Một Đánh Giá Phê Bình Về Truyền Thông Trong Hệ Thống Đa Robot

Jennifer Gielis1, Ajay Pal Singh1, Amanda Prorok1
1Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

Tóm tắt

Tóm tắt Mục Đích Của Đánh Giá

Bài đánh giá này tóm tắt vai trò rộng rãi của các định dạng và công nghệ truyền thông trong việc cho phép các hệ thống đa robot. Chúng tôi tiếp cận lĩnh vực này từ hai góc độ: ứng dụng robot cần khả năng giao tiếp để hoàn thành các nhiệm vụ và các công nghệ mạng đã cho phép các hệ thống đa robot mới và tiên tiến hơn.

Những Phát Hiện Gần Đây

Thông qua bài đánh giá này, chúng tôi nhận diện một sự thiếu hụt các công trình nghiên cứu giải quyết một cách tổng thể vấn đề đồng thiết kế và tối ưu hóa giữa robot và các mạng lưới mà chúng sử dụng. Chúng tôi cũng nêu bật vai trò của các phương pháp định hướng dữ liệu và học máy trong việc phát triển các quy trình giao tiếp cho các hệ thống đa robot. Đặc biệt, chúng tôi đề cập đến các công trình gần đây phân tách khỏi các mẫu giao tiếp được thiết kế thủ công, và cũng thảo luận về khoảng cách “từ mô phỏng đến thực tế” trong bối cảnh này.

Từ khóa


Tài liệu tham khảo

Tilley J Automation, robotics, and the factory of the future. McKinsey Market Report; 2017

Kamagaew A, et al. Concept of Cellular Transport Systems in facility logistics. In: Proceedings of the 5th international conference on automation, robotics and applications. IEEE; 2011 https://doi.org/10.1109/ICARA.2011.6144853

Enright JJ, Wurman PR. Optimization and coordinated autonomy in mobile fulfillment systems. Workshops at the Twenty-Fifth AAAI conference on artificial intelligence; 2011

Ma H, et al. Lifelong multi-agent path finding for online pickup and delivery tasks. 2017. arXiv:1705.10868 [cs]

Hyldmar N, He Y, Prorok A. A fleet of miniature cars for experiments in cooperative driving. IEEE International Conference Robotics and Automation (ICRA); 2019. https://doi.org/10.17863/CAM.37116

Dressler F, Hartenstein H, Altintas O, Tonguz O. Inter-vehicle communication: Quo vadis. In: IEEE Communications Magazine; 52.6. 2014. pp. 170–77

Ferreira M, et al. Self-organized traffic control. In: Proceedings of the seventh ACM international workshop on VehiculAr InterNETworking. ACM; 2010. pp. 85–90

Cherubini A, et al. Collaborative manufacturing with physical human-robot interaction. Robotics and Computer-Integrated Manufacturing. 2016;40:1–13.

Noguchi N, Barawid OC. Robot farming system using multiple robot tractors in Japan agriculture. In: IFAC Proceedings volumes; 2011. 44.1. pp. 633–37. https://doi.org/10.3182/20110828-6-IT-1002.03838

Albani D, et al. Monitoring and mapping with robot swarms for agricultural applications. In: 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). 2017

Bonabeau E, et al. Swarm intelligence: from natural to artificial systems. Oxford University Press; 1999

Nagpal R, Shrobe H, Bachrach J. Organizing a global coordinate system from local information on an ad hoc sensor network. In: Information processing in sensor networks. Springer; 2003. pp. 333–348

Rubenstein M, Cornejo A, Nagpal R. Programmable self-assembly in a thousand-robot swarm. In: Science. vol 345.6198; 2014. pp. 795–99

Pugh J, Martinoli A. Relative localization and communication module for small-scale multi-robot systems. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. IEEE; 2006. pp. 188–93

Beckers R, Holland OE, Deneubourg J-L. Fom local actions to global tasks: stigmergy and collective robotics. In: Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic, Volume 1, Volume 2 Prerational Intelligence: Interdisciplinary Perspectives on the Behavior of Natural and Artificial Systems, Volume 3. Springer; 2000. 1008–22

