Encyclopedia of Robotics

Living Edition
| Editors: Marcelo H Ang, Oussama Khatib, Bruno Siciliano

Networked Robots

  • Sarah TangEmail author
  • Vijay Kumar
Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-41610-1_21-1

Synonyms

Definition

Networked robotics studies teams of robots that utilize a communication network to coordinate with each other, sensors, computers, or humans to accomplish complex goals. Robots can be terrestrial, aerial, or underwater and can communicate implicitly – detecting each other using sensors, such as cameras or LIDAR – or, explicitly, sending messages in the form of light, sound, or radio signals. Research in this area aims to enable teams of robots to self-organize to complete complex tasks. The availability of multiple robots allows for greater efficiency and redundancy such that tasks can still be completed even if some robots fail. The communication network allows robots to leverage data collected by other agents, for example, sensor data about a remote location or feedback data from a previous attempt of the same task, to adapt their own actions. These capabilities give networked robots the potential to impact many industries, including...

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References

  1. Alonso-Mora J, Montijano E, Schwager M, Rus D (2016) Distributed multi-robot formation control among obstacles: a geometric and optimization approach with consensus. In: IEEE international conference on robotics and automation (ICRA), Stockholm, pp 5356–5363Google Scholar
  2. Atanasov N, Ny JL, Pappas GJ (2014) Distributed algorithms for stochastic source seekign with mobile robot networks. ASME J Dyn Syst Meas Control 137(3). https://doi.org/10.1115/1.4027892 CrossRefGoogle Scholar
  3. Augugliaro F, Schoellig AP, D’Andrea R (2013) Dance of the flying machines: methods for designing and executing an aerial dance choreography. IEEE Robot Autom Mag 20(4):96–104.  https://doi.org/10.1109/MRA.2013.2275693 CrossRefGoogle Scholar
  4. Cao Y, Yu W, Ren W, Chen G (2013) An overview of recent progress in the study of distributed multi-agent coordination. IEEE Trans Ind Inf 9(1):427–438.  https://doi.org/10.1109/TII.2012.2219061 CrossRefGoogle Scholar
  5. Chaimowicz L, Cowley A, Gomez-Ibanez D, Grocholsky B, Hsieh MA, Hsu H, Keller JF, Kumar V, Swaminathan R, Taylor CJ (2005) Deploying air-ground multi-robot teams in urban environments. In: Parker LE, Schneider, FE, Schultz, AC (eds) Multi-robot systems. From swarms to intelligent automata, vol III. Springer, Dordrecht, pp 223–234. https://doi.org/10.1007/1-4020-3389-3_18
  6. Charrow B, Kahn G, Patil S, Liu S, Goldberg KY, Abbeel P, Michael N, Kumar V (2015) Information-theoretic planning with trajectory optimization for dense 3d mapping. In: Robotics: science and systemsGoogle Scholar
  7. Choset H (2001) Coverage for robotics – a survey of recent results. Ann Math Artif Intell 31(1):113–126. https://doi.org/10.1023/A:1016639210559
  8. Choudhary S, Carlone L, Nieto C, Rogers J, Christensen HI, Dellaert F (2017) Distributed mapping with privacy and communication constraints: lightweight algorithms and object-based models. Int J Robot Res (IJRR) 36(12):1286–1311CrossRefGoogle Scholar
  9. Cornejo A, Nagpal R (2015) Distributed range-based relative localization of robot swarms. In: Akin HL, Amato NM, Isler V, van der Stappen AF (eds) Algorithmic foundations of robotics XI: selected contributions of the eleventh international workshop on the algorithmic foundations of robotics. Springer, Cham, pp 91–107. https://doi.org/10.1007/978-3-319-16595-0_6 Google Scholar
  10. Detweiler C, Banerjee S, Doniec M, Jiang M, Peri F, Chen RF, Rus D (2014) Adaptive decentralized control of mobile underwater sensor networks and robots for modeling underwater phenomena. J Sens Actuator Netw 3(2):113–149.  https://doi.org/10.3390/jsan3020113 CrossRefGoogle Scholar
  11. Fink J, Ribeiro A, Kumar V (2012) Robust control for mobility and wireless communication in cyber? Physical systems with application to robot teams. Proc IEEE 100(1):164–178CrossRefGoogle Scholar
