Recent Advances in Formations of Multiple Robots
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Amigoni F, Banfi J, Basilico N. Multirobot exploration of communication-restricted environments: a survey. IEEE Intell Syste 2017;32(6):48–57.
Tuci E, Alkilabi MHM, Akanyeti O. Cooperative object transport in multi-robot systems: a review of the state-of-the-art. Front Robot AI 2018;5:59.
Dong X, Li Q, Ren Z, Zhong Y. Formation-containment control for high-order linear time-invariant multi-agent systems with time delays. J Franklin Inst 2015;352(9):3564–3584.
Ramachandran RK, Preiss JA, Sukhatme GS. 2019. Resilience by reconfiguration: exploiting heterogeneity in robot teams. arXiv:1903.04856.
Shapira Y, Agmon N. Path planning for optimizing survivability of multi-robot formation in adversarial environments. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2015. p. 4544–4549.
Mosteo AR, Montijano E, Tardioli D. Optimal role and position assignment in multi-robot freely reachable formations. Automatica 2017;81:305–313.
MacAlpine P, Price E, Stone P. Scram: scalable collision-avoiding role assignment with minimal-makespan for formational positioning. In: AAAI; 2015. p. 2096–2102.
Bose K, Adhikary R, Kundu MK, Sau B. Arbitrary pattern formation on infinite grid by asynchronous oblivious robots. Theor Comput Sci 2020;815:213–227.
Flocchini P, Prencipe G, Santoro N, Viglietta G. Distributed computing by mobile robots: uniform circle formation. Distrib Comput 2017;30(6):413–457.
Hyondong O, Shirazi AR, Sun C, Jin Y. Bio-inspired self-organising multi-robot pattern formation: a review. Robot Auton Syst 2017;91:83–100.
Wang Y, Cheng L, Hou Z-G, Yu J, Tan M. Optimal formation of multirobot systems based on a recurrent neural network. IEEE Trans Neural Netw Learn Syst 2015;27(2):322–333.
Wan S, Lu J, Fan P. Semi-centralized control for multi robot formation. In: 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE). IEEE; 2017. p. 31–36.
Dutta A, Dasgupta P, Nelson C. Distributed configuration formation with modular robots using (sub) graph isomorphism-based approach. Auton Robot 2019;43(4):837–857.
Dutta A, Dasgupta P. 2016. formation and Information collection by modular robotic systems. In: Simultaneous configuration IEEE international conference on robotics and automation (ICRA). IEEE; 2016, p. 5216–5221.
Beard RW, Lawton J, Hadaegh FY. A coordination architecture for spacecraft formation control. IEEE Trans Control Syst Technol 2001;9(6):777–790.
Ren W. Consensus strategies for cooperative control of vehicle formations. IET Control Theory Appl 2007;1(2):505–512.
Peng Z, Yang S, Wen G, Rahmani A. 2014. Distributed consensus-based robust adaptive formation control for nonholonomic mobile robots with partial known dynamics. Math Probl Eng. 2014:
Wei H, Lv Q, Duo N, Wang G, Liang B. Consensus algorithms based multi-robot formation control under noise and time delay conditions. Appl Sci 2019;9(5):1004.
Wang C, He P, Li H, Tian J, Wang K, Li Y. Noise-tolerance consensus formation control for multi-robotic networks. Trans Inst Meas Control. 2020;42(8):1569–1581.
Aditya P, Apriliani E, Zhai G, Arif DK. 2019. Formation control of multi-robot motion systems and state estimation using extended kalman filter. In: International Conference on Electrical Engineering and Informatics (ICEEI). IEEE; 2019, p. 99–104.
Listmann KD, Masalawala MV, Adamy J. 2009. Consensus for formation control of nonholonomic mobile robots. In: IEEE international conference on robotics and automation. IEEE; 2009, p. 3886–3891.
Briñón-Arranz L, Renzaglia A, Schenato L. Multirobot symmetric formations for gradient and hessian estimation with application to source seeking. IEEE Trans Robot 2019;35(3):782–789.
Zhu S, Wang D, Low CB. Cooperative control of multiple uavs for source seeking. J Intell Robot Syst 2013;70(1-4):293–301.
Antonelli G, Arrichiello F, Caccavale F, Marino A. Decentralized time-varying formation control for multi-robot systems. Int Jo Robot Res 2014;33(7):1029–1043.
Goodwine B. Modeling a multi-robot system with fractional-order differential equations. In: 2014 IEEE International Conference On Robotics and Automation (ICRA). IEEE; 2014, p.1763–1768.
