Distributed multi-robot formation control in dynamic environments

This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to co...

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Vydané v:	Autonomous robots Ročník 43; číslo 5; s. 1079 - 1100
Hlavní autori:	Alonso-Mora, Javier, Montijano, Eduardo, Nägeli, Tobias, Hilliges, Otmar, Schwager, Mac, Rus, Daniela
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 15.06.2019 Springer Nature B.V
Predmet:	Artificial Intelligence Collision avoidance Computational geometry Computer Imaging Computer simulation Control Convexity Engineering Hulls Mathematical programming Mechatronics Moving obstacles Multiple robots Optimization Parameters Pattern Recognition and Graphics Reconfiguration Robot control Robotics Robotics and Automation Robots Rotary wing aircraft Visibility Vision Distributed robotics Dynamic environments Formation control Unmanned aerial vehicles Micro air vehicles Multi-robot systems Collision avoidance Drones
ISSN:	0929-5593, 1573-7527
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Abstract	This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate. Our approach leverages both constrained optimization and multi-robot consensus to compute the parameters of the multi-robot formation. This ensures that the robots make progress and avoid collisions with static and moving obstacles. In particular, via distributed consensus, the robots compute (a) the convex hull of the robot positions, (b) the desired direction of movement and (c) a large convex region embedded in the four dimensional position-time free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation to remain within the convex neighborhood of the robots. The method allows for reconfiguration. Each robot then navigates towards its assigned position in the target collision-free formation via an individual controller that accounts for its dynamics. This approach is efficient and scalable with the number of robots. We present an extensive evaluation of the communication requirements and verify the method in simulations with up to sixteen quadrotors. Lastly, we present experiments with four real quadrotors flying in formation in an environment with one moving human.
AbstractList	This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate. Our approach leverages both constrained optimization and multi-robot consensus to compute the parameters of the multi-robot formation. This ensures that the robots make progress and avoid collisions with static and moving obstacles. In particular, via distributed consensus, the robots compute (a) the convex hull of the robot positions, (b) the desired direction of movement and (c) a large convex region embedded in the four dimensional position-time free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation to remain within the convex neighborhood of the robots. The method allows for reconfiguration. Each robot then navigates towards its assigned position in the target collision-free formation via an individual controller that accounts for its dynamics. This approach is efficient and scalable with the number of robots. We present an extensive evaluation of the communication requirements and verify the method in simulations with up to sixteen quadrotors. Lastly, we present experiments with four real quadrotors flying in formation in an environment with one moving human. This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate. Our approach leverages both constrained optimization and multi-robot consensus to compute the parameters of the multi-robot formation. This ensures that the robots make progress and avoid collisions with static and moving obstacles. In particular, via distributed consensus, the robots compute (a) the convex hull of the robot positions, (b) the desired direction of movement and (c) a large convex region embedded in the four dimensional position-time free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation to remain within the convex neighborhood of the robots. The method allows for reconfiguration. Each robot then navigates towards its assigned position in the target collision-free formation via an individual controller that accounts for its dynamics. This approach is efficient and scalable with the number of robots. We present an extensive evaluation of the communication requirements and verify the method in simulations with up to sixteen quadrotors. Lastly, we present experiments with four real quadrotors flying in formation in an environment with one moving human.
