1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Control, Robotics, and Autonomous Systems
  4. Volume 3, 2020
  5. Article

Annual Review of Control, Robotics, and Autonomous Systems

Volume 3, 2020

Mobile Sensor Networks and Control: Adaptive Sampling of Spatiotemporal Processes

Abstract

The control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.

Keyword(s): consensus, cooperative control, Gaussian processes, graph theory, mutual information, self-propelled particle, Voronoi partition
Loading

Article metrics loading...

/content/journals/10.1146/annurev-control-073119-090634
2020-05-03
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/control/3/1/annurev-control-073119-090634.html?itemId=/content/journals/10.1146/annurev-control-073119-090634&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Choset H. 2001. Coverage for robotics—a survey of recent results. Ann. Math. Artif. Intell. 31:113–26
     [Google Scholar]
  2. 2. 
    Cassandras CG, Li W. 2005. Sensor networks and cooperative control. Eur. J. Control 11:436–63
     [Google Scholar]
  3. 3. 
    Chung H, Oh S, Shim DH, Sastry SS 2011. Toward robotic sensor webs: algorithms, systems, and experiments. Proc. IEEE 99:1562–86
     [Google Scholar]
  4. 4. 
    Hero AO, Cochran D. 2011. Sensor management: past, present, and future. IEEE Sens. J. 11:3064–75
     [Google Scholar]
  5. 5. 
    Curtin TB, Bellingham JG. 2009. Progress toward autonomous ocean sampling networks. Deep-Sea Res. II 56:62–67
     [Google Scholar]
  6. 6. 
    Wang Y, Liu Y, Guo Z 2012. Three-dimensional ocean sensor networks: a survey. J. Ocean Univ. China 11:436–50
     [Google Scholar]
  7. 7. 
    Dunbabin M, Marques L. 2012. Robotics for environmental monitoring. IEEE Robot. Autom. Mag. 19:24–39
     [Google Scholar]
  8. 8. 
    Leonard N. 2016. Cooperative vehicle environmental monitoring. Springer Handbook of Ocean Engineering MR Dhanak, NI Xiros441–58 Cham, Switz: Springer
     [Google Scholar]
  9. 9. 
    Willcox JS, Bellingham JG, Zhang Y, Baggeroer AB 2001. Performance metrics for oceanographic surveys with autonomous underwater vehicles. IEEE J. Ocean. Eng. 26:711–25
     [Google Scholar]
  10. 10. 
    Lermusiaux PFJ. 2007. Adaptive modeling, adaptive data assimilation and adaptive sampling. Physica D 230:172–96
     [Google Scholar]
  11. 11. 
    Delcroix T, McPhaden MJ, Dessier A, Gouriou Y 2005. Time and space scales for sea surface salinity in the tropical oceans. Deep-Sea Res. I 52:787–813
     [Google Scholar]
  12. 12. 
    Ouimet M, Cortés J. 2014. Robust, distributed estimation of internal wave parameters via inter-drogue measurements. IEEE Trans. Control Syst. Technol. 22:980–94
     [Google Scholar]
  13. 13. 
    Caiti A, Munafó A, Viviani R 2007. Adaptive on-line planning of environmental sampling missions with a team of cooperating autonomous underwater vehicles. Int. J. Control 80:1151–68
     [Google Scholar]
  14. 14. 
    Das J, Py F, Maughan T, O'Reilly T, Messié M et al. 2012. Coordinated sampling of dynamic oceanographic features with underwater vehicles and drifters. Int. J. Robot. Res. 31:626–46
     [Google Scholar]
  15. 15. 
    Fossum TO, Eidsvik J, Ellingsen I, Alver MO, Fragoso GM et al. 2018. Information-driven robotic sampling in the coastal ocean. J. Field Robot. 35:1101–21
     [Google Scholar]
  16. 16. 