Brambilla M, et al. Swarm robotics: a review from the swarm engineering perspective. In: Swarm Intelligence. Springer; 2013. vol 7.1. pp. 1–41

Grippa P, et al. Drone delivery systems: job assignment and dimensioning. en In: Autonomous Robots; 2019. vol 43.2. pp. 261–74. https://doi.org/10.1007/s10514-018-9768-8

Spieser K, et al. Shared-vehicle mobility-on-demand systems: a fleet operator’s guide to rebalancing empty vehicles. In: Transportation Research Board. 2016

Chinchali S, et al. Network offloading policies for cloud robotics: a learning-based approach. In: Autonomous Robots. Springer; 2021. vol 45.7. pp. 997–1012

Kehoe B, et al. A survey of research on cloud robotics and automation. In: IEEE Transactions on automation science and engineering. IEEE; 2015. vol. 12.2. 398–409

Gielis J, Prorok A, Improving 802.11 p for delivery of safety-critical navigation information in robot-to-robot communication networks. In: IEEE communications magazine. IEEE; 2021. vol 59.1. pp. 16–21

Tarapore D, Groß R, Zauner K-P. Sparse robot swarms: Moving swarms to real-world applications. In: Frontiers in Robotics and AI. vol 7. 2020. pp. 83. This paper outlines some of the key challenges in communications and control when robotic teams are spread over larger volumes (as opposed to dense operations with potentially lower latencies.)

Preiss JA, et al. Crazyswarm: A large nano-quadcopter swarm. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2017. pp. 3299–304

Tolstaya E, et al. Learning decentralized controllers for robot swarms with graph neural networks. In: Conf. Robot Learning 2019. Osaka, Japan. Int. Found. Robotics Res; 2019

Kushleyev A, et al. Towards a swarm of agile micro quadrotors. In: Autonomous Robots; 2013. vol. 35.4. pp. 287–300

Vásárhelyi G, et al. Optimized flocking of autonomous drones in confined environments. In: Science Robotics; 2018. vol. 3.20. p. eaat3536

Prorok A Redundant Robot Assignment on Graphs with Uncertain Edge Costs. en. In: Distributed Autonomous Robotic Systems. Ed. by Correll, N., Schwager, M., and Otte, M. Springer Proceedings in Advanced Robotics; 2019, pp. 313-27. ISBN: 978-3-030-05816-6.

Khamis A, Hussein A, Elmogy A. Multi-robot task allocation: A review of the state-of-the-art. In: Cooperative robots and sensor networks 2015. 2015. pp. 31–51

Yu J, LaValle SM. Optimal multi-robot path planning on graphs: Complete algorithms and effective heuristics. 2015. arXiv:1507.03290 [cs]

Wu W, Bhattacharya S, Prorok A. Multi-robot path deconfliction through prioritization by path prospects. In: IEEE International Conference on Robotics and Automation; 2020. arXiv: 1908.02361

Cáp M, et al. Asynchronous decentralized prioritized planning for coordination in multi-robot system. In: Intelligent robots and systems (IROS), 2013 IEEE/RSJ international conference on; 2013. pp. 3822–29

Desaraju VR, How JP. Decentralized path planning for multi-agent teams with complex constraints. In: Autonomous Robots; 2012. vol. 32.4. pp. 385–403

Dong J, Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach. In, et al. IEEE international conference on robotics and automation (ICRA). IEEE. 2015;2015:5807–14.

Gil S, et al. Guaranteeing spoof-resilient multi-robot networks. In: Autonomous Robots; 2017. vol. 41.6. pp. 1383–1400. https://doi.org/10.1007/s10514-017-9621-5

Saulnier K, et al. Resilient flocking for mobile robot teams. In: IEEE Robotics and Automation Letters; 2017. vol. 2.2. pp. 1039–46

Guerrero-Bonilla L, Saldana D, Kumar V. Dense r-robust formations on lattices. In: 2020 IEEE international conference on robotics and automation (ICRA); 2020. pp. 6633–39

Stojanovic M. Recent advances in high-speed underwater acoustic communications. In: IEEE Journal of Oceanic engineering; 1996. vol. 23.2. pp. 125–36