Author	Nägeli, Tobias Alonso-Mora, Javier Schwager, Mac Hilliges, Otmar Montijano, Eduardo Rus, Daniela
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Cites_doi	10.1109/ICRA.2015.7140034 10.1137/S1052623499350013 10.1007/s10514-016-9578-9 10.1109/JPROC.2011.2158377 10.1109/TAC.2011.2166668 10.1007/s10514-011-9225-4 10.1109/ICRA.2015.7139967 10.1016/j.automatica.2014.10.022 10.1177/0278364912462493 10.1177/0278364917719333 10.1016/j.automatica.2011.08.019 10.1007/s10514-015-9429-0 10.1007/s10514-013-9349-9 10.1109/TRO.2016.2523542 10.1016/j.robot.2016.09.002 10.1007/BF01840371 10.1177/0278364916632065 10.1109/70.976023 10.1145/3072959.3073712 10.1109/TCST.2014.2314460 10.1109/TCST.2007.903066 10.1109/TRO.2010.2062070 10.9746/jcmsi.8.285 10.1109/ROBOT.2010.5509534 10.1016/j.automatica.2012.06.040 10.1109/TRO.2007.909833 10.1007/s10514-014-9412-1 10.1109/70.736776 10.1109/LRA.2017.2665693 10.1109/CDC.2000.912786
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References	Quigley, M., Conley, K., Gerkey, B. P., Faust, J., Foote, T., Leibs, J., Wheeler, R., & Ng, A. Y. (2009). Ros: An open-source robot operating system. In: IEEE ICRA workshop on open source software. Michael, N., Zavlanos, M. M., Kumar, V., & Pappas, G. J. (2008). Distributed multi-robot task assignment and formation control. In: IEEE international conference on robotics and automation. Alonso-MoraJNägeliTSiegwartRBeardsleyPCollision avoidance for aerial vehicles in multi-agent scenariosAutonomous Robots201539110112110.1007/s10514-015-9429-0 Augugliaro, F., Schoellig, A. P., & D’Andrea, R. (2012). Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach. In: IEEE/RSJ international conference on intelligent robots and systems. OhKKParkMCAhnHSA survey of multi-agent formation controlAutomatica2015533424440331861810.1016/j.automatica.2014.10.0221371.93015 GillPEMurrayWSaundersMASNOPT: An SQP algorithm for large-scale constrained optimizationSIAM Journal on Optimization20021249791006192250510.1137/S10526234993500131027.90111 RenWBeardRWDistributed consensus in multi-vehicle cooperative control. Communications and control engineering2008LondonSpringer1144.93002 ErdmannMLozano-PerezTOn multiple moving objectsAlgorithmica1987247752191836310.1007/BF018403710643.68152 SchwagerMJulianBJAngermannMRusDEyes in the sky: Decentralized control for the deployment of robotic camera networksProceedings of the IEEE20119991541156110.1109/JPROC.2011.2158377 Derenick, J., Spletzer, J., & Kumar, V.(2010). A semidefinite programming framework for controlling multi-robot systems in dynamic environments. In: IEEE conference on decision and control. Montijano, E., & Mosteo, A. R. (2014). Efficient multi-robot formations using distributed optimization. In: IEEE 53th conference on decision and control. Alonso-MoraJBakerSRusDMulti-robot formation control and object transport in dynamic environments via constrained optimizationThe International Journal of Robotics Research20173691000102110.1177/0278364917719333 MontijanoECristofaloEZhouDSchwagerMSaguesCVision-based distributed formation control without an external positioning systemIEEE Transactions on Robotics201632233935110.1109/TRO.2016.2523542 DesaiJPOstrowskiJPKumarVModeling and control of formations of nonholonomic mobile robotsIEEE Transaction on Robotics and Automation200117690590810.1109/70.976023 Mosteo, A. R., Montano, L., & Lagoudakis, M. G. (2008). Guaranteed-performance multi-robot routing under limited communication range. In: Distributed autonomous robotic systems (pp. 491–502). Ayanian, N., & Kumar, V. (2010a). Abstractions and controllers for groups of robots in environments with obstacles. In: IEEE international conference on robotics and automation, Anchorage, AK (pp. 