    Paull L, Saeedi S, Seto M, Li H 2013. Sensor-driven online coverage planning for autonomous underwater vehicles. IEEE/ASME Trans. Mechatron. 18:1827–38
     [Google Scholar]
  17. 17. 
    Rudnick DL, Davis RE, Eriksen CC, Fratantoni DM, Perry MJ 2004. Underwater gliders for ocean research. Mar. Technol. Soc. J. 38:48–59
     [Google Scholar]
  18. 18. 
    Davis RE, Leonard NE, Fratantoni DM 2009. Routing strategies for underwater gliders. Deep-Sea Res. II 56:173–87
     [Google Scholar]
  19. 19. 
    Smith RN, Chao Y, Li PP, Caron DA, Jones BH, Sukhatme GS 2010. Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model. Int. J. Robot. Res. 29:1475–97
     [Google Scholar]
  20. 20. 
    Zhang Y, Bellingham JG, Chao Y 2010. Error analysis and sampling strategy design for using fixed or mobile platforms to estimate ocean flux. J. Atmos. Ocean. Technol. 27:481–506
     [Google Scholar]
  21. 21. 
    Smith RN, Schwager M, Smith SL, Jones BH, Rus D, Sukhatme GS 2011. Persistent ocean monitoring with underwater gliders: adapting sampling resolution. J. Field Robot. 28:714–41
     [Google Scholar]
  22. 22. 
    Mourre B, Alvarez A. 2012. Benefit assessment of glider adaptive sampling in the Ligurian sea. Deep-Sea Res. I 68:68–78
     [Google Scholar]
  23. 23. 
    Smedstad LF, Barron CN, Bourg RN, Brooking MW, Bryant DA et al. 2015. An expansion of glider observation strategies to systematically transmit and analyze preferred waypoints of underwater gliders. Ocean Sensing and Monitoring VII WW Hou, RA Arnone pap. 94590J. Proc. SPIE Vol 9459 Bellingham, WA: Soc. Photo-Opt. Instrum. Eng.
     [Google Scholar]
  24. 24. 
    Lermusiaux PFJ, Subramani DN, Lin J, Kulkarni CS, Gupta A et al. 2017. A future for intelligent autonomous ocean observing systems. J. Mar. Res. 75:765–813
     [Google Scholar]
  25. 25. 
    Baumgartner KAC, Ferrari S, Wettergren TA 2009. Robust deployment of dynamic sensor networks for cooperative track detection. IEEE Sens. J. 9:1029–48
     [Google Scholar]
  26. 26. 
    Howe BM, Chao Y, Arabshahi P, Roy S, McGinnis T, Gray A 2010. A smart sensor web for ocean observation: fixed and mobile platforms, integrated acoustics, satellites and predictive modeling. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 3:507–21
     [Google Scholar]
  27. 27. 
    Chang D, Wu W, Edwards CR, Zhang F 2017. Motion tomography: mapping flow fields using autonomous underwater vehicles. Int. J. Robot. Res. 36:320–36
     [Google Scholar]
  28. 28. 
    Wang C, Wei L, Wang Z, Song M, Mahmoudian N 2018. Reinforcement learning-based multi-AUV adaptive trajectory planning for under-ice field estimation. Sensors 18:3859
     [Google Scholar]
  29. 29. 
    Abbasi F, Mesbahi A, Velni JM 2019. A new Voronoi-based blanket coverage control method for moving sensor networks. IEEE Trans. Control Syst. Technol. 27:409–17
     [Google Scholar]
  30. 30. 
    Majumdar SJ. 2016. A review of targeted observations. Am. Meteorol. Soc. 97:2287–303
     [Google Scholar]
  31. 31. 
    Biswas MK, Krishnamurti TN. 2008. Adaptive use of research aircraft data sets for hurricane forecasts. Meteorol. Atmos. Phys. 99:43–64
     [Google Scholar]
  32. 32. 
