An introductory review of swarm technology for spacecraft on-orbit servicing

Abstract

This review paper presents a comprehensive evaluation and forward-looking perspective on the underexplored topic of servicing target objects using spacecraft swarms. Such targets can be known or unknown, cooperative or uncooperative, and pose significant challenges in modern space operations due to their inherent complexity and unpredictability. Successfully servicing space objects is vital for active debris removal and broader on-orbit servicing tasks such as satellite maintenance, repair, refueling, orbital assembly, and construction. Significant effort has been invested in the literature to explore the servicing of targets using a single spacecraft. Given its advantages and benefits, this paper expands the discussion to encompass a swarm approach to the problem. This review covers various single-spacecraft approaches and presents a critical examination of the existing, although limited, body of work dedicated to servicing orbital objects using multiple spacecraft. The focus is also broadened to include some influential studies concerning the characterization, capture, and manipulation of physical objects by general multiagent systems, a subject with significant parallels to the core interest of this manuscript. Furthermore, this article also delves into the realm of simultaneous localization and mapping, highlighting its application within close-proximity operations in space, especially when dealing with unknown uncooperative targets. Special attention is paid to the benefits that this field can receive from distributed multiagent architectures. Finally, an exploration of the promising field of swarm robotics is presented, with an emphasis on its potential to revolutionize the servicing of orbital target objects. Concurrently, a survey of general research directly engaging swarms in the orbital context is conducted. This review aims to bridge the knowledge gap and stimulate further research in the underexplored domain of servicing space targets with spacecraft swarms.

Keywords

space robotics / on-orbit servicing / close-proximity operations / guidance and navigation / swarm robotics / multiagent systems / object capture and manipulation / simultaneous localization and mapping

References

[1] Papadopoulos E, Aghili F, Ma O, Lampariello R. Robotic manipulation and capture in space: a survey. Front Robot AI. 2021;8:686723.

[2] Nanjangud A, Blacker PC, Bandyopadhyay S, Gao Y. Robotics and AI-enabled on-orbit operations with future generation of small satellites. Proc IEEE. 2018;106(3):429-439.

[3] Spenko M. Making contact: a review of robotic attachment mechanisms for extraterrestrial applications. Adv Intell Syst. 2023;5(3):2100063.

[4] Opromolla R, Fasano G, Rufino G, Grassi M. Uncooperative pose estimation with a LIDAR-based system. Acta Astronaut. 2015;110:287-297.

[5] Opromolla R, Fasano G, Rufino G, Grassi M. A model-based 3d template matching technique for pose acquisition of an uncooperative space object. Sensors. 2015;15(3):6360-6382.

[6] Bai H. ICP algorithm: theory, practice and its slam-oriented taxonomy. Appl Comput Eng. 2023;2(1):10-21.

[7] Opromolla R, Fasano G, Rufino G, Grassi M. Pose estimation for spacecraft relative navigation using model-based algorithms. IEEE Trans Aerosp Electron Syst. 2017;53(1):431-447.

[8] Woods JO, Christian JA. Lidar-based relative navigation with respect to non-cooperative objects. Acta Astronaut. 2016;126:298-311.

[9] Zhu Z, Xiang W, Huo J, Yang M, Zhang G, Wei L. Non-cooperative target pose estimation based on improved iterative closest point algorithm. J Syst Eng Electron. 2022;33(1):1-10.

[10] Christian JA, Robinson SB, D’Souza CN, Ruiz JP. Cooperative relative navigation of spacecraft using flash light detection and ranging sensors. J Guid Control Dyn. 2014;37(2):452-465.

[11] Adams D, Majji M. Velocimeter Lidar-based relative rate estimation for autonomous rendezvous, proximity operations, and docking applications. Paper presented at:44th annual AAS Guidance, Navigation and Control Conference (AAS 22-174). 2022;Breckenridge, CO, USA.

[12] Tweddle BE, Saenz-Otero A. Relative computer vision-based navigation for small inspection spacecraft. J Guid Control Dyn. 2015;38(5):969-978.

[13] Liu C, Hu W. Relative pose estimation for cylinder-shaped spacecrafts using single image. IEEE Trans Aerosp Electron Syst. 2014;50(4):3036-3056.

[14] Sharma S, D’Amico S. Comparative assessment of techniques for initial pose estimation using monocular vision. Acta Astronaut. 2016;123:435-445.

[15] Capuano V, Kim K, Harvard A, Chung SJ. Monocular-based pose determination of uncooperative space objects. Acta Astronaut. 2020;166:493-506.

[16] Sharma S, D’Amico S. Neural network-based pose estimation for noncooperative spacecraft rendezvous. IEEE Trans Aerosp Electron Syst. 2020;56(6):4638-4658.

[17] Piazza M, Maestrini M, Di Lizia P. Monocular relative pose estimation pipeline for uncooperative resident space objects. J Aerosp Inf Syst. 2022;19(9):613-632.