Heymans N, Bauwens J-C. Fractal rheological models and fractional differential equations for viscoelastic behavior. Rheol Acta 1994;33(3):210–219.
Mayes J. 2012. Reduction and approximation in large and infinite potential-driven flow networks. Citeseer.
Habibi G, Kingston Z, Xie W, Jellins M, McLurkin J. 2015. estimation and Motion controllers for collective transport by multi-robot systems. In: Distributed centroid IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2015, p. 1282–1288.
Bo G, Dai L, Cimini LJ. Routing strategies in multihop cooperative networks. IEEE Trans Wirel Commun 2009;8(2):843–855.
McLurkin J, Yamins D. Dynamic task assignment in robot swarms. In Robotics: Science and Systems, vol. 8. Citeseer; 2005.
Montijano E, Cristofalo E, Zhou D, Schwager M, Saguees C. Vision-based distributed formation control without an external positioning system. IEEE Trans Robot 2016;32(2):339–351.
Aranda M, López-Nicolás G, Sagüés C, Mezouar Y. Formation control of mobile robots using multiple aerial cameras. IEEE Trans Robot 2015;31(4):1064–1071.
Kuriki Y, Namerikawa T. Formation control with collision avoidance for a multi-uav system using decentralized mpc and consensus-based control. SICE J Control Measur Syst Integr 2015;8(4):285–294.
Alonso-Mora J, Montijano E, Nägeli T, Hilliges O, Schwager M, Rus D. Distributed multi-robot formation control in dynamic environments. Auton Robot 2019;43(5):1079–1100.
Alonso-Mora J, Baker S, Rus D. Multi-robot formation control and object transport in dynamic environments via constrained optimization. Int J Robot Res 2017;36(9):1000–1021.
Deng G, Zhang H, Zhong H, Miao Z, Li L, Yu M, Jonathan Wu QM. 2019. Distributed Multi-robot formation control based on two-layer nearest neighbor information (tnni) consensus. In: IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE; 2019, p. 1091–1097.
Rahimi R, Abdollahi F, Naqshi K. Time-varying formation control of a collaborative heterogeneous multi agent system. Robot Auton Syst 2014;62(12):1799–1805.
Burns A, Schulze B, John A St. Persistent multi-robot formations with redundancy. In: Distributed Autonomous Robotic Systems. Springer; 2018. p. 133–146.
Chen Z, Jiang C, Guo Y. 2019. Distance-based formation control of a three-robot system. In: Chinese Control And Decision Conference (CCDC). IEEE; 2019, p. 5501–5507.
Abichandani P, Levin K, Bucci D. 2019. Decentralized formation coordination of multiple quadcopters under communication constraints. In: International Conference on Robotics and Automation (ICRA). IEEE; 2019. p. 3326–3332.
Otte M, Correll N. Any-com multi-robot path-planning with dynamic teams: multi-robot coordination under communication constraints. In: Experimental Robotics. Springer; 2014, p. 743–757.
Otte M, Correll N. Any-com multi-robot path-planning: Maximizing collaboration for variable bandwidth. In: Distributed autonomous robotic systems. Springer; 2013, p. 161–173.
Pan Z, Wang D, Deng H, Li K. A virtual spring method for the multi-robot path planning and formation control. Int J Control Autom Syst 2019;17(5):1272–1282.
Zhang F, Chen W. 2007. Self-healing for mobile robot networks with motion synchronization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE: 2007. p. 3107–3112.
Cao K, Qiu Z, Xie L. Relative docking and formation control via range and odometry measurements. IEEE Trans Control Netw Syst 2019;7(2):912–922.
Mei W, Bullo F. 2017. Lasalle invariance principle for discrete-time dynamical systems: a concise and self-contained tutorial. arXiv:1710.03710.
Fidan B, Dasgupta S, Anderson BDO. Adaptive range-measurement-based target pursuit. Int J Adapt Control Signal Process 2013;27(1-2):66–81.
Güler S, Fidan B, Dasgupta S, Anderson BDO, Shames I. Adaptive source localization based station keeping of autonomous vehicles. IEEE Trans Autom Control 2016;62(7):3122–3135.
Nguyen T. M., Qiu Z, Cao M, Nguyen T. H. , Xie L. Single landmark distance-based navigation. IEEE Trans Control Syst Technol 2020;28(5):2021–2028.
Jiang B, Deghat M, Anderson BDO. Simultaneous velocity and position estimation via distance-only measurements with application to multi-agent system control. IEEE Trans Autom Control 2016;62(2): 869–875.
