[26] KurikiY., NamerikawaT.. Formation control with collision avoidance for a multi-UAV system using decentralized MPC and consensus-based control. SICE J. Control Meas. Syst. Integr., 2015, 8(4):285-294

[27] ChangC., ShiauJ.. Quadrotor formation strategies based on distributed consensus and model predictive controls. Appl. Sci., 2018, 8(11

[28] IlleM., NamerikawaT.. Collision avoidance between multi-UAV-systems considering formation control using MPC. 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2017 651-656

[29] CaiG., ChenB.M., LeeT.H., LumK.. Comprehensive nonlinear modeling of a miniature unmanned helicopter. J. Am. Helicopter Soc., 2012, 57 1):1-13

[30] CaiG., ChenB.M., LeeT.H.. Unmanned Rotorcraft Systems, 2011 Berlin Springer

[31] S.K. Phang, System design methodology and implementation of micro-unmanned aerial vehicles. PhD thesis, National University of Singapore (2014)

[32] DeitsR., TedrakeR.. Computing large convex regions of obstacle-free space through semidefinite programming. Algorithmic Foundations of Robotics XI, 2015 Berlin Springer 109-124

[33] Alonso-MoraJ., MontijanoE., NageliT., HilligesO., SchwagerM., RusD.. Distributed multi-robot formation control in dynamic environments. Auton. Robots, 2019, 43(5):1079-1100

[34] LinZ.. Low Gain Feedback, 1999 London Springer

[35] LaiS., LanM., ChenB.M.. Model predictive local motion planning with boundary state constrained primitives. IEEE Robot. Autom. Lett., 2019, 4(4):3577-3584

Funding

Chinese University of Hong Kong(14206821)