[18] Kaki S, Deutsch J, Black K, Cura-Portillo A, Jones BA, Akella MR. Real-time image-based relative pose estimation and filtering for spacecraft applications. J Aerosp Inf Syst. 2023;20(6):290-307.

[19] Setterfield TP, Miller DW, Saenz-Otero A, Frazzoli E, Leonard JJ. Inertial properties estimation of a passive on-orbit object using polhode analysis. J Guid Control Dyn. 2018;41(10):2214-2231.

[20] Meng Q, Han D, Wang Z. A model-free method for attitude estimation and inertial parameter identification of a noncooperative target. Adv Space Res. 2023;71(3):1735-1751.

[21] Feng Q, Zhu ZH, Pan Q, Hou X. Relative state and inertia estimation of unknown tumbling spacecraft by stereo vision. IEEE Access. 2018;6:54126-54138.

[22] Pesce V, Lavagna M, Bevilacqua R. Stereovision-based pose and inertia estimation of unknown and uncooperative space objects. Adv Space Res. 2017;59(1):236-251.

[23] Yu F, He Z, Qiao B, Yu X. Stereo-vision-based relative pose estimation for the rendezvous and docking of noncooperative satellites. Math Probl Eng. 2014;2014:461283.

[24] Xu Z, Chen Y, Xu Z. Optimal guidance and collision avoidance for docking with the rotating target spacecraft. Adv Space Res. 2019;63(10):3223-3234.

[25] Park H, Zappulla II, R, Zagaris C, Virgili-Llop J, Romano M. Nonlinear model predictive control for spacecraft rendezvous and docking with a rotating target. Paper presented at:27th AAS/AIAA Space Flight Mechanics Meeting (AAS 17-496). 2017;San Antonio, TX, USA.

[26] Oestreich CE, Linares R, Gondhalekar R. Autonomous six-degree-of-freedom spacecraft docking with rotating targets via reinforcement learning. J Aerosp Inf Syst. 2021;18(7):417-428.

[27] Espinoza A, Hettrick H, Albee K, Hernandez AC, Linares R. End-to-end framework for close proximity in-space robotic missions. 70th Annual International Astronautical Congress. 2019;Washington DC, USA.

[28] Albee K, Oestreich C, Specht C, et al. A robust observation, planning, and control pipeline for autonomous rendezvous with tumbling targets. Fronti Robot AI. 2021;8:641338.

[29] Kim SC, Shepperd SW, Norris LH, Goldberg H, Wallace MS. Mission design and trajectory analysis for inspection of a host spacecraft by a microsatellite. Paper presented at: IEEE Aerospace Conference. 2007;Big Sky, MT, USA (pp. 640-662).

[30] Maestrini M, Di Lizia P. Guidance strategy for autonomous inspection of unknown non-cooperative resident space objects. J Guid Control Dyn. 2022;45(6):1126-1136.

[31] Fourie D, Tweddle B, Ulrich S, Otero AS. Vision-based relative navigation and control for autonomous spacecraft inspection of an unknown object. Paper presented at: AIAA Guidance, Navigation, and Control (GNC) Conference (AIAA 2013-4759). 2013.

[32] Yashwanth N. Spacecraft Motion Planning and Control under Probabilistic Uncertainty for Coordinated Inspection and Safe Learning. PhD Thesis. California Institute of Technology;2021.

[33] Nakka YKK, Hönig W, Choi C, Harvard A, Rahmani A, Chung SJ. Information-based guidance and control architecture for multispacecraft on-orbit inspection. AIAA Scitech Forum, virtual event, Paper No. AIAA 2021-1103, AIAA, 2021, 1-22. January 2021.

[34] Sabatini M, Volpe R, Palmerini GB. Centralized visual based navigation and control of a swarm of satellites for on-orbit servicing. Acta Astronaut. 2020;171:323-334.

[35] Mian S, Garrett T, Glandon A, et al. Autonomous spacecraft inspection with free-flying drones. Paper presented at: AIAA/IEEE 39th Digital Avionics Systems Conference (DASC). October 11-15, 2020;San Antonio, Texas (pp. 24-32).

[36] Corpino S, Stesina F. Inspection of the cis-lunar station using multipurpose autonomous cubesats. Acta Astronaut. 2020;175:591-605.

[37] Faghihi S, Tavana S, de Ruiter AHJ. Multiple spacecraft coordination and motion planning for full-coverage inspection of large complex space structures. Acta Astronaut. 2023;202:119-129.

[38] Udrea B, Nayak M. A cooperative multi-satellite mission for controlled active debris removal from low Earth orbit. Paper presented at: IEEE Aerospace Conference. 2015;Big Sky, MT, USA (pp. 1-15).