Wang J. On sign-board based inter-robot communication in distributed robotic systems. In: Robotics and automation. 1045–50. IEEE Comput. Soc. Press. https://doi.org/10.1109/ROBOT.1994.351219

Cao YU, Fukunaga AS, Kahng A. Cooperative Mobile Robotics: Antecedents and Directions. In: Autonomous Robots; 1997. vol 4.1. https://doi.org/10.1023/A:1008855018923

IEEE I. IEEE Standard for Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications. In: 1997. [Accessed 02 Jan 2022]. https://doi.org/10.1109/IEEESTD.1997.85951

Luu BB, et al. A soldier-robot ad hoc network. In: Fifth Annual IEEE international conference on pervasive computing and communications workshops percom workshops 2007 19-23 March, 2007, White Plains, New York, USA. IEEE Computer Society; 2007. pp. 558–563. https://doi.org/10.1109/PERCOMW.2007.13

Houda K, Lakel R. Synchronized communication in a set of autonomous mobile robots using bluetooth technology. In: Procedia Computer Science; 2015. vol 73. pp. 154–61. 10.1016/j.procs.2015.12.061

Naik G, Choudhury B, Park J-M. IEEE 802.11bd & 5G NR V2X: Evolution of radio access technologies for V2X communications. In: IEEE Access; 2019. vol 7. pp. 70169–84. https://doi.org/10.1109/ACCESS.2019.2919489

Ertürk MA, et al. A Survey on LoRaWAN architecture, protocol and technologies. In: Future internet; 2019. vol. 11.10. pp. 216. https://doi.org/10.3390/fi11100216

Feng D, et al. Ultra-reliable and low-latency communications: applications, opportunities and challenges. In: Science China Information Sciences; 2021. vol. 64.2. pp. 1–12. https://doi.org/10.1007/s11432-020-2852-1

Mourad A, et al. A Baseline Roadmap for Advanced Wireless Research Beyond 5G. In: Electronics; 2020. vol. 9.2. https://doi.org/10.3390/electronics9020351

Van den Berg J, Lin M, Manocha D. Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE International conference on robotics and automation. IEEE; 2008. pp. 1928–35

Koutsiamanis R-A, et al. From best effort to deterministic packet delivery for wireless industrial IoT networks. In: IEEE Transactions on industrial informatics; 2018. vol. 14.10. pp. 4468–80. https://doi.org/10.1109/TII.2018.2856884

Fan X, et al. UAV-assisted data dissemination in delay-constrained VANETs. In: Mobile information systems; 2018. vol 2018. pp. 1–12. https://doi.org/10.1155/2018/8548301

Sarr C, et al. Bandwidth estimation for IEEE 802.11-based ad hoc networks. In: IEEE transactions on mobile computing; 2008. vol. 7.10. 1228–41. https://doi.org/10.1109/TMC.2008.41

Yan C, et al. A Comprehensive survey on UAV communication channel modeling. In: IEEE Access; 2019. vol. 7. pp. 107769–92. https://doi.org/10.1109/ACCESS.2019.2933173

Khodayi-mehr R, Kantaros Y, Zavlanos MM. Distributed state estimation using intermittently connected robot networks. In: IEEE transactions on robotics; 2019. vol. 35.3. pp. 709–24. https://doi.org/10.1109/TRO.2019.2897865

Gupta L, Jain R, Vaszkun G. Survey of important issues in UAV communication networks. In: IEEE communications surveys & tutorials; 2016. vol. 18.2. pp. 1123–52. https://doi.org/10.1109/COMST.2015.2495297

Deng L, et al. Delay-constrained topology-transparent distributed scheduling for MANETs. In: IEEE transactions on vehicular technology; 2021. vol. 70.1. pp. 1083–88. https://doi.org/10.1109/TVT.2020.3046856

Elmezughi MK, Thomas A. Performance analysis of OFDM scheme with channel estimation and doppler shift effects. In: Albahit journal of applied sciences; 2021. vol 2.1. pp. 2–8

IEEE I. P1920.2: Working group on vehicle-to-vehicle communications for unmanned aircraft systems; 2022