    Choi H-L, How JP. 2011. Efficient targeting of sensor networks for large-scale systems. IEEE Trans. Control Syst. Technol. 19:1569–77
     [Google Scholar]
  33. 33. 
    Choi H-L, How JP. 2011. Coordinated targeting of mobile sensor networks for ensemble forecast improvement. IEEE Sens. J. 11:621–33
     [Google Scholar]
  34. 34. 
    Elston J, Frew E. 2010. Unmanned aircraft guidance for penetration of pre-tornadic storms. J. Guid. Control Dyn. 33:99–107
     [Google Scholar]
  35. 35. 
    Morss RE, Emanuel KA, Snyder C 2001. Idealized adaptive observation strategies for improving numerical weather prediction. J. Atmos. Sci. 58:210–32
     [Google Scholar]
  36. 36. 
    Cressie N, Kornak J. 2003. Spatial statistics in the presence of location error with an application to remote sensing of the environment. Stat. Sci. 18:436–56
     [Google Scholar]
  37. 37. 
    Kumar M, Cohen K, HomChaudhuri B 2011. Cooperative control of multiple uninhabited aerial vehicles for monitoring and fighting wildfires. J. Aerosp. Comput. Inf. Commun. 8:1–16
     [Google Scholar]
  38. 38. 
    Han J, Xu Y, Di L, Chen Y 2013. Low-cost multi-UAV technologies for contour mapping of nuclear radiation field. J. Intell. Robot. Syst. Theory Appl. 70:401–10
     [Google Scholar]
  39. 39. 
    Barrientos A, Colorado J, del Cerro J, Martinez A, Rossi C et al. 2011. Aerial remote sensing in agriculture: a practical approach to area coverage and path planning for fleets of mini aerial robots. J. Field Robot. 28:667–89
     [Google Scholar]
  40. 40. 
    Tokekar P, Hook JV, Mulla D, Isler V 2016. Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Trans. Robot. 32:1498–511
     [Google Scholar]
  41. 41. 
    Kingston D, Beard RW, Holt RS 2008. Decentralized perimeter surveillance using a team of UAVs. IEEE Trans. Robot. 24:1394–404
     [Google Scholar]
  42. 42. 
    Hsieh MA, Cowley A, Keller JF, Chaimowicz L, Grocholsky B et al. 2007. Adaptive teams of autonomous aerial and ground robots for situational awareness. J. Field Robot. 24:992–1013
     [Google Scholar]
  43. 43. 
    Nastasi KM, Black JT. 2019. Adaptively tracking maneuvering spacecraft with a globally distributed, diversely populated surveillance network. J. Guid. Control Dyn. 42:1033–48
     [Google Scholar]
  44. 44. 
    Holzinger MJ, Jah MK. 2018. Challenges and potential in space domain awareness. J. Guid. Control Dyn. 41:15–18
     [Google Scholar]
  45. 45. 
    Atanasov N, Ny JL, Daniilidis K, Pappas GJ 2015. Decentralized active information acquisition: theory and application to multi-robot SLAM. 2015 IEEE International Conference on Robotics and Automation4775–82 Piscataway, NJ: IEEE
     [Google Scholar]
  46. 46. 
    Mostofi Y. 2011. Compressive cooperative sensing and mapping in mobile networks. IEEE Trans. Mob. Comput. 10:1769–84
     [Google Scholar]
  47. 47. 
    Singh AK, Hahn J. 2005. Determining optimal sensor locations for state and parameter estimation for stable nonlinear systems. Ind. Eng. Chem. Res. 44:5645–59
     [Google Scholar]
  48. 48. 
    Chang D-M, Yu C-C, Chien I-L 2001. Arrangement of multi-sensor for spatio-temporal systems: application to sheet-forming processes. Chem. Eng. Sci. 56:5709–17
     [Google Scholar]
  49. 49. 
    Lui K, Zhang X, Shi J 2014. Adaptive sensor allocation strategy for process monitoring and diagnosis in a Bayesian network. IEEE Trans. Autom. Sci. Eng. 11:452–62
     [Google Scholar]
  50. 50. 