[39] Rekleitis G, Papadopoulos E. On-orbit cooperating space robotic servicers handling a passive object. IEEE Trans Aerosp Electron Syst. 2015;51(2):802-814.

[40] Han N, Luo J, Ma W, Yuan J. Integrated identification and control for nanosatellites reclaiming failed satellite. Acta Astronaut. 2018;146:387-398.

[41] Fan S, Xing F, Liu X, Chen X, You Z. Quick-response attitude takeover control using multiple servicing spacecraft based on inertia properties identification. Adv Space Res. 2022;70(7):1890-1916.

[42] Chang H, Huang P, Zhang Y, Meng Z, Liu Z. Distributed control allocation for spacecraft attitude takeover control via cellular space robot. J Guid Control Dyn. 2018;41(11):2499-2506.

[43] Matsuka K, Santamaria-Navarro A, Capuano V, Harvard A, Rahmani A, Chung S-J. Collaborative pose estimation of an unknown target using multiple spacecraft. Paper presented at: IEEE Aerospace Conference (50100). 2021;Big Sky, MT, USA (pp. 1-11).

[44] Song Y, Fang X, Liu B, Li C, Li Y, Yang SX. A novel foraging algorithm for swarm robotics based on virtual pheromones and neural network. Appl Soft Comput. 2020;90:106156.

[45] Liu J, Li H, Sun L, et al. In-situ resources for infrastructure construction on Mars: a review. Int J Transport Sci Technol. 2022;11(1):1-16.

[46] Cheng Z, Hou X, Zhang X, Zhou L, Guo J, Song C. In-orbit assembly mission for the space solar power station. Acta Astronaut. 2016;129:299-308.

[47] Sinhmar H, Kress-Gazit H. Guaranteed encapsulation of targets with unknown motion by a minimalist robotic swarm. IEEE Trans Robot. 2024;40:816-830.

[48] Bono A, D’Alfonso L, Fedele G, Gazi V. Target capturing in an ellipsoidal region for a swarm of double integrator agents. IEEE/CAA J Autom Sin. 2022;9(5):801-811.

[49] Li X, Liu X, Wang G, Han S, Shi C, Che H. Cooperative target enclosing and tracking control with obstacles avoidance for multiple nonholonomic mobile robots. Appl Sci. 2022;12(6):2876.

[50] Franchi A, Stegagno P, Oriolo G. Decentralized multi-robot encirclement of a 3D target with guaranteed collision avoidance. Auton Robots. 2016;40:245-265.

[51] Hamed O, Hamlich M. Improvised multi-robot cooperation strategy for hunting a dynamic target. Paper presented at:2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT). 2020;Marrakech, Morocco (pp. 1-4).

[52] Lei Q, Wisse M. Object grasping by combining caging and force closure. Paper presented at: Proceedings of the 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV). 2016. (pp. 1-8).

[53] Wan W, Shi B, Wang Z, Fukui R. Multirobot object transport via robust caging. IEEE Trans Syst Man Cybernet Syst. 2020;50(1):270-280.

[54] Shinde V, Dutta A, Saxena A. Experiments on multi-agent capture of a stochastically moving object using modified projective path planning. Robotica. 2013;31(2):267-284.

[55] Yoshikawa T. Passive and active closures by constraining mechanisms. Proc IEEE Int Conf Robot Automat. 1996;2:1477-1484.

[56] 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.

[57] Marino A, Pierri F. A two-stage approach for distributed cooperative manipulation of an unknown object without explicit communication and unknown number of robots. Rob Auton Syst. 2018;103:122-133.

[58] Miyano T, Romberg J, Egerstedt M. Primal-dual gradient dynamics for cooperative unknown payload manipulation without communication. Paper presented at: American Control Conference (ACC). 2020;Denver, CO, USA (pp. 2061-2067).

[59] Miyano T, Romberg J, Egerstedt M. Distributed force/position optimization dynamics for cooperative unknown payload manipulation. Paper presented at:59th IEEE Conference on Decision and Control (CDC). 2020;Jeju, Korea (South) (pp. 5366-5373).

[60] Sharma P, Saxena A, Dutta A. Multi-agent form closure capture of a generic 2D polygonal object based on projective path planning. Paper presented at: Proceedings of the 26th Computers and Information in Engineering Conference. 2006.

[61] Mishra T, Guha P, Dutta A, Venkatesh KS. Stochastic re-grasp planning for vision-aided capture of deforming and moving object. Mechatronics. 2009;19(4):510-519.

[62] Dai Y, Kim Y, Wee S, Lee D, Lee S. Symmetric caging formation for convex polygonal object transportation by multiple mobile robots based on fuzzy sliding mode control. ISA Trans. 2016;60:321-332.

[63] Schranz M, Umlauft M, Sende M, Elmenreich W. Swarm robotic behaviors and current applications. Front Robot AI. 2020;7:36.