Ramphull D, et al. A review of mobile ad hoc NETwork (MANET) protocols and their applications. In: 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS); 2021. pp. 204–11. https://doi.org/10.1109/ICICCS51141.2021.9432258

Al-Heety OS, et al. A comprehensive survey: benefits, services, recent works, challenges, security, and use cases for SDN-VANET. In: IEEE Access; 2020. vol. 8. pp. 91028–47. https://doi.org/10.1109/ACCESS.2020.2992580

Srivastava A, Prakash J. Future FANET with application and enabling techniques: Anatomization and sustainability issues. In: Computer science review; 2021. vol. 39. pp. 100359. https://doi.org/10.1016/j.cosrev.2020.100359

Tahir MN, Katz M. Performance evaluation of IEEE 802.11p, LTE and 5G in connected vehicles for cooperative awareness. In: Engineering Reports; 2021. https://doi.org/10.1002/eng2.12467

Hameed Mir Z, Filali F. LTE and IEEE 802.11p for vehicular networking: a performance evaluation. In: EURASIP journal on wireless communications and networking; 2014. vol. 2014.1. https://doi.org/10.1186/1687-1499-2014-89

Ziouva E, Antonakopoulos T. CSMA/CA performance under high traffic conditions: throughput and delay analysis. In: Computer communications; 2002. vol. 25.3. pp. 313–21. https://doi.org/10.1016/S0140-3664(01)00369-3

Adelantado F, et al. Understanding the limits of LoRaWAN. In: IEEE communications magazine; 2017. vol. 55.9. pp. 34–40. https://doi.org/10.1109/MCOM.2017.1600613

Shahin N, Ali R, Kim Y-T. Hybrid slotted-CSMA/CA-TDMA for efficient massive registration of IoT devices. In: IEEE Access; 2018. vol. 6. pp. 18366–82. https://doi.org/10.1109/ACCESS.2018.2815990

Petrosky EE, Michaels AJ, Ridge DB. Network Scalability Comparison of IEEE 802.15.4 and Receiver-Assigned CDMA. IEEE internet of things journal; 2019. vol. 4. pp. 6060–69. https://doi.org/10.1109/JIOT.2018.2884455

Shumeye Lakew D, et al. Routing in flying ad hoc networks: a comprehensive survey. In: IEEE communications surveys & tutorials; 2020. vol. 22.2. pp. 1071–120. 10.1109/COMST.2020.2982452. This paper provides an overview of ad-hoc networks, their taxonomy, features (such as mobility models) and some of the protocols commonly employed. This holds relevance for ad-hoc networks in general.

Hentati AI, Fourati LC. Comprehensive survey of UAVs communication networks. In: Computer Standards & Interfaces; 2020. vol. 72. pp. 103451. https://doi.org/10.1016/j.csi.2020.103451

Chataut R, Akl R. Massive MIMO systems for 5G and beyond networks-overview, recent trends, challenges, and future research direction. In: Sensors (Basel, Switzerland); 2020. vol. 20.10. https://doi.org/10.3390/s20102753

Liu X, Wang X, Efficient antenna selection and user scheduling in 5G Massive MIMO-NOMA system. In,. IEEE 83rd Vehicular Technology Conference (VTC Spring). IEEE. 2016;2016:1–5. https://doi.org/10.1109/VTCSpring.2016.7504208.

Chen H, et al. Ultra-reliable low latency cellular networks: use cases, challenges and approaches. In: IEEE Communications Magazine; 2018. vol. 56.12. pp. 119–25. https://doi.org/10.1109/MCOM.2018.1701178

Zeng Y, Wu Q, Zhang R. Accessing from the sky: A tutorial on UAV communications for 5G and beyond. In: Proceedings of the IEEE; 2019. vol. 107.12. pp. 2327–75. https://doi.org/10.1109/JPROC.2019.2952892

Molina-Masegosa R, Gozalvez J, System level evaluation of LTE-V2V Mode 4 communications and its distributed scheduling. In,. IEEE 85th vehicular technology conference (VTC Spring). IEEE. 2017;2017:1–5. https://doi.org/10.1109/VTCSpring.2017.8108463.