  12. Hausman K, Müller J, Hariharan A, Ayanian N, Sukhatme GS (2015) Cooperative multi-robot control for target tracking with onboard sensing. Int J Robot Res 34(13):1660–1677CrossRefGoogle Scholar
  13. Hock A, Schoellig AP (2016) Distributed iterative learning control for a team of quadrotors. In: IEEE conference 636 on decision and control (CDC), MelbourneGoogle Scholar
  14. Jimnez-Gonzlez A, de Dios JRM, Ollero A (2013) Testbeds for ubiquitous robotics: a survey. Robot Autom Syst 61(12):1487–1501Google Scholar
  15. Jing G, Tosun T, Yim M, Kress-Gazit H (2016) An end-to-end system for accomplishing tasks with modular robots. In: Proceedings of robotics: science and systems, Ann ArborGoogle Scholar
  16. Julian BJ, Angermann M, Schwager M, Rus D (2012) Distributed robotic sensor networks: an information-theoretic approach. Int J Robot Res 31(10):1134–1154. https://doi.org/10.1177/0278364912452675 CrossRefGoogle Scholar
  17. Kolling A, Walker P, Chakraborty N, Sycara K, Lewis M (2016) Human interaction with robot swarms: a survey. IEEE Trans Human Mach Syst 46(1):9–26.  https://doi.org/10.1109/THMS.2015.2480801 CrossRefGoogle Scholar
  18. Kushleyev A, Mellinger D, Kumar V (2012) Towards a swarm of agile micro quadrotors. In: Robotics: science and systemsGoogle Scholar
  19. Levine S, Abbeel P (2014) Learning neural network policies with guided policy search under unknown dynamics. In: Neural information processing systems (NIPS), MontrealGoogle Scholar
  20. Lindsey Q, Mellinger D, Kumar V (2011) Construction of cubic structures with quadrotor teams. In: Robotics: science and systems (RSS), Los AngelesGoogle Scholar
  21. Luna R, Bekris KE (2011) Push and swap: fast cooperative path-finding with completeness guarantees. In: Proceedings of the twenty-second international joint conference on artificial intelligence (IJCAI), Barcelona, pp 294–300Google Scholar
  22. Mather T, Hsieh MA (2011) Distributed robot ensemble control for deployment to multiple sites. In: Robotics: science and systemsGoogle Scholar
  23. McLurkin J, Smith J, Frankel J, Sotkowitz D, Blau D, Schmidt B (2006) Speaking swarmish: human-robot interface design for large swarms of autonomous mobile robots. In: AAAI spring symposium, Palo Alto, pp 72–75Google Scholar
  24. Mohta K, Turpin M, Kushleyev A, Mellinger D, Michael N, Kumar V (2016) QuadCloud: a rapid response force with quadrotor teams. In: Experimental robotics: the 14th international symposium on experimental robotics. Springer, Cham, pp 577–590. https://doi.org/10.1007/978-3-319-23778-7_38 Google Scholar
  25. Nikolaidis S, Lasota P, Ramakrishnan R, Shah J (2015) Improved human-robot team performance through cross-training, an approach inspired by human team training practices. Int J Robot Res (IJRR) 34(14): 1711–1730CrossRefGoogle Scholar
  26. Oh KK, Park MC, Ahn HS (2015) A survey of multi-agent formation control. Automatica 53:424–440. https://doi.org/10.1016/j.automatica.2014.10.022 MathSciNetCrossRefGoogle Scholar
  27. Omidshafiei S, Pazis J, Amato C, How JP, Vian J (2017) Deep decentralized multi-task multi-agent reinforcement learning under partial observability. https://arxiv.org/abs/1703.06182
  28. Pickem D, Glotfelter P, Wang L, Mote M, Ames A, Feron E, Egerstedt M (2016) The robotarium: a remotely accessible swarm robotics research testbed. https://arxiv.org/abs/1609.04730. Accessed 30 June 2017
  29. Preiss JA, Honig W, Sukhatme GS, Ayanian N (2017) Crazyswarm: a large nano-quadcopter swarm. In: IEEE international conference on robotics and automation (ICRA), Marina Bay SandsGoogle Scholar
  30. Prorok A, Kumar V (2016) A macroscopic privacy model for heterogeneous robot swarms. In: International conference on swarm intelligenceGoogle Scholar
  31. Prorok A, Hsieh MA, Kumar V (2015) Fast redistribution of a swarm of heterogeneous robots. In: International conference on bio-inspired information and communications technologies, New YorkGoogle Scholar
  32. Prorok A, Hsieh MA, Kumar V (2017) The impact of diversity on optimal control policies for heterogeneous robot swarms. IEEE Trans Robot (T-RO) 33(2): 346–358CrossRefGoogle Scholar