3537–3542). Chen, Y., Cutler, M., & How, J. P. (2015). Decoupled multiagent path planning via incremental sequential convex programming. In IEEE international conference on robotics and automation (ICRA). FranchiAMasoneCGrabeVRyllMBulfhoffHHGiordanoPRModeling and control of UAV bearing formations with bilateral high-level steeringInternational Journal of Robotics Research2012311504152510.1177/0278364912462493 NestmeyerTRobuffo GiordanoPBlthoffHHFranchiADecentralized simultaneous multi-target exploration using a connected network of multiple robotsAutonomous Robots2017414989101110.1007/s10514-016-9578-9 DerenickJCSpletzerJRConvex optimization strategies for coordinating large-scale robot formationsIEEE Transaction on Robotics2007231252125910.1109/TRO.2007.9098331194.93143 KurikiYNamerikawaTFormation control with collision avoidance for a multi-UAV system using decentralized mpc and consensus-based controlSICE Journal of Control, Measurement, and System Integration20158428529410.9746/jcmsi.8.285 Odelga, M., Stegagno, P., & Bülthoff, HH. (2016). Obstacle detection, tracking and avoidance for a teleoperated UAV. In: 2016 IEEE international conference on robotics and automation (ICRA) (pp. 2984–2990). IEEE. SabattiniLSecchiCFantuzziCArbitrarily shaped formations of mobile robots: Artificial potential fields and coordinate transformationAutonomous Robots20113038539710.1007/s10514-011-9225-4 Saha, I., Ramaithitima, R., Kumar, V., Pappas, G. J., & Seshia, S. A. (2014). Automated composition of motion primitives for multi-robot systems from safe LTL specifications. In: IEEE/RSJ international conference on intelligent robots and systems. LynchNDistributed algorithms1997BurlingtonMorgan Kaufmann publishers TurpinMMohtaKMichaelNKumarVGoal assignment and trajectory planning for large teams of interchangeable robotsAutonomous Robots201437440141510.1007/s10514-014-9412-1 KiaSSCortesJMartinezSDistributed convex optimization via continuous-time coordination algorithms with discrete-time communicationAutomatica201655525426433366751377.93018 Alonso-Mora, J., Montijano, E., Schwager, M., & Rus, D. (2016). Distributed multi-robot navigation in formation among obstacles: A geometric and optimization approach with consensus. In IEEE international conference on robotics and automation. Suzuki, T., Sekine, T., Fujii, T., Asama, H., & Endo, I. (2000). Cooperative formation among multiple mobile robot teleoperation in inspection task. In Proceedings of the 39th IEEE Conference on Decision and Control, 2000 (Vol. 1, pp. 358–363). IEEE. UrcolaPLazaroMTCastellanosJAMontanoLCooperative minimum expected length planning for robot formations in stochastic mapsRobotics and Autonomous Systems201787385010.1016/j.robot.2016.09.002 BurgerMNotarstefanoGAllgowerFBulloFA distributed simplex algorithm for degenerate linear programs and multi-agent assignmentsAutomatica201248922982304295691110.1016/j.automatica.2012.06.0401257.93033 BalchTArkinRCBehavior-based formation control for multirobot teamsIEEE Transaction on Robotics and Automation199814692693910.1109/70.736776 NägeliTMeierLDomahidiAAlonso-MoraJHilligesOReal-time planning for automated multi-view drone cinematographyACM Transactions on Graphics (TOG)201736413210.1145/3072959.3073712 WurmanPRD’AndreaRMountzMCoordinating hundreds of cooperative, autonomous vehicles in warehousesAI Magazine20082919 DongXYuBShiZZhongYTime-varying formation control for unmanned aerial vehicles: Theories and applicationsIEEE Transactions on Control Systems Technology201523134034810.1109/TCST.2014.2314460 MostaghNMichaelNJadbabaieADaniilidisKVision-based, distributed control laws for motion coordination of nonholonomic robotsIEEE Transactions on Robotics2009254851860 AyanianNKumarVDecentralized feedback controllers