He Y, Zhu L, Sun G, Dong M. Study on formation control system for underwater spherical multi-robot. Microsyst Technol 2019;25(4):1455–1466.
Hauri S, Alonso-Mora J, Breitenmoser A, Siegwart R, Beardsley P. Multi-robot formation control via a real-time drawing interface. In: Field and service robotics. Springer; 2014. p. 175–189.
Reynolds CW. Flocks, herds and schools: A distributed behavioral model. In: Proceedings of the 14th annual conference on Computer graphics and interactive techniques; 1987. p. 25–34.
Olfati-Saber R. Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Trans Autom Control 2006;51(3):401–420.
Alonso-Mora J, Breitenmoser A, Rufli M, Beardsley P, Siegwart R. Optimal reciprocal collision avoidance for multiple non-holonomic robots. In: Distributed autonomous robotic systems. Springer; 2013. p. 203–216.
Jia Y, Wang L. Leader–follower flocking of multiple robotic fish. IEEE/ASME Trans Mechatron 2014;20(3):1372–1383.
Gunn T, Anderson J. Dynamic heterogeneous team formation for robotic urban search and rescue. J Comput Syst Sci 2015;81(3):553–567.
Cai X, De Queiroz M. Adaptive rigidity-based formation control for multirobotic vehicles with dynamics. IEEE Trans Control Syst Technol 2014;23(1):389–396.
Derhami V, Momeni Y. Applying reinforcement learning in formation control of agents. In: Intelligent Distributed Computing IX. Springer; 2016. p. 297–307.
Khan A, Tolstaya E, Ribeiro A, Kumar V. Graph policy gradients for large scale robot control. In: Conference on Robot Learning; 2020. p. 823–834.
Jiang C, Chen Z, Guo Y. Learning decentralized control policies for multi-robot formation. In: 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE; 2019p. 758–765. This study uses deep-learning for learning control policies to achieve multi-robot formation from the robot’s local observation without inter-robot communication.
Lin J, Yang X, Zheng P, Cheng H. 2019. End-to-end decentralized multi-robot navigation in unknown complex environments via deep reinforcement learning. In: IEEE International Conference on Mechatronics and Automation (ICMA). IEEE; 2019, p. 2493–2500.
Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O. 2017. Proximal policy optimization algorithms. arXiv:1707.06347.
Xiao H, Philip Chen C L. Leader-follower consensus multi-robot formation control using neurodynamic-optimization-based nonlinear model predictive control. IEEE Access 2019;7:43581–43590.
Desai JP. Modeling multiple teams of mobile robots: a graph theoretic approach. In: Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No. 01CH37180), vol. 1. IEEE; 2001. p. 381–386.
Kaminka GA, Lupu I, Agmon N. Construction of optimal control graphs in multi-robot systems. In: Distributed Autonomous Robotic Systems. Springer; 2018, p. 163–175.
Yoo SJ, Park BS. Connectivity-preserving approach for distributed adaptive synchronized tracking of networked uncertain nonholonomic mobile robots. IEEE Trans Cybern 2017;48(9):2598–2608.
Yoo SJ, Park BS. Connectivity preservation and collision avoidance in networked nonholonomic multi-robot formation systems: Unified error transformation strategy. Automatica 2019;103:274–281.
Dai Y, Kim Y, Wee S, Lee D, Lee S. A switching formation strategy for obstacle avoidance of a multi-robot system based on robot priority model. ISA Trans 2015;56:123–134.
Lee S-M, Kim H, Myung H, Yao X. Cooperative coevolutionary algorithm-based model predictive control guaranteeing stability of multirobot formation. IEEE Trans Control Syst Technol 2014;23(1): 37–51.
Benzerrouk A, Adouane L, Martinet P. Stable navigation in formation for a multi-robot system based on a constrained virtual structure. Robot Auton Syst 2014;62(12):1806–1815.
Adouane L. 2009. Orbital obstacle avoidance algorithm for reliable and on-line mobile robot navigation. Portuguese Journal Robotica N79, automacao controlo and instrumentacao.
Kim D-H, Kim J-H. A real-time limit-cycle navigation method for fast mobile robots and its application to robot soccer. Robot Auton Syst 2003;42(1):17–30.
Jie MS, Baek JH, Hong YS, Lee KW. Real time obstacle avoidance for mobile robot using limit-cycle and vector field method. In: International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. Springer; 2006, p. 866–873.
Choi S, Kim J. 2019. Three dimensional formation control to pursue an underwater evader utilizing underwater robots measuring the sound generated from the evader. IEEE Access 7:150720–150728.