[64] Wang ZD, Kumar V. Object closure and manipulation by multiple cooperating mobile robots. Proc IEEE Int Conf Robot Automat. 2002;1:394-399.

[65] Berman S, Lindsey Q, Sakar MS, Kumar V, Pratt SC. Experimental study and modeling of group retrieval in ants as an approach to collective transport in swarm robotic systems. Proc IEEE. 2011;99(9):1470-1481.

[66] Cadena C, Carlone L, Carrillo H, et al. Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans Robot. 2016;32(6):1309-1332.

[67] Lichter M, Dubowsky S. State, shape, and parameter estimation of space objects from range images. Paper presented at: IEEE International Conference on Robotics and Automation, April 26 to May 1, 2004;New Orleans, Louisiana (Vol. 3, pp. 2974-2979).

[68] Augenstein S, Rock S. Simultaneous Estimation of Target Pose and 3-D Shape Using the FastSLAM Algorithm. AIAA 2009-5782. AIAA Guidance, Navigation, and Control Conference. August 2009.

[69] Sonnenburg A, Tkocz M, Janschek K. EKF-SLAM based approach for spacecraft rendezvous navigation with unknown target spacecraft. IFAC Proc Vol. 2010;43(15):339-344.

[70] Segal S, Carmi A, Gurfil P. Stereovision-based estimation of relative dynamics between noncooperative satellites: theory and experiments. IEEE Trans Control Syst Technol. 2014;22(2):568-584.

[71] Schnitzer F, Janschek K, Willich G. Experimental results for imagebased geometrical reconstruction for spacecraft rendezvous navigation with unknown and uncooperative target spacecraft. Paper presented at: IEEE/RSJ International Conference on Intelligent Robots and Systems. October 7-12, 2012;Vilamoura-Algarve, Portugal (pp. 5040-5045).

[72] Mur-Artal R, Montiel JMM, Tardos JD. ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans Robot. 2015;31(5):1147-1163.

[73] Dor M, Tsiotras P. ORB-SLAM applied to spacecraft noncooperative rendezvous. Paper presented at:2018 Space Flight Mechanics Meeting (AIAA 2018-1963). January 2018.

[74] Thomas DJ, Kelly S, Black JT. A monocular SLAM method for satellite proximity operations. Am Control Conf. 2016;2016:4035-4040.

[75] Tweddle BE, Saenz-Otero A, Leonard JJ, Miller DW. Factor graph modeling of rigid-body dynamics for localization, mapping, and parameter estimation of a spinning object in space. J Field Robot. 2015;32(6):897-933.

[76] Dellaert F, Kaess M. Factor graphs for robot perception. 2017.

[77] Setterfield TP, Miller DW, Leonard JJ, Saenz-Otero A. Mapping and determining the center of mass of a rotating object using a moving observer. Int J Robot Res. 2018;37(1):83-103.

[78] Setterfield TP, Miller D, Leonard JJ, Saenz-Otero A. Smoothing-based estimation of an inspector satellite trajectory relative to a passive object. Paper presented at: IEEE Aerospace Conference. 2017;Big Sky, MT, USA (pp. 1-11).

[79] Vassallo C, Tabib W, Peterson K. Orbital SLAM. 12th Conference on Computer and Robot Vision. 2015;Halifax, NS, Canada (pp. 305-312).

[80] Dor M, Skinner KA, Tsiotras P, Driver T. Visual SLAM for asteroid relative navigation. Paper presented at: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2021;Nashville, TN, USA (pp. 2066-2075).

[81] Dor M, Driver T, Getzandanner K, Tsiotras P. AstroSLAM: autonomous monocular navigation in the vicinity of a celestial small body–theory and experiments. arXiv:2212.00350 [cs.RO]. 2022.

[82] Driver T, Skinner KA, Dor M, Tsiotras P. AstroVision: towards autonomous feature detection and description for missions to small bodies using deep learning. Acta Astronaut. 2023;210:393-410.

[83] Villa J, McMahon JW, Hockman B, Nesnas I. Autonomous navigation and dense shape reconstruction using stereophotogrammetry at small celestial bodies. Paper presented at: AAS Guidance, Navigation, and Control Conference. 2022;Breckenridge, CO, USA.

[84] Bercovici B, McMahon JW. Robust autonomous small-body shape reconstruction and relative navigation using range images. J Guid Control Dyn. 2019;42(7):1473-1488.

[85] Guo C, Deng S, Xu Y, He J. Identification, attitude estimation and capture of tumbling spacecraft based on SLAM. 2020.

[86] Maestrini M, Di Lizia P. COMBINA: relative navigation for unknown uncooperative resident space object. AIAA 2022-2384. AIAA SciTech 2022 Forum. January 2022.

[87] Maestrini M, De Luca MA, Di Lizia P. Relative navigation strategy about unknown and uncooperative targets. J Guid Control Dyn. 2023;46(9):1708-1725.