    Choi H-L, How JP. 2010. Continuous trajectory planning of mobile sensors for informative forecasting. Automatica 46:1266–75
     [Google Scholar]
  51. 51. 
    Ghaffarkhah A, Mostofi Y. 2012. Path planning for networked robotic surveillance. IEEE Trans. Signal Proc. 60:3560–75
     [Google Scholar]
  52. 52. 
    de Silva V, Ghrist R 2007. Coverage in sensor networks via persistent homology. Algebraic Geom. Topol. 7:339–58
     [Google Scholar]
  53. 53. 
    Amigoni F, Caglioti V. 2010. An information-based exploration strategy for environment mapping with mobile robots. Robot. Auton. Syst. 58:684–99
     [Google Scholar]
  54. 54. 
    Choi J, Oh S, Horowitz R 2009. Distributed learning and cooperative control for multi-agent systems. Automatica 25:2802–14
     [Google Scholar]
  55. 55. 
    Dou L, Song C, Wang X, Liu L, Feng G 2018. Coverage control for heterogeneous mobile sensor networks subject to measurement errors. IEEE Trans. Autom. Control 63:3479–86
     [Google Scholar]
  56. 56. 
    Kwok A, Martínez S. 2012. Coverage maximization with autonomous agents in fast flow environments. J. Optim. Theory Appl. 155:986–1007
     [Google Scholar]
  57. 57. 
    McDougall D, Moore RO. 2017. Optimal strategies for the control of autonomous vehicles in data assimilation. Physica D 351–52:42–52
     [Google Scholar]
  58. 58. 
    Paley DA, Peterson C. 2009. Stabilization of collective motion in a time-invariant flowfield. J. Guid. Control Dyn. 32:771–79
     [Google Scholar]
  59. 59. 
    Song C, Feng G, Fan Y, Wang Y 2011. Decentralized adaptive awareness coverage control for multi-agent networks. Automatica 47:2749–56
     [Google Scholar]
  60. 60. 
    Jadaliha M, Lee J, Choi J 2012. Adaptive control of multiagent systems for finding peaks of uncertain static fields. J. Dyn. Syst. Meas. Control 134:051007
     [Google Scholar]
  61. 61. 
    Briñón-Arranz L, Seuret A, Canudas de Wit C 2014. Cooperative control design of time-varying formations of multi-agent systems. IEEE Trans. Autom. Control 59:2283–88
     [Google Scholar]
  62. 62. 
    Elamvazhuthi K, Kuiper H, Berman S 2018. PDE-based optimization for stochastic mapping and coverage strategies using robotic ensembles. Automatica 95:356–67
     [Google Scholar]
  63. 63. 
    Stanković MS, Johansson KH, Stipanović DM 2012. Distributed seeking of Nash equilibria with applications to mobile sensor networks. IEEE Trans. Autom. Control 57:904–19
     [Google Scholar]
  64. 64. 
    Cortés J. 2012. Deployment of an unreliable robotic sensor network for spatial estimation. Syst. Control Lett. 61:41–49
     [Google Scholar]
  65. 65. 
    Olfati-Saber R, Jalalkamali P. 2012. Coupled distributed estimation and control for mobile sensor networks. IEEE Trans. Autom. Control 57:2609–14
     [Google Scholar]
  66. 66. 
    Mysorewala MF, Cheded L, Popa DO 2012. A distributed multi-robot adaptive sampling scheme for the estimation of the spatial distribution in widespread fields. EURASIP J. Wirel. Commun. Netw. 2012:223
     [Google Scholar]
  67. 67. 
    Gu D, Hu H. 2012. Spatial Gaussian process regression with mobile sensor networks. IEEE Trans. Neural Netw. Learn. Syst. 23:1279–90
     [Google Scholar]
  68. 68. 