Miao L, Virtusio JJ, Hua K-L. PC5-based cellular-V2X evolution and deployment. In: Sensors (Basel, Switzerland); 2021. vol. 21.3. https://doi.org/10.3390/s21030843

Le T-K, Salim U, Kaltenberger F. An Overview of Physical Layer Design for Ultra-Reliable Low-Latency Communications in 3GPP Releases 15, 16, and 17. In: IEEE Access; 2021. vol. 9. pp. 433–44, https://doi.org/10.1109/ACCESS.2020.3046773

Ali R, et al. URLLC for 5G and Beyond: Requirements, enabling incumbent technologies and network intelligence. In: IEEE Access; 2021. vol. 9. pp. 67064–95. https://doi.org/10.1109/ACCESS.2021.3073806

Navarro-Ortiz J, et al. A Survey on 5G Usage scenarios and traffic models. In: IEEE Communications Surveys & Tutorials; 2020. vol. 22.2. pp. 905–29. https://doi.org/10.1109/COMST.2020.2971781

Lagen S, Patriciello N, Giupponi L. Cellular and Wi-Fi in unlicensed spectrum: competition leading to convergence. In: 2020 2nd 6G Wireless Summit (6G SUMMIT).[S.1]:. IEEE; 2020, pp. 1–5. https://doi.org/10.1109/6GSUMMIT49458.2020.9083786

Di Zhang D, et al. Performance Analysis of FD-NOMA-Based Decentralized V2X Systems. In: IEEE transactions on communications; 2019. vol. 67.7. pp. 5024–36. https://doi.org/10.1109/TCOMM.2019.2904499

Shahab MB, et al. Grant-Free Non-Orthogonal Multiple Access for IoT: A Survey. IEEE communications surveys & tutorials; 2020. vol. 22.3. pp. 1805–38. https://doi.org/10.1109/COMST.2020.2996032

Soria E, Schiano F, Floreano D. Distributed predictive drone swarms in cluttered environments. In: IEEE Robotics and Automation Letters; 2021. vol. 7.1

Zhou X, et al. Decentralized spatial-temporal trajectory planning for multicopter swarms. In: arXiv:2106.12481; 2021

Tordesillas J, How JP. MADER: Trajectory planner in multiagent and dynamic environments. In: IEEE Transactions on Robotics; 2021

Luis CE, Vukosavljev M, Schoellig AP. Online trajectory generation with distributed model predictive control for multi-robot motion planning. In: IEEE Robotics and Automation Letters; 2020. vol. 5.2. pp. 604–11

Kepler ME, Stilwell DJ. An approach to reduce communication for multi-agent mapping applications. In: 2020 IEEE/RSJ international conference on intelligent robots and systems (IROS); 2020. pp. 4814–20. https://doi.org/10.1109/IROS45743.2020.9341117

Trawny N, Roumeliotis SI, Giannakis GB. Cooperative multi-robot localization under communication constraints. In: 2009 IEEE international conference on robotics and automation; 2009. pp. 4394–400. https://doi.org/10.1109/ROBOT.2009.5152606

Muralidharan A, Mostofi Y. Communication-aware robotics: exploiting motion for communication. In: Annual Review of Control, Robotics, and Autonomous Systems; 2021. vol. 4.1. pp. 115–139. https://doi.org/10.1146/annurev-control-071420-080708

Grancharova A, et al. UAVs trajectory planning by distributed MPC under radio communication path loss constraints. In: Journal of Intelligent & Robotic Systems; 2015. vol. 79.1. pp. 115–34

Zeng T, et al. Joint communication and control for wireless autonomous vehicular platoon systems. In: IEEE Transactions on Communications; 2019. https://doi.org/10.1109/TCOMM.2019.2931583

Zeng T, et al. Wireless communications and control for swarms of cellular-connected UAVs. In: 2018 52nd Asilomar Conference on Signals, Systems, and Computers; 2018. https://doi.org/10.1109/ACSSC.2018.8645472