[88] Pesce V, Agha-mohammadi A-a, Lavagna M. Autonomous navigation &mapping of small bodies. Paper presented at: IEEE Aerospace Conference. 2018;Big Sky, MT, USA (pp. 1-10).

[89] Brandonisio A, Lavagna M, Guzzetti D. Reinforcement learning for uncooperative space objects smart imaging path-planning. J Astronaut Sci. 2021;68:1145-1169.

[90] Brandonisio A, Capra L, Lavagna M. Deep reinforcement learning spacecraft guidance with state uncertainty for autonomous shape reconstruction of uncooperative target. Adv Space Res. 2023. In press.

[91] Stacey N, Dennison K, D’Amico S. Autonomous asteroid characterization through nanosatellite swarming. Paper presented at: IEEE Aerospace Conference (AERO). 2022;Big Sky, MT, USA (pp. 1-21).

[92] Kegeleirs M, Grisetti G, Birattari M. Swarm SLAM: challenges and perspectives. Front Robot AI. 2021;8:618268.

[93] Lajoie P-Y, Beltrame G. Swarm-SLAM: sparse decentralized collaborative simultaneous localization and mapping framework for multirobot systems. IEEE Robot Automat Lett. 2024;9(1):475-482.

[94] Mahboob H, Yasin JN, Jokinen S, Haghbayan M-H, Plosila J, Yasin MM. DCP-SLAM: distributed collaborative partial swarm SLAM for efficient navigation of autonomous robots. Sensors. 2023;23(2):1025.

[95] Huang Y, Shan T, Chen F, Englot B. DiSCo-SLAM: distributed scan context-enabled multi-robot LiDAR SLAM with two-stage global-local graph optimization. IEEE Robot Automat Lett. 2022;7(2):1150-1157.

[96] Macario Barros A, Michel M, Moline Y, Corre G, Carrel F. A comprehensive survey of visual SLAM algorithms. Robotics. 2022;11(1):24.

[97] Denniston CE, Chang Y, Reinke A, et al. Loop closure prioritization for efficient and scalable multi-robot SLAM. IEEE Robot Automat Lett. 2022;7:9651-9658.

[98] Grisetti G, Kummerle R, Stachniss C, Burgard W. A tutorial on graph-based SLAM. IEEE Intell Transport Syst Mag. 2010;2(4):31-43.

[99] Kaess M, Johannsson H, Roberts R, Ila V, Leonard JJ, Dellaert F. iSAM2: incremental smoothing and mapping using the Bayes tree. Int J Robot Res. 2012;31(2):216-235.

[100] Tourani A, Bavle H, Sanchez-Lopez JL, Voos H. Visual SLAM: what are the current trends and what to expect? Sensors. 2022;22(23):9297.

[101] Tian Y, Koppel A, Bedi AS, How JP. Asynchronous and parallel distributed pose graph optimization. IEEE Robot Automat Lett. 2020;5:5819-5826.

[102] Aerts P, Slaets P, Demeester E. Incorporating moving landmarks within 2D graph-based SLAM for dynamic environments. Paper presented at:6th International Conference on Mechanical Engineering and Robotics Research (ICMERR). 2021;Krakow, Poland (pp. 1-7).

[103] National Geographic. These birds flock in mesmerising swarms of thousands–but why is still a mystery. March 2021. Accessed February 16, 2024. https://www.nationalgeographic.co.uk/animals/2021/03/these-birds-flock-in-mesmerising-swarms-ofthousands- but-why-is-still-a-mystery

[104] Cheraghi AR, Shahzad S, Graffi K. Past, present, and future of swarm robotics. In: Arai K, ed. Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems. Vol. 296. Springer;2022.

[105] Ivanov D, Monakhova U, Ovchinnikov M. Nanosatellites swarm deployment using decentralized differential drag-based control with communicational constraints. 2019.

[106] Liu G-P, Zhang S. A survey on formation control of small satellites. Proc IEEE. 2018;106(3):440-457.

[107] Andrievsky B, Popov AM, Kostin I, Fadeeva J. Modeling and control of satellite formations: a survey. Automation. 2022;3(3):511-544.

[108] D’Arrigo P, Santandrea S. APIES: a mission for the exploration of the main asteroid belt using a swarm of microsatellites. Acta Astronaut. 2006;59(8-11):689-699.

[109] Curtis SA, Truszkowski W, Rilee ML, Clark PE. ANTS for human exploration and development of space. Paper presented at: IEEE Aerospace Conference Proceedings (Cat. No. 03TH8652). 2003;Big Sky, MT, USA (Vol. 1, pp. 1-261).

[110] Vassev E, Hinchey M. Self-awareness in autonomous nanotechnology swarm missions. Paper presented at: Fifth IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshop. 2011;Ann Arbor, MI, USA (pp. 133-136).