    Kia SS, Van Scoy B, Cortés J, Freeman RA, Lynch KM, Martínez S 2019. Tutorial on dynamic average consensus: the problem, its applications, and the algorithms. IEEE Control Syst. Mag. 39:340–72
     [Google Scholar]
  69. 69. 
    Peterson C, Paley DA. 2013. Distributed estimation for motion coordination in an unknown spatially varying flowfield. J. Guid. Control Dyn. 36:894–98
     [Google Scholar]
  70. 70. 
    Denman KL, Gargett AE. 1983. Time and space scales of vertical mixing and advection of phytoplankton in the upper ocean. Limnol. Oceanogr. 28:801–15
     [Google Scholar]
  71. 71. 
    Karspeck AR, Kaplan A, Sain SR 2012. Bayesian modelling and ensemble reconstruction of mid-scale spatial variability in North Atlantic sea-surface temperatures for 1850–2008. Q. J. R. Meteorol. Soc. 138:234–48
     [Google Scholar]
  72. 72. 
    Garcia MR, Vilas C, Banga JR, Alonso AA 2007. Optimal field reconstruction of distributed process systems from partial measurements. Ind. Eng. Chem. Res 46:530–39
     [Google Scholar]
  73. 73. 
    Salam T, Hsieh MA. 2019. Adaptive sampling and reduced-order modeling of dynamic processes by robot teams. IEEE Robot. Autom. Mag. 4:477–84
     [Google Scholar]
  74. 74. 
    Michini M, Hsieh MA, Forgoston E, Schwartz IB 2014. Robotic tracking of coherent structures in flows. IEEE Trans. Robot. 30:593–603
     [Google Scholar]
  75. 75. 
    Uciński D. 2004. Optimal Measurement Methods for Distributed Parameter System Identification Boca Raton, FL: CRC
  76. 76. 
    Alonso AA, Kevrekidis IG, Banga JR, Frouzakis CE 2004. Optimal sensor location and reduced order observer design for distributed process systems. Comput. Chem. Eng. 28:27–35
     [Google Scholar]
  77. 77. 
    Demetriou MA. 2012. Adaptive control of 2-D PDEs using mobile collocated actuator/sensor pairs with augmented vehicle dynamics. IEEE Trans. Autom. Control 57:2979–93
     [Google Scholar]
  78. 78. 
    Xu Y, Choi J. 2012. Spatial prediction with mobile sensor networks using Gaussian processes with built-in Gaussian Markov random fields. Automatica 48:1735–40
     [Google Scholar]
  79. 79. 
    Xu Y, Choi J, Dass S, Maiti T 2013. Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields. Automatica 49:3520–30
     [Google Scholar]
  80. 80. 
    Choi J, Milutinović D. 2015. Tips on stochastic optimal feedback control and Bayesian spatiotemporal models: applications to robotics. J. Dyn. Syst. Meas. Control 137:030801
     [Google Scholar]
  81. 81. 
    Marino A, Antonelli G, Aguiar AP, Pascoal A, Chiaverini S 2015. A decentralized strategy for multirobot sampling/patrolling: theory and experiments. IEEE Trans. Control Syst. Technol. 23:313–22
     [Google Scholar]
  82. 82. 
    Monroy JG, Blanco J-L, Gonzalez-Jimenez J 2016. Time-variant gas distribution mapping with obstacle information. Auton. Robots 40:1–16
     [Google Scholar]
  83. 83. 
    Xu Y, Choi J, Oh S 2011. Mobile sensor network navigation using Gaussian processes with truncated observations. IEEE Trans. Robot. 27:1118–31
     [Google Scholar]
  84. 84. 
    Nguyen LV, Kodagoda S, Ranasinghe R, Dissanayake G 2017. Adaptive placement for mobile sensors in spatial prediction under locational errors. IEEE Sens. J. 27:794–802
     [Google Scholar]
  85. 85. 