Dimarogonas D, Johansson K. Bounded control of network connectivity in multi-agent systems. In: IET Control Theory & Applications; 2010. vol. 4.8. pp. 1330–38. https://doi.org/10.1049/iet-cta.2009.0229

Ji M, Egerstedt M. Distributed coordination control of multiagent systems while preserving connectedness. In: IEEE Transactions on Robotics; 2007. vol. 23.4. pp. 693–703. https://doi.org/10.1109/TRO.2007.900638

Stachura M, Frew EW. Cooperative target localization with a communication-aware unmanned aircraft system. In: Journal of Guidance, Control, and Dynamics; 2011. vol. 34.5. pp. 1352–62. https://doi.org/10.2514/1.51591

Olivieri de Souza BJ, Endler M. Coordinating movement within swarms of UAVs through mobile networks. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops); 2015. pp. 154–59. https://doi.org/10.1109/PERCOMW.2015.7134011

Mardani A, Chiaberge M, Giaccone P. Communication-aware UAV path planning. In: IEEE Access; 2019. vol. 7. pp. 52609–21. https://doi.org/10.1109/ACCESS.2019.2911018

Hsieh MA, et al. Maintaining network connectivity and performance in robot teams. In: Journal of field robotics; 2008. vol. 25. pp. 111–131

Schouwenaars T, et al. Multivehicle path planning for nonline-of-sight communication. In: Journal of Field Robotics; 2006. vol. 23. pp. 269–90. https://doi.org/10.1002/rob.20119

Fridman A, et al. Distributed path planning for connectivity under uncertainty by ant colony optimization. In: 2008 American Control Conference; 2008. pp. 1952–58. https://doi.org/10.1109/ACC.2008.4586778

Hayat S, et al. Multi-objective UAV path planning for search and rescue. In: 2017 IEEE International Conference on Robotics and Automation (ICRA); 2017, pp. 5569–74. https://doi.org/10.1109/ICRA.2017.7989656

Doniec M, et al. Using optical communication for remote underwater robot operation. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2010. pp. 4017–4022

Vasilescu I, et al. Data collection, storage, and retrieval with an underwater sensor network. In: Proceedings of the 3rd international conference on embedded networked sensor systems. San Diego, California, USA. Association for Computing Machinery; 2005. pp. 154–65. https://doi.org/10.1145/1098918.1098936

Zolich A, et al. Survey on communication and networks for autonomous marine systems. In: Journal of Intelligent & Robotic Systems; 2019. vol. 95.3. pp. 789–813

Hollinger GA, et al. Communication protocols for underwater data collection using a robotic sensor network. In: 2011 IEEE GLOBECOM Workshops (GC Wkshps); 2011. pp. 1308–13. https://doi.org/10.1109/GLOCOMW.2011.6162397

Anderson J, Hollinger GA. Communication planning for cooperative terrain-based underwater localization. In: Sensors; 2021. vol. 21.5. p. 1675. https://doi.org/10.3390/s21051675

Best G, et al. Planning-aware communication for decentralised multi-robot coordination. In: 2018 IEEE International Conference on Robotics and Automation (ICRA); 2018. pp. 1050–57. https://doi.org/10.1109/ICRA.2018.8460617

Alshehri A, Miller T, Sonenberg L. Modeling communication of collaborative multiagent system under epistemic planning. In: International Journal of Intelligent Systems; 2021. vol. 36.10. pp. 5959–80. https://doi.org/10.1002/int.22536

Unhelkar VV, Shah JA. Contact: Deciding to communicate during time-critical collaborative tasks in unknown, deterministic domains. In: Thirtieth AAAI Conference on Artificial Intelligence; 2016

Tsiogkas N, Lane DM. Towards an online approach for knowledge communication planning: Extended Abstract. In: 2019 international symposium on multi-robot and multi-agent systems (MRS); 2019. pp. 142–44. https://doi.org/10.1109/MRS.2019.8901086

Marcotte RJ, et al. Optimizing multi-robot communication under bandwidth constraints. In: Autonomous Robots; 2020. vol. 44.1. pp. 43–55

Otsu K, Supervised autonomy for communication-degraded subterranean exploration by a robot team. In, et al. IEEE Aerospace Conference. IEEE. 2020;2020:1–9. https://doi.org/10.1109/AERO47225.2020.9172537.