[111] Dekens E, Engelen S, Noomen R. A satellite swarm for radio astronomy. Acta Astronaut. 2014;102:321-331.

[112] Nevinskaia AS, Bentum MJ, Engelen S, Monna B, Naeije MC. Orbit design of a swarm for ultra-long wavelength radio interferometry with preliminary swarm and thruster sizing. Acta Astronaut. 2021;188:463-472.

[113] Linz H, Bhatia D, Buinhas L, et al. InfraRed Astronomy Satellite Swarm Interferometry (IRASSI): overview and study results. Adv Space Res. 2020;65(2):831-849.

[114] Lippe C, D’Amico S. Spacecraft swarm dynamics and control about asteroids. Adv Space Res. 2021;67(11):3426-3443.

[115] Nallapu RT, Thangavelautham J. Design and sensitivity analysis of spacecraft swarms for planetary moon reconnaissance through coorbits. Acta Astronaut. 2021;178:854-869.

[116] Lippe C, D’Amico S. Optimal spacecraft swarm reconfiguration through chief orbit refinement. Acta Astronaut. 2021;183:162-175.

[117] Koenig AW, D’Amico S. Robust and safe N-spacecraft swarming in perturbed near-circular orbits. J Guid Control Dyn. 2018;41(8):1643-1662.

[118] Lippe C, D’Amico S. Safe, delta-v-efficient spacecraft swarm reconfiguration using lyapunov stability and artificial potentials. J Guid Control Dyn. 2022;45(2):213-231.

[119] Morgan D, Chung SJ, Blackmore L, Acikmese B, Bayard D, Hadaegh FY. Swarm-keeping strategies for spacecraft under J2 and atmospheric drag perturbations. J Guid Control Dyn. 2012;35(5):1492-1506.

[120] Morgan D, Chung SJ, Hadaegh FY. Spacecraft swarm guidance using a sequence of decentralized convex optimizations. Paper presented at: AIAA/AAS Astrodynamics Specialist Conference. AIAA 2012-4583. August 2012.

[121] Morgan D, Chung SJ, Hadaegh FY. Model predictive control of swarms of spacecraft using sequential convex programming. J Guid Control Dyn. 2014;37(6):1725-1740.

[122] Morgan D, Chung SJ, Hadaegh FY. Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and model predictive control. Paper presented at: AIAA Guidance, Navigation, and Control Conference. AIAA-0599. January 2015.

[123] Quinton F, Grand C, Lesire C. Market approaches to the multi-robot task allocation problem: a survey. J Intell Robot Syst. 2023;107:29.

[124] Bandyopadhyay S, Baldini F, Foust R, et al. Distributed fast motion planning for spacecraft swarms in cluttered environments using spherical expansions and sequence of convex optimization problems. Presented at: 9th International Workshop on Satellite Constellations and Formation Flying;2017 Jun 19-21;Boulder, Colorado. JPL Open Repository;2017. Available from: https://hdl.handle.net/2014/46249

[125] Bandyopadhyay S, Baldini F, Foust R, et al. Distributed spatiotemporal motion planning for spacecraft swarms in cluttered environments. Paper presented at: AIAA SPACE and Astronautics Forum and Exposition. AIAA-5323. September 2017.

[126] Foust RC, Lupu ES, Nakka YK, Chung SJ, Hadaegh FY. Autonomous in-orbit satellite assembly from a modular heterogeneous swarm. Acta Astronaut. 2020;169:191-205.

[127] Hadaegh FY, Chung SJ, Manohara HM. On development of 100-gram-class spacecraft for swarm applications. IEEE Syst J. 2016;10(2):673-684.

[128] Chen T, Shan J, Wen H, Xu S. Review of attitude consensus of multiple spacecraft. Astrodynamics. 2022;6:329-356.

[129] Ge X, Han QL, Zhang XM, Ding D. Dynamic event-triggered control and estimation: a survey. Int J Automat Comput. 2021;18:857-886.

[130] Wu J, Liu M, Wang Y, Cao X. Event-trigger-based cluster coordinated control of spacecraft swarm under switching topology. Aerosp Sci Technol. 2023;135:108200.

[131] Izzo D, Pettazzi L. Autonomous and distributed motion planning for satellite swarm. J Guid Control Dyn. 2007;30(2):449-459.

[132] Pinciroli C, Birattari M, Tuci E, et al. Self-organizing and scalable shape formation for a swarm of Pico satellites. Paper presented at: NASA/ESA Conference on Adaptive Hardware and Systems. 2008;Noordwijk, Netherlands (pp. 57-61).

[133] Ayre M, Izzo D, Pettazzi L. Self assembly in space using behaviour based intelligent components. 2005.