for multi-agent teams in environments with obstaclesIEEE Transactions on Robotics201026587888710.1109/TRO.2010.2062070 Alonso-Mora, J., Knepper, R. A., Siegwart, R., & Rus, D. (2015a). Local motion planning for collaborative multi-robot manipulation of deformable objects. In: IEEE international conference on robotics and automation. Deits, R., & Tedrake, R. (2014). Computing large convex regions of obstacle-free space through semidefinite programming. In Workshop on the algorithmic fundamentals of robotics. Schoch, M., Alonso-Mora, J., Siegwart, R., & Beardsley, P. (2014). Viewpoint and trajectory optimization for animation display with aerial vehicles. In: 2010 IEEE international conference on robotics and automation (ICRA) (pp. 4711–4716). IEEE. KeviczkyTBorrelliFFregeneKGodboleDBalasGJDecentralized receding horizon control and coordination of autonomous vehicle formationsIEEE Transactions on Control Systems Technology2008161193310.1109/TCST.2007.903066 Domahidi, A., Zgraggen, A. U., Zeilinger, M. N., Morari, M., & Jones, C. N. (2012). Efficient interior point methods for multistage problems arising in receding horizon control. In: 47th IEEE conference on decision and control, 2008. CDC 2008 (pp. 668–674). IEEE. KushleyevAMellingerDPowersCKumarVTowards a swarm of agile micro quadrotorsAutonomous Robots201335428730010.1007/s10514-013-9349-9 MorganDSubramanianGPChungS-JHadaeghFYSwarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programmingThe International Journal of Robotics Research201635101261128510.1177/0278364916632065 NägeliTAlonso-MoraJDomahidiARusDHilligesOReal-time motion planning for aerial videography with dynamic obstacle avoidance and viewpoint optimizationIEEE Robotics and Automation Letters2017231696170310.1109/LRA.2017.2665693 Balch, T., & Hybinette, M. (2000). Social potentials for scalable multi-robot formations. In: IEEE international conference on robotics and automation. Domahidi, A., & Jerez, J. (2016). FORCES Pro: Code generation for embedded optimization. https://www.embotech.com/FORCES-Pro. LinZFrancisBMaggioreMNecessary and sufficient graphical conditions for formation control of unicyclesIEEE Transaction on Automatic Control200550154054521108191365.93324 OhK-KAhnH-SFormation control of mobile agents based on inter-agent distance dynamicsAutomatica2011471023062312288685510.1016/j.automatica.2011.08.0191228.93014 HatanakaTIgarashiYFujitaMSpongMWPassivity-based pose synchronization in three dimensionsIEEE Transactions on Automatic Control2012572360375291874210.1109/TAC.2011.21666681369.93417 J Alonso-Mora (9783_CR4) 2015; 39 T Nägeli (9783_CR35) 2017; 36 9783_CR29 J Alonso-Mora (9783_CR3) 2017; 36 9783_CR8 M Schwager (9783_CR46) 2011; 99 A Kushleyev (9783_CR26) 2013; 35 N Lynch (9783_CR28) 1997 E Montijano (9783_CR31) 2016; 32 9783_CR38 9783_CR6 9783_CR5 X Dong (9783_CR18) 2015; 23 9783_CR30 T Keviczky (9783_CR23) 2008; 16 T Nägeli (9783_CR36) 2017; 2 N Mostagh (9783_CR33) 2009; 25 9783_CR34 M Burger (9783_CR10) 2012; 48 P Urcola (9783_CR49) 2017; 87 L Sabattini (9783_CR43) 2011; 30 T Hatanaka (9783_CR22) 2012; 57 JP Desai (9783_CR15) 2001; 17 N Ayanian (9783_CR7) 2010; 26 KK Oh (9783_CR40) 2015; 53 9783_CR47 Z Lin (9783_CR27) 2005; 50 PR Wurman (9783_CR50) 2008; 29 9783_CR41 M Erdmann (9783_CR19) 1987; 2 SS Kia (9783_CR24) 2016; 55 9783_CR45 9783_CR44 T Balch (9783_CR9) 1998; 14 M Turpin (9783_CR48) 2014; 37 T Nestmeyer (9783_CR37) 2017; 41 JC Derenick (9783_CR14) 2007; 23 W Ren (9783_CR42) 2008 9783_CR2 PE Gill (9783_CR21) 2002; 12 9783_CR1 9783_CR17 Y Kuriki (9783_CR25) 2015; 8 9783_CR16 D Morgan (9783_CR32) 2016; 35 9783_CR13 9783_CR12 9783_CR11 K-K Oh (9783_CR39) 2011; 47 A Franchi (9783_CR20) 2012; 31