Tranzatto M. CERBERUS in the DARPA Subterranean Challenge. In: Science Robotics; 2022. vol. 7.66. p. eabp9742

Mascarich F, A self-deployed multi-channel wireless communications system for subterranean robots. In, et al. IEEE Aerospace Conference. IEEE. 2020;2020:1–8. https://doi.org/10.1109/AERO47225.2020.9172496.

Kantaros Y, Zavlanos MM. Distributed communication-aware coverage control by mobile sensor networks. In: Automatica; 2016. vol. 63. pp. 209–20. https://doi.org/10.1016/j.automatica.2015.10.035

Zavlanos MM, Ribeiro A, Pappas GJ. Network integrity in mobile robotic networks. In: IEEE Transactions on Automatic Control; 2013. vol. 58.1. pp. 3–18. https://doi.org/10.1109/TAC.2012.2203215

Kassir A, Fitch R, Sukkarieh S. Decentralised information gathering with communication costs. In: 2012 IEEE international conference on robotics and automation; 2012. pp. 2427–32. https://doi.org/10.1109/ICRA.2012.6224806

Fink J, Ribeiro A, Kumar V. Robust control for mobility and wireless communication in Cyber-Physical systems with application to robot teams. In: Proceedings of the IEEE; 2012. vol. 100.1. pp. 164–78. https://doi.org/10.1109/JPROC.2011.2161427

Le Ny J, Ribeiro A, Pappas GJ, Adaptive communication-constrained deployment of mobile robotic networks. In,. american control conference (ACC). Montreal, QC. IEEE. 2012;2012:3742–7. https://doi.org/10.1109/ACC.2012.6314804.

Stephan J, et al. Concurrent control of mobility and communication in multirobot systems. In: IEEE Transactions on Robotics; 2017. vol. 33.5. pp. 1248–54. https://doi.org/10.1109/TRO.2017.2705119

Halsted T, et al. A survey of distributed optimization methods for multi-robot systems; 2021. arXiv:2103.12840 [cs]

Amato C, et al. Decentralized control of partially observable Markov decision processes. In: 52nd IEEE conference on decision and control. IEEE; 2013. pp. 2398–405

Parker L. ALLIANCE: an architecture for fault tolerant multirobot cooperation. In: IEEE Transactions on Robotics and Automation; 1998. vol. 14.2. pp. 220–40. https://doi.org/10.1109/70.681242

Mammeri Z. Reinforcement learning based routing in networks: Review and classification of approaches. In: IEEE Access; 2019. vol. 2019. pp. 55916–50. https://doi.org/10.1109/ACCESS.2019.2913776

Zheng Z, Sangaiah AK, Wang T. Adaptive communication protocols in flying ad hoc network. In: IEEE Communications Magazine; 2018. vol. 56.1. pp. 136–42. https://doi.org/10.1109/MCOM.2017.1700323

Bithas PS, et al. A Survey on Machine-Learning Techniques for UAV-Based Communications. In: Sensors (Basel, Switzerland); 2019. vol. 19.23. https://doi.org/10.3390/s19235170

Wang J-L, et al. Machine learning based rapid 3D channel modeling for UAV communication networks. In: CCNC 2019: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), Piscataway, NJ. IEEE; 2019. pp. 1–5. https://doi.org/10.1109/CCNC.2019.8651718

Rajeswaran A, et al. Towards generalization and simplicity in continuous control. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, California, USA. Curran Associates Inc; 2017. pp. 6553–64

Tobin J, et al. Domain randomization for transferring deep neural networks from simulation to the real world. In: IEEE/RSJ International Conference on Intelligent Robots and Systems; 2017. pp. 23–30. https://doi.org/10.1109/IROS.2017.8202133

Everett M, Chen YF, How JP. Motion planning among dynamic, decision-making agents with deep reinforcement learning. In: IEEE/RSJ international conference on intelligent robots and systems; 2018. pp. 3052–59. https://doi.org/10.1109/IROS.2018.8593871