Reddy P V, Justh E W, Krishnaprasad PS. Motion Camouflage in three dimensions. In: proceedings of the 45th IEEE Conference on Decision and Control. . IEEE; 2006. p. 3327–3332.
Liu C, He J, Zhu S, Chen C. Dynamic topology inference via external observation for multi-robot formation control. In: 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM). IEEE; 2019. pages p. 6 This research presents the topology inference problem of multi-robot formation control systems via external observation, which is first to require no prior knowledge of system dynamics.
Nascimento TP, Conceiċao AGS, Moreira AP. Multi-robot nonlinear model predictive formation control: the obstacle avoidance problem. Robotica 2016;34(3):549.
Peng L, Guan F, Perneel L, Fayyad-Kazan H, Timmerman M. Decentralized multi-robot formation control with communication delay and asynchronous clock. J Intell Robot Syst 2018;89(3-4): 465–484.
Houska B, Ferreau HJ, Diehl M. An auto-generated real-time iteration algorithm for nonlinear mpc in the microsecond range. Automatica 2011;47(10):2279–2285.
Xu D, Zhang X, Zhu Z, Chen C, Yang P. Behavior-based formation control of swarm robots. mathematical Problems in Engineering; 2014.
Vásárhelyi G, Cs V, Somorjai G, Tarcai N, Szörényi T, Nepusz T, Vicsek T. 2014. Outdoor flocking and Formation flight with autonomous aerial robots. In: IEEE/RSJ International Conference on Intelligent Robots and SystemsIEEE; 2014. p. 3866–3873.
Dang AD, La HM, Horn J. Distributed formation control for autonomous robots following desired shapes in noisy environment. In: IEEE international conference on multisensor fusion and integration for intelligent systems (MFI). IEEE; 2016. p. 285–290.
Huang J, Wu W, Ning Y, Zhou N, Xu Z. 2019. A behavior control scheme for multi-robot systems under human intervention. In: Chinese Control Conference (CCC)IEEE; 2019. p. 6189–6193.
Shen D, Sun W, Sun Z. Adaptive pid formation control of nonholonomic robots without leader’s velocity information. ISA Trans 2014;53(2):474–480.
Gallardo N, Pai K, Erol BA, Benavidez P, Mo J. 2016. Parrot bebop drone. In: Formation control implementation using kobuki turtlebots World Automation Congress (WAC)IEEE; 2016. p. 1–6.
Li G, St-Onge D, Pinciroli C, Gasparri A, Garone E, Beltrame G. This work suggests distributed behaviors for progressively deployed swarm robots, and shows how a formation can gradually grow in time, with guaranteed convergence for the joining process. Auton Robot 2019;43(6):1505–1521.
Dutta A, Ufimtsev V, Asaithambi A. Correlation clustering based coalition formation for multi-robot task allocation. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 2019. p. 906–913.
Demaine ED, Immorlica N. Correlation clustering with partial information. In: Approximation, Randomization, and Combinatorial Optimization.. Algorithms and Techniques. Springer; 2003. p. 1–13.
Ge X, Han Q-L, Zhang X-M. Achieving cluster formation of multi-agent systems under aperiodic sampling and communication delays. IEEE Trans Ind Electron 2017;65(4):3417– 3426.
Khalil HK. Universal integral controllers for minimum-phase nonlinear systems. IEEE Trans Autom Control 2000;45(3):490–494.
Sutton RS, Barto AG. 2018. Reinforcement an introduction learning. MIT press.
Liu Y, Bucknall R. A survey of formation control and motion planning of multiple unmanned vehicles. Robotica 2018;36(7):1019–1047.
Abichandani P, Benson HY, Kam M. Decentralized multi-vehicle path coordination under communication constraints. In: 2011 IEEE/RSJ International Conference On Intelligent Robots and Systems. IEEE; 2011. p. 2306–2313.
Abichandani P, Mallory K, Hsieh M-yA. Experimental multi-vehicle path coordination under communication connectivity constraints. In: Experimental Robotics. Springer; 2013. p. 183–197.
Abichandani P, Torabi S, Basu S, Benson H. Mixed integer nonlinear programming framework for fixed path coordination of multiple underwater vehicles under acoustic communication constraints. IEEE J Ocean Eng 2015;40(4):864–873.
Kanayama Y, Kimura Y, Miyazaki F, Noguchi T. A stable tracking control method for an autonomous mobile robot. In: Proceedings IEEE International Conference on Robotics and Automation. IEEE; 1990. p. 384–389.