  33. Ramaithitima R, Whitzer M, Bhattacharya S, Kumar V (2016) Automated creation of topological maps in unknown environments using a swarm of resource-constrained robots. IEEE Robot Autom Lett (RA-L) 1(2):746–753CrossRefGoogle Scholar
  34. Ribeiro A, Schizas I, Roumeliotis S, Giannakis G (2010) Kalman filtering in wireless sensor networks – incorporating communication cost in state estimation problems. IEEE Control Syst Mag 30(2):66–86MathSciNetCrossRefGoogle Scholar
  35. Romanishin JW, Gilpin K, Claici S, Rus D (2015) 3d m-blocks: self-reconfiguring robots capable of locomotion via pivoting in three dimensions. In: IEEE international conference on robotics and automation (ICRA), Seattle, pp 1925–1932Google Scholar
  36. Rubenstein M, Cornejo A, Nagpal R (2014) Programmable self-assembly in a thousand-robot swarm. Science 345(6198):795–799.  https://doi.org/10.1126/science.1254295 CrossRefGoogle Scholar
  37. Saulnier K, Saldana D, Prorok A, Pappas GJ, Kumar V (2015) Resilient flocking for mobile robot teams. IEEE Robot Autom Lett (R-AL) 2(2):1039–1046.  https://doi.org/10.1109/LRA.2017.2655142 CrossRefGoogle Scholar
  38. Schwager M, Michael N, Kumar V, Rus D (2011) Time scales and stability in networked multi-robot systems. In: IEEE international conference on robotics and automation (ICRA), Shanghai, pp 3855–3862Google Scholar
  39. Schwager M, Vitus MP, Powers S, Rus D, Tomlin CJ (2017) Robust adaptive coverage control for robotic sensor networks. IEEE Trans Control Netw Syst 4(3):462–476.  https://doi.org/10.1109/TCNS.2015.2512326.Sept MathSciNetCrossRefGoogle Scholar
  40. Solovey K, Halperin D (2014) k-color multi-robot motion planning. Int J Robot Res (IJRR) 33(1):82–97CrossRefGoogle Scholar
  41. Sreenath K, Kumar V (2013) Dynamics, control and planning for cooperative manipulation of payloads suspended by cables from multiple quadrotor robots. In: Robotics: science and systems (RSS)Google Scholar
  42. Tang S, Kumar V (2018) A complete algorithm for generating safe trajectories for multi-robot teams. In: Bicchi A, Burgard W (eds) Robotics research, vol 2. Springer International Publishing, Cham, pp 599–616CrossRefGoogle Scholar
  43. Turpin M, Michael N, Kumar V (2014a) CAPT: concurrent assignment and planning of trajectories for multiple robots. Int J Robot Res 33(1):98–112CrossRefGoogle Scholar
  44. Turpin M, Mohta K, Michael N, Kumar V (2014b) Goal assignment and trajectory planning for large teams of interchangeable robots. Autom Robots 37(4):401–415CrossRefGoogle Scholar
  45. Varshavskaya P, Kaelbling LP, Rus D (2009) Efficient distributed reinforcement learning through agreement. In: Asama H, Kurokawa H, Ota J, Sekiyama K (eds) Distributed autonomous robotic systems 8. Springer, Berlin/Heidelberg, pp 367–378. https://doi.org/10.1007/978-3-642-00644-9_33 CrossRefGoogle Scholar
  46. Wurman PR, D’Andrea R, Mountz M (2008) Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Mag 29(1):9–20Google Scholar
  47. Yang P, Freeman RA, Gordon GJ, Lynch KM, Srinivasa SS, Sukthankar R (2010) Decentralized estimation and control of graph connectivity for mobile sensor networks. Automatica 46(2):390–396MathSciNetCrossRefGoogle Scholar
  48. Yu J, Rus D (2015) An effective algorithmic framework for near optimal multi-robot path planning. In: The international symposium on robotics research (ISRR), Sestri LevanteGoogle Scholar
  49. Zavlanos MM, Egerstedt MB, Pappas GJ (2011) Graph-theoretic connectivity control of mobile robot networks. Proc IEEE 99(9):1525–1540.  https://doi.org/10.1109/JPROC.2011.2157884 CrossRefGoogle Scholar
  50. Zavlanos MM, Ribeiro A, Pappas GJ (2013) Network integrity in mobile robotic networks. IEEE Trans Autom Control 58(1):3–18.  https://doi.org/10.1109/TAC.2012.2203215 MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.GRASP LabUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.School of Engineering and Applied ScienceUniversity of PennsylvaniaPhiladelphiaUSA

Section editors and affiliations

  • Jee-Hwan Ryu
    • 1
  1. 1.School of Mechanical EngineeringKorea University of Technology & EducationCheon-AnRepublic of Korea