References_xml	– reference: Alonso-Mora, J., Knepper, R. A., Siegwart, R., & Rus, D. (2015a). Local motion planning for collaborative multi-robot manipulation of deformable objects. In: IEEE international conference on robotics and automation. – reference: BalchTArkinRCBehavior-based formation control for multirobot teamsIEEE Transaction on Robotics and Automation199814692693910.1109/70.736776 – reference: Alonso-Mora, J., Montijano, E., Schwager, M., & Rus, D. (2016). Distributed multi-robot navigation in formation among obstacles: A geometric and optimization approach with consensus. In IEEE international conference on robotics and automation. – reference: TurpinMMohtaKMichaelNKumarVGoal assignment and trajectory planning for large teams of interchangeable robotsAutonomous Robots201437440141510.1007/s10514-014-9412-1 – reference: UrcolaPLazaroMTCastellanosJAMontanoLCooperative minimum expected length planning for robot formations in stochastic mapsRobotics and Autonomous Systems201787385010.1016/j.robot.2016.09.002 – reference: Ayanian, N., & Kumar, V. (2010a). Abstractions and controllers for groups of robots in environments with obstacles. In: IEEE international conference on robotics and automation, Anchorage, AK (pp. 3537–3542). – reference: Quigley, M., Conley, K., Gerkey, B. P., Faust, J., Foote, T., Leibs, J., Wheeler, R., & Ng, A. Y. (2009). Ros: An open-source robot operating system. In: IEEE ICRA workshop on open source software. – reference: Domahidi, A., Zgraggen, A. U., Zeilinger, M. N., Morari, M., & Jones, C. N. (2012). Efficient interior point methods for multistage problems arising in receding horizon control. In: 47th IEEE conference on decision and control, 2008. CDC 2008 (pp. 668–674). IEEE. – reference: Chen, Y., Cutler, M., & How, J. P. (2015). Decoupled multiagent path planning via incremental sequential convex programming. In IEEE international conference on robotics and automation (ICRA). – reference: LinZFrancisBMaggioreMNecessary and sufficient graphical conditions for formation control of unicyclesIEEE Transaction on Automatic Control200550154054521108191365.93324 – reference: KushleyevAMellingerDPowersCKumarVTowards a swarm of agile micro quadrotorsAutonomous Robots201335428730010.1007/s10514-013-9349-9 – reference: NestmeyerTRobuffo GiordanoPBlthoffHHFranchiADecentralized simultaneous multi-target exploration using a connected network of multiple robotsAutonomous Robots2017414989101110.1007/s10514-016-9578-9 – reference: SchwagerMJulianBJAngermannMRusDEyes in the sky: Decentralized control for the deployment of robotic camera networksProceedings of the IEEE20119991541156110.1109/JPROC.2011.2158377 – reference: RenWBeardRWDistributed consensus in multi-vehicle cooperative control. Communications and control engineering2008LondonSpringer1144.93002 – reference: HatanakaTIgarashiYFujitaMSpongMWPassivity-based pose synchronization in three dimensionsIEEE Transactions on Automatic Control2012572360375291874210.1109/TAC.2011.21666681369.93417 – reference: Deits, R., & Tedrake, R. (2014). Computing large convex regions of obstacle-free space through semidefinite programming. In Workshop on the algorithmic fundamentals of robotics. – reference: Schoch, M., Alonso-Mora, J., Siegwart, R., & Beardsley, P. (2014). Viewpoint and trajectory optimization for animation display with aerial vehicles. In: 2010 IEEE international conference on robotics and automation (ICRA) (pp. 4711–4716). IEEE. – reference: Alonso-MoraJNägeliTSiegwartRBeardsleyPCollision avoidance for aerial vehicles in multi-agent scenariosAutonomous Robots201539110112110.1007/s10514-015-9429-0 – reference: SabattiniLSecchiCFantuzziCArbitrarily shaped formations of mobile robots: Artificial potential fields and coordinate transformationAutonomous