    Choi S, Jadaliha M, Choi J, Oh S 2015. Distributed Gaussian process regression under localization uncertainty. J. Dyn. Syst. Meas. Control 137:031007
     [Google Scholar]
  86. 86. 
    Graham R, Cortés J. 2012. Cooperative adaptive sampling of random fields with partially known covariance. Int. J. Robust Nonlinear Control 22:504–34
     [Google Scholar]
  87. 87. 
    Xu Y, Choi J. 2011. Adaptive sampling for learning Gaussian processes using mobile sensor networks. Sensors 11:3051–66
     [Google Scholar]
  88. 88. 
    Xu Y, Choi J. 2012. Stochastic adaptive sampling for mobile sensor networks using kernel regression. Int. J. Control Autom. Syst. 10:778–86
     [Google Scholar]
  89. 89. 
    Hoffmann GM, Tomlin CJ. 2010. Mobile sensor network control using mutual information methods and particle filters. IEEE Trans. Autom. Control 55:32–47
     [Google Scholar]
  90. 90. 
    Wettergren TA, Traweek CM. 2012. The search benefits of autonomous mobility in distributed sensor networks. Int. J. Distrib. Sensor Netw. 8:797040
     [Google Scholar]
  91. 91. 
    Baumgartner K, Ferrari S, Rao A 2009. Optimal control of a mobile sensor network for cooperative target detection. IEEE. J. Ocean. Eng. 34:678–97
     [Google Scholar]
  92. 92. 
    Adurthi N, Singla P, Singh T 2013. Optimal information collection for nonlinear systems—an application to multiple target tracking and localization. 2013 American Control Conference3870–75 Piscataway, NJ: IEEE
     [Google Scholar]
  93. 93. 
    Ferrari S, Fierro R, Perteet B, Cai C, Baumgartner K 2009. A geometric optimization approach to detecting and intercepting dynamic targets using a mobile sensor network. SIAM J. Control Optim. 48:292–320
     [Google Scholar]
  94. 94. 
    Oh S, Schenato L, Chen P, Sastry S 2007. Tracking and coordination of multiple agents using sensor networks: system design, algorithms and experiments. Proc. IEEE 95:234–54
     [Google Scholar]
  95. 95. 
    Schlotfeldt B, Thakur D, Atanasov N, Kumar V, Pappas GJ 2018. Anytime planning for decentralized multirobot active information gathering. IEEE Robot. Autom. Lett. 3:1025–32
     [Google Scholar]
  96. 96. 
    Mavrommati A, Tzorakoleftherakis E, Abraham I, Murphey TD 2018. Real-time area coverage and target localization using receding-horizon ergodic exploration. IEEE Trans. Robot. 34:62–80
     [Google Scholar]
  97. 97. 
    Ögren P, Fiorelli E, Leonard NE 2004. Cooperative control of mobile sensor networks: adaptive gradient climbing in a distributed environment. IEEE Trans. Autom. Control 49:1292–302
     [Google Scholar]
  98. 98. 
    Wu W, Zhang F. 2012. Robust cooperative exploration with a switching strategy. IEEE Trans. Robot. 28:828–39
     [Google Scholar]
  99. 99. 
    Zhang F, Leonard NE. 2010. Cooperative filters and control for cooperative exploration. IEEE Trans. Autom. Control 55:650–63
     [Google Scholar]
  100. 100. 
    Ramírez-Llanos E, Martínez S. 2019. Stochastic source seeking for mobile robots in obstacle environments via the SPSA method. IEEE Trans. Autom. Control 64:1732–39
     [Google Scholar]
  101. 101. 
    Uciński D, Patan M. 2010. Sensor network design for the estimation of spatially distributed processes. Int. J. Appl. Math. Comput. Sci. 20:459–81
     [Google Scholar]
  102. 102. 
    Baronov D, Baillieul J. 2012. Decision making for rapid information acquisition in the reconnaissance of random fields. Proc. IEEE 100:776–801
     [Google Scholar]
  103. 103. 