[134] Huang H, Yang L, Zhu Y, Zhang Y. Collective trajectory planning for satellite swarm using inter-satellite electromagnetic force. Acta Astronaut. 2014;104:220-230.

[135] An M, Wang Z, Zhang Y. Self-organizing control strategy for asteroid intelligent detection swarm based on attraction and repulsion. Acta Astronaut. 2017;130:84-96.

[136] Mushet G, Mingotti G, Colombo C, Mcinnes C. Self-organising satellite constellation in geostationary earth orbit. IEEE Trans Aerosp Electron Syst. 2015;51(2):910-923.

[137] Sabatini M, Palmerini GB. Collective control of spacecraft swarms for space exploration. Celest Mech Dyn Astron. 2009;105:229-244.

[138] Elamvazhuthi K, Berman S. Mean-field models in swarm robotics: a survey. Bioinspir Biomim. 2019;15(1):015001.

[139] Eren U, Demirer N, Açıkmeşe B. Density-based feedback control for earth orbiting swarms via velocity fields. IFAC-PapersOnLine. 2018;51(12):44-49.

[140] Yang C, Zhang H, Fu W. Pattern control for large-scale spacecraft swarms in elliptic orbits via density fields. Chin J Aeronaut. 2022;35(3):367-379.

[141] Doerr B, Linares R, Zhu P, Ferrari S. Random finite set theory and optimal control for large spacecraft swarms. In 2019 Space Flight Mechanics Meeting;2019:3729-3732.

[142] Liu X, Meng Z, You Z. Adaptive collision-free formation control for under-actuated spacecraft. Aerosp Sci Technol. 2018;79:223-232.

[143] Xu Y, Luo D, Li D, You Y, Duan H. Target-enclosing affine formation control of two-layer networked spacecraft with collision avoidance. Chin J Aeronaut. 2019;32:2679-2693.

[144] Wang C, Chen D, Liao W, Liang Z. Autonomous obstacle avoidance strategies in the mission of large space debris removal using potential function. Adv Space Res. 2023;72:2860-2873.

[145] Wu Y, Wang W, Zhu H, Wu S, Damaren CJ. Adaptive fault-tolerant control for spacecraft formation under external disturbances with guaranteed performance. Adv Space Res. 2023;72:1583-1592.

[146] Alikhani A, SafaDehghan M, Shafieenejad I. Fault tolerant guidance of under-actuated satellite formation flying using inter-vehicle Coulomb force. 2019.

[147] Kruger J, D’Amico S. Autonomous angles-only multitarget tracking for spacecraft swarms. Acta Astronaut. 2021;189:514-529.

[148] Koenig AW, Kruger J, Sullivan J, D’Amico S. ARTMS: enabling autonomous distributed angles-only orbit estimation for spacecraft swarms. Paper presented at: American Control Conference (ACC). 2021;New Orleans, LA, USA (pp. 4282-4289).

[149] Sanchez H, McIntosh DM, Cannon H, et al. Starling1: swarm technology demonstration. Paper presented at:32nd Annual AIAA/USU Conference on Small Satellites. SSC18-VII-08. 2018.

[150] Matsuka K, Feldman AO, Lupu ES, Chung SJ, Hadaegh FY. Decentralized formation pose estimation for spacecraft swarms. Adv Space Res. 2021;67(11):3527-3545.

[151] Kamal AT, Farrell JA, Roy-Chowdhury AK. Information weighted consensus filters and their application in distributed camera networks. IEEE Trans Autom Control. 2013;58:3112-3125.

[152] Ryu K, Back J. Distributed Kalman-filtering: distributed optimization viewpoint. Paper presented at:2019 IEEE 58th Conference on Decision and Control (CDC). December 1, 2019 (pp. 2640-2645).

[153] Bandyopadhyay S, Chung SJ. Distributed Bayesian filtering using logarithmic opinion pool for dynamic sensor networks. Automatica. 2018;97:7-17.

[154] Timmons T, Beeley J, Bailet G, McInnes CR. Range-based relative navigation for a swarm of centimeter-scale femto-spacecraft. J Guid Control Dyn. 2022;45(9):1583-1597.

[155] Renga A, Grassi M, Tancredi U. Relative navigation in LEO by carrier-phase differential GPS with intersatellite ranging augmentation. Int J Aerosp Eng. 2013;2013:627509.

[156] Shim H, Kim OJ, Yu S, Kee C, Cho DH, Kim HD. Precise in-orbit relative navigation technique for rendezvous mission of CubeSats using only GPS receivers. CEAS Space J. 2023;16:117-137.

[157] Low SY, D’Amico S. Precise distributed satellite navigation: differential GPS with sensor-coupling for integer ambiguity resolution. arXiv preprint arXiv:2310.16108. 2023.

[158] Dianetti AD, Gnam C, Crassidis JL. Spacecraft proximity operations using ultra-wideband communication devices. AIAA 2021-1866. AIAA Scitech 2021 Forum. January 2021.