Robots20113038539710.1007/s10514-011-9225-4 – reference: ErdmannMLozano-PerezTOn multiple moving objectsAlgorithmica1987247752191836310.1007/BF018403710643.68152 – reference: OhKKParkMCAhnHSA survey of multi-agent formation controlAutomatica2015533424440331861810.1016/j.automatica.2014.10.0221371.93015 – reference: LynchNDistributed algorithms1997BurlingtonMorgan Kaufmann publishers – reference: MostaghNMichaelNJadbabaieADaniilidisKVision-based, distributed control laws for motion coordination of nonholonomic robotsIEEE Transactions on Robotics2009254851860 – reference: Saha, I., Ramaithitima, R., Kumar, V., Pappas, G. J., & Seshia, S. A. (2014). Automated composition of motion primitives for multi-robot systems from safe LTL specifications. In: IEEE/RSJ international conference on intelligent robots and systems. – reference: NägeliTMeierLDomahidiAAlonso-MoraJHilligesOReal-time planning for automated multi-view drone cinematographyACM Transactions on Graphics (TOG)201736413210.1145/3072959.3073712 – reference: Odelga, M., Stegagno, P., & Bülthoff, HH. (2016). Obstacle detection, tracking and avoidance for a teleoperated UAV. In: 2016 IEEE international conference on robotics and automation (ICRA) (pp. 2984–2990). IEEE. – reference: Balch, T., & Hybinette, M. (2000). Social potentials for scalable multi-robot formations. In: IEEE international conference on robotics and automation. – reference: Suzuki, T., Sekine, T., Fujii, T., Asama, H., & Endo, I. (2000). Cooperative formation among multiple mobile robot teleoperation in inspection task. In Proceedings of the 39th IEEE Conference on Decision and Control, 2000 (Vol. 1, pp. 358–363). IEEE. – reference: Derenick, J., Spletzer, J., & Kumar, V.(2010). A semidefinite programming framework for controlling multi-robot systems in dynamic environments. In: IEEE conference on decision and control. – reference: KiaSSCortesJMartinezSDistributed convex optimization via continuous-time coordination algorithms with discrete-time communicationAutomatica201655525426433366751377.93018 – reference: Mosteo, A. R., Montano, L., & Lagoudakis, M. G. (2008). Guaranteed-performance multi-robot routing under limited communication range. In: Distributed autonomous robotic systems (pp. 491–502). – reference: Augugliaro, F., Schoellig, A. P., & D’Andrea, R. (2012). Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach. In: IEEE/RSJ international conference on intelligent robots and systems. – reference: DesaiJPOstrowskiJPKumarVModeling and control of formations of nonholonomic mobile robotsIEEE Transaction on Robotics and Automation200117690590810.1109/70.976023 – reference: WurmanPRD’AndreaRMountzMCoordinating hundreds of cooperative, autonomous vehicles in warehousesAI Magazine20082919 – reference: OhK-KAhnH-SFormation control of mobile agents based on inter-agent distance dynamicsAutomatica2011471023062312288685510.1016/j.automatica.2011.08.0191228.93014 – reference: BurgerMNotarstefanoGAllgowerFBulloFA distributed simplex algorithm for degenerate linear programs and multi-agent assignmentsAutomatica201248922982304295691110.1016/j.automatica.2012.06.0401257.93033 – reference: Domahidi, A., & Jerez, J. (2016). FORCES Pro: Code generation for embedded optimization. https://www.embotech.com/FORCES-Pro. – reference: Alonso-MoraJBakerSRusDMulti-robot formation control and object transport in dynamic environments via constrained optimizationThe International Journal of Robotics Research20173691000102110.1177/0278364917719333 – reference: KeviczkyTBorrelliFFregeneKGodboleDBalasGJDecentralized receding horizon control and coordination of autonomous vehicle formationsIEEE Transactions on Control Systems Technology2008161193310.1109/TCST.2007.903066 – reference: NägeliTAlonso-MoraJDomahidiARusDHilligesOReal-time motion planning for aerial videography with dynamic obstacle avoidance and viewpoint optimizationIEEE Robotics and Automation Letters2017231696170310.1109/LRA.2017.2665693 – reference: FranchiAMasoneCGrabeVRyllMBulfhoffHHGiordanoPRModeling and control of UAV bearing formations with bilateral high-level steeringInternational Journal of Robotics Research2012311504152510.1177/0278364912462493 – reference: DongXYuBShiZZhongYTime-varying formation control for unmanned aerial vehicles: Theories and applicationsIEEE Transactions on Control Systems Technology201523134034810.1109/TCST.2014.2314460 – reference: AyanianNKumarVDecentralized feedback controllers for multi-agent teams in environments with obstaclesIEEE Transactions on Robotics201026587888710.1109/TRO.2010.2062070 – reference: GillPEMurrayWSaundersMASNOPT: An SQP algorithm for large-scale constrained optimizationSIAM Journal on Optimization20021249791006192250510.1137/S10526234993500131027.90111 – reference: MontijanoECristofaloEZhouDSchwagerMSaguesCVision-based distributed formation control without an external positioning systemIEEE Transactions on Robotics201632233935110.1109/TRO.2016.2523542 – reference: MorganDSubramanianGPChungS-JHadaeghFYSwarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programmingThe International Journal of Robotics Research201635101261128510.1177/0278364916632065 – reference: Montijano, E., & Mosteo, A. R. (2014). Efficient multi-robot formations using distributed optimization. In: IEEE 53th conference on decision and control. – reference: DerenickJCSpletzerJRConvex optimization strategies for coordinating large-scale robot formationsIEEE Transaction on Robotics2007231252125910.1109/TRO.2007.9098331194.93143 – reference: KurikiYNamerikawaTFormation control with collision avoidance for a multi-UAV system using decentralized mpc and consensus-based controlSICE Journal of Control, Measurement, and System Integration20158428529410.9746/jcmsi.8.285 – reference: Michael, N., Zavlanos, M. M., Kumar, V., & Pappas, G. J. (2008). Distributed multi-robot task assignment and formation control. In: IEEE international conference on robotics and automation. – volume: 50 start-page: 540 issue: 1 year: 2005 ident: 9783_CR27 publication-title: IEEE Transaction on Automatic Control – ident: 9783_CR11 doi: 10.1109/ICRA.2015.7140034 – ident: 9783_CR45 – volume: 12 start-page: 979 issue: 4 year: 2002 ident: 9783_CR21 publication-title: SIAM Journal on Optimization doi: 10.1137/S1052623499350013 – ident: 9783_CR16 – volume: 41 start-page: 989 issue: 4 year: 2017 ident: 9783_CR37 publication-title: Autonomous Robots doi: 10.1007/s10514-016-9578-9 – volume: 99 start-page: 1541 issue: 9 year: 2011 ident: 9783_CR46 publication-title: Proceedings of the IEEE doi: 10.1109/JPROC.2011.2158377 – ident: 9783_CR12 – volume: 57 start-page: 360 issue: 2 year: 2012 ident: 9783_CR22 publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2011.2166668 – volume: 30 start-page: 385 year: 2011 ident: 9783_CR43 publication-title: Autonomous Robots doi: 10.1007/s10514-011-9225-4 – ident: 9783_CR29 – ident: 9783_CR1 doi: 10.1109/ICRA.2015.7139967 – volume: 53 start-page: 424 issue: 3 year: 2015 ident: 9783_CR40 publication-title: Automatica doi: 10.1016/j.automatica.2014.10.022 – ident: 9783_CR5 – ident: 9783_CR2 – ident: 9783_CR17 – volume: 31 start-page: 1504 year: 2012 ident: 9783_CR20 publication-title: International Journal of Robotics Research doi: 10.1177/0278364912462493 – ident: 9783_CR13 – volume: 36 start-page: 1000 issue: 9 year: 2017 ident: 9783_CR3 publication-title: The International Journal of Robotics Research doi: 10.1177/0278364917719333 – ident: 9783_CR34 – ident: 9783_CR38 – volume: 47 start-page: 2306 