Sartoretti G, et al. PRIMAL: pathfinding via reinforcement and imitation multi-agent learning. In: IEEE Robotics and Automation Letters; 2019. vol. 4.3. pp. 2378–85

Prorok A, et al. The holy grail of multi-robot planning: Learning to generate online-scalable solutions from offline-optimal experts In: Autonomous Agents and Multi-Agent Systems (AAMAS); 2022

Paulos J, et al. Decentralization of multiagent policies by learning what to communicate. In: 2019 International Conference on Robotics and Automation (ICRA). IEEE; 2019. pp. 7990–96

Scarselli F, et al. The graph neural network model. In: IEEE Transactions on Neural Networks; 2009. vol. 20.1. pp. 61–80

Gama F, et al. Convolutional neural network architectures for signals supported on graphs. In: IEEE Trans. Signal Process; 2019. vol. 67.4. pp. 1034–49

Prorok A. Graph neural networks for learning robot team coordination. In: Federated AI for Robotics Workshop, IJCAI-ECAI/ICML/AAMAS 2018; arXiv:1805.03737 [cs]; 2018

Kortvelesy R, Prorok A. ModGNN: Expert policy approximation in multi-agent systems with a modular graph neural network architecture. In: International Conference on Robotics and Automation (ICRA); 2021

Khan A,et al. Graph policy gradients for large scale robot control. Ed. Kaelbling LP, Kragic D, Sugiura K. Proceedings of the conference on robot learning. PMLR; 2020. vol. 100. pp. 823–34

Tolstaya E, et al. Learning decentralized controllers for robot swarms with graph neural networks; 2019. arXiv:1903.10527 [cs]

Li Q, et al. Graph neural networks for decentralized multi-robot path planning. In: Autonomous Agents and Multi-Agent Systems (AAMAS); 2020

Hu T-K, et al. Scalable perception-action-communication loops with convolutional and graph neural networks. In: IEEE Transactions on Signal and Information Processing over Networks; 2022. vol. 8. pp. 12–24. https://doi.org/10.1109/TSIPN.2021.3139336

Li Q, et al. Message-aware graph attention networks for large-scale multi-robot path planning. In: IEEE Robotics and Automation Letters; 2021. vol. 6.3. pp. 5533–40

Sukhbaatar S, Szlam A, Fergus R. Learning multiagent communication with backpropagation. In: Proceedings of the 30th international conference on neural information processing systems,’16. Curran Associates Inc; 2016. pp. 2252–60

Singh A, Jain T, Sukhbaatar S. Learning when to communicate at scale in multiagent cooperative and competitive tasks. In: International Conference on Learning Representations; 2018

Jiang J, Lu Z. Learning attentional communication for multi-agent cooperation. In: Proceedings of the 32nd international conference on neural information processing systems,’18. Red Hook, NY, USA. Curran Associates Inc; 2018. pp. 7265–75

Das A, et al. TarMAC: Targeted multi-agent communication. In: Proceedings of the 36th International Conference on Machine Learning; 2019. pp. 1538–46. PMLR, This paper demonstrates a learning-based approach to multi-robot communications, particularly, learning what and when to communicate so as to maximize task-specific rewards.

Serra-Gómez, Á, et al. With whom to communicate: learning efficient communication for multi-robot collision avoidance. In: 2020 IEEE/RSJ international conference on intelligent robots and systems (IROS); 2020. https://doi.org/10.1109/IROS45743.2020.9341762

Blumenkamp J, Prorok A. The emergence of adversarial communication in multi-agent reinforcement learning; 2020. arXiv:2008.02616 [cs]

Mechraoui A, et al. Co-design for wireless networked control of an intelligent mobile robot. In: ICINCO 2009-6th International Conference on Informatics in Control, Automation and Robotics; 2009, p. 7

Mechraoui A, et al. A co-design distributed Bayesian approach for decision and scheduling of WNCs. In: IFAC Proceedings Volumes; 2011. vol. 44.1. pp. 14970–75

Blumenkamp J, et al. A framework for real-world multi-robot systems running decentralized GNN-based policies. In: International Conference on Robotics and Automation (ICRA); 2022