[159] Tran VP, Garratt MA, Kasmarik K, Anavatti SG, Abpeikar S. Frontier-led swarming: robust multi-robot coverage of unknown environments. Swarm Evol Comput. 2022;75:101171.

[160] Pang B, Song Y, Zhang C, Yang R. Effect of random walk methods on searching efficiency in swarm robots for area exploration. Appl Intell. 2021;51:5189-5199.

[161] Zhang H, Bai L, Qi J, Xiao Y. Area coverage ofsSwarm robotics based on anti-flocking framework with dynamical clustering. Paper presented at: IEEE International Conference on Unmanned Systems (ICUS). October 2022.

[162] Javed S, Tahir Khan M, Ahmad Y. Multi-robot exploration in unstructured and dynamic environments. Int J Eng Works. 2020;07:189-196.

[163] Kwa HL, Leong Kit J, Bouffanais R. Balancing collective exploration and exploitation in multi-agent and multi-robot systems: a review. Front Robot AI. 2022;8:771520.

[164] De Croon GCHE, Dupeyroux JJG, Fuller SB, Marshall JAR. Insectinspired AI for autonomous robots. Sci Robot. 2022;7(67):eabl6334.

[165] Collett M, Chittka L, Collett TS. Spatial memory in insect navigation. Curr Biol. 2013;23(17):R789-R800.

[166] McGuire K, de Croon G, De Wagter C, Tuyls K, Kappen H. Efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone. IEEE Robot Automat Lett. 2017;2(2):1070-1076.

[167] McGuire KN, De Wagter C, Tuyls K, Kappen HJ, de Croon GCHE. Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment. Sci Robot. 2019;4(35):eaaw9710.

[168] Ebrahimi A, Janabi-Sharifi F, Ghanbari A. UavisBug: vision-based 3D motion planning and obstacle avoidance for a mini-UAV in an unknown indoor environment. Can Aeronaut Space J. 2014;60(1):9-21.

[169] McGuire KN, de Croon GCHE, Tuyls K. A comparative study of bug algorithms for robot navigation. Rob Auton Syst. 2019;121:103261.

[170] Xianjia Y, Qingqing L, Queralta JP, Heikkonen J, Westerlund T. Applications of UWB networks and positioning to autonomous robots and industrial systems. Paper preseneted at:10th Mediterranean Conference on Embedded Computing (MECO). June 2021.

[171] Guo K, Qiu Z, Meng W, Xie L, Teo R. Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments. Int J Micro Air Veh. 2017;9(3):169-186.

[172] Cao Y, Yang C, Li R, Knoll A, Beltrame G. Accurate position tracking with a single UWB anchor. Paper presented at: IEEE International Conference on Robotics and Automation (ICRA). 2020;Paris, France (pp. 2344-2350).

[173] He W, Cheng R, Mao K, Yan K, Wei J, Xu Y. A novel anchorless node positioning method for wireless sensor network. J Sens. 2022;2022:5385393.

[174] Safaei A, Sharf I. Velocity estimation for UAVs using ultra wide-band system. Paper presented at: International Conference on Unmanned Aircraft Systems (ICUAS). 2021;Athens, Greece (pp. 202-209).

[175] Heydariaan M, Dabirian H, Gnawali O. AnguLoc: concurrent angle of arrival estimation for indoor localization with UWB radios. Paper presented at:16th International Conference on Distributed Computing in Sensor Systems (DCOSS). 2020;Marina del Rey, CA, USA (pp. 112-119).

[176] Mohamed SAS, Haghbayan MH, Westerlund T, Heikkonen J, Tenhunen H, Plosila J. A survey on odometry for autonomous navigation systems. IEEE Access. 2019;7:97466-97486.

[177] Hüttenrauch M, Šošic A, Neumann G. Deep reinforcement learning for swarm systems. J Mach Learn Res. 2019;20(1):1966-1996.

[178] Tolstaya E, Gama F, Paulos J, Pappas G, Kumar V, Ribeiro A. Learning decentralized controllers for robot swarms with graph neural networks. proceedings of the conference on robot learning. PMLR. 2020;100:671-682.

[179] Bredeche N, Haasdijk E, Prieto A. Embodied evolution in collective robotics: a review. Front Robot AI. 2018;5:12.

[180] Doncieux S, Bredeche N, Mouret JB, Eiben AE. Evolutionary robotics: what, why, and where to. Front Robot AI. 2015;2:4.

[181] Stolfi DH, Danoy G. An evolutionary algorithm to optimise a distributed UAV swarm formation system. Appl Sci. 2022;12(20):10218.

RIGHTS & PERMISSIONS

2024 2024 The Authors. International Journal of Mechanical System Dynamics published by John Wiley & Sons Australia, Ltd on behalf of Nanjing University of Science and Technology.