issue: 10 year: 2011 ident: 9783_CR39 publication-title: Automatica doi: 10.1016/j.automatica.2011.08.019 – volume: 39 start-page: 101 issue: 1 year: 2015 ident: 9783_CR4 publication-title: Autonomous Robots doi: 10.1007/s10514-015-9429-0 – volume: 35 start-page: 287 issue: 4 year: 2013 ident: 9783_CR26 publication-title: Autonomous Robots doi: 10.1007/s10514-013-9349-9 – ident: 9783_CR41 – volume: 32 start-page: 339351 issue: 2 year: 2016 ident: 9783_CR31 publication-title: IEEE Transactions on Robotics doi: 10.1109/TRO.2016.2523542 – volume: 87 start-page: 3850 year: 2017 ident: 9783_CR49 publication-title: Robotics and Autonomous Systems doi: 10.1016/j.robot.2016.09.002 – volume: 2 start-page: 477 year: 1987 ident: 9783_CR19 publication-title: Algorithmica doi: 10.1007/BF01840371 – volume: 35 start-page: 1261 issue: 10 year: 2016 ident: 9783_CR32 publication-title: The International Journal of Robotics Research doi: 10.1177/0278364916632065 – volume: 17 start-page: 905 issue: 6 year: 2001 ident: 9783_CR15 publication-title: IEEE Transaction on Robotics and Automation doi: 10.1109/70.976023 – ident: 9783_CR30 – volume: 36 start-page: 132 issue: 4 year: 2017 ident: 9783_CR35 publication-title: ACM Transactions on Graphics (TOG) doi: 10.1145/3072959.3073712 – volume: 25 start-page: 851860 issue: 4 year: 2009 ident: 9783_CR33 publication-title: IEEE Transactions on Robotics – volume: 23 start-page: 340 issue: 1 year: 2015 ident: 9783_CR18 publication-title: IEEE Transactions on Control Systems Technology doi: 10.1109/TCST.2014.2314460 – volume: 16 start-page: 19 issue: 1 year: 2008 ident: 9783_CR23 publication-title: IEEE Transactions on Control Systems Technology doi: 10.1109/TCST.2007.903066 – ident: 9783_CR44 – volume: 26 start-page: 878 issue: 5 year: 2010 ident: 9783_CR7 publication-title: IEEE Transactions on Robotics doi: 10.1109/TRO.2010.2062070 – volume: 8 start-page: 285 issue: 4 year: 2015 ident: 9783_CR25 publication-title: SICE Journal of Control, Measurement, and System Integration doi: 10.9746/jcmsi.8.285 – volume-title: Distributed algorithms year: 1997 ident: 9783_CR28 – ident: 9783_CR6 doi: 10.1109/ROBOT.2010.5509534 – volume: 48 start-page: 2298 issue: 9 year: 2012 ident: 9783_CR10 publication-title: Automatica doi: 10.1016/j.automatica.2012.06.040 – volume: 29 start-page: 9 issue: 1 year: 2008 ident: 9783_CR50 publication-title: AI Magazine – volume: 23 start-page: 1252 year: 2007 ident: 9783_CR14 publication-title: IEEE Transaction on Robotics doi: 10.1109/TRO.2007.909833 – volume: 37 start-page: 401 issue: 4 year: 2014 ident: 9783_CR48 publication-title: Autonomous Robots doi: 10.1007/s10514-014-9412-1 – volume: 14 start-page: 926 issue: 6 year: 1998 ident: 9783_CR9 publication-title: IEEE Transaction on Robotics and Automation doi: 10.1109/70.736776 – volume: 55 start-page: 254 issue: 5 year: 2016 ident: 9783_CR24 publication-title: Automatica – volume-title: Distributed consensus in multi-vehicle cooperative control. 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Snippet	This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static... This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static...
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SubjectTerms	Artificial Intelligence Collision avoidance Computational geometry Computer Imaging Computer simulation Control Convexity Engineering Hulls Mathematical programming Mechatronics Moving obstacles Multiple robots Optimization Parameters Pattern Recognition and Graphics Reconfiguration Robot control Robotics Robotics and Automation Robots Rotary wing aircraft Visibility Vision
Title	Distributed multi-robot formation control in dynamic environments
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