    La HM, Sheng W, Chen J 2015. Cooperative and active sensing in mobile sensor networks for scalar field mapping. IEEE Trans. Syst. Man Cybernet. Syst. 45:1–12
     [Google Scholar]
  104. 104. 
    Galceran E, Carreras M. 2013. A survey on coverage path planning for robotics. Robot. Auton. Syst. 61:1258–76
     [Google Scholar]
  105. 105. 
    Bullo F, Cortés J, Martínez S 2009. Distributed Control of Robotic Networks Princeton, NJ: Princeton Univ. Press
  106. 106. 
    Stergiopoulos Y, Tzes A. 2013. Spatially distributed area coverage optimisation in mobile robotic networks with arbitrary convex anisotropic patterns. Automatica 49:232–37
     [Google Scholar]
  107. 107. 
    Mathew G, Mezić I. 2011. Metrics for ergodicity and design of ergodic dynamics for multi-agent systems. Physica D 240:432–42
     [Google Scholar]
  108. 108. 
    Zhang B, Adurthi N, Rai R, Singla P 2016. A novel sampling technique for probabilistic static coverage problems. J. Mech. Des. 138:031403
     [Google Scholar]
  109. 109. 
    Cortés J, Martínez S, Karatas T, Bullo F 2004. Coverage control for mobile sensing networks. IEEE Trans. Robot. Autom. 20:243–55
     [Google Scholar]
  110. 110. 
    Zhong M, Cassandras CG. 2011. Distributed coverage control and data collection with mobile sensor networks. IEEE Trans. Autom. Control 56:2445–55
     [Google Scholar]
  111. 111. 
    Ny JL, Pappas GJ. 2013. Adaptive deployment of mobile robotic networks. IEEE Trans. Autom. Control 58:654–66
     [Google Scholar]
  112. 112. 
    Graham R, Cortés J. 2012. Adaptive information collection by robotic sensor networks for spatial estimation. IEEE Trans. Autom. Control 57:1404–19
     [Google Scholar]
  113. 113. 
    Nowzari C, Cortés J. 2012. Self-triggered coordination of robotic networks for optimal deployment. Automatica 48:1077–87
     [Google Scholar]
  114. 114. 
    Song C, Liu L, Feng G, Wang Y, Gao Q 2013. Persistent awareness coverage control for mobile sensor networks. Automatica 49:1867–73
     [Google Scholar]
  115. 115. 
    Kantaros Y, Thanou M, Tzes A 2015. Distributed coverage control for concave areas by a heterogeneous robot-swarm with visibility sensing constraints. Automatica 53:195–207
     [Google Scholar]
  116. 116. 
    Stergiopoulos Y, Thanou M, Tzes A 2015. Distributed collaborative coverage-control schemes for non-convex domains. IEEE Trans. Autom. Control 60:2422–27
     [Google Scholar]
  117. 117. 
    Hussein II, Stipanović DM. 2007. Effective coverage control for mobile sensor networks with guaranteed collision avoidance. IEEE Trans. Control Syst. Technol. 15:642–57
     [Google Scholar]
  118. 118. 
    Susca S, Martínez S, Bullo F 2008. Monitoring environmental boundaries with a robotic sensor network. IEEE Trans. Control Syst. Technol. 16:288–96
     [Google Scholar]
  119. 119. 
    Tahbaz-Salehi A, Jadbabaie A. 2010. Distributed coverage verification in sensor networks without location information. IEEE Trans. Autom. Control 55:1837–49
     [Google Scholar]
  120. 120. 
    Gusrialdi A, Yu C. 2014. Exploiting the use of information to improve coverage performance of robotic sensor networks. IET Control Theory Appl 8:1270–83
     [Google Scholar]
  121. 121. 
    Cortés J. 2010. Coverage optimization and spatial load balancing by robotic sensor networks. IEEE Trans. Autom. Control 55:749–54
     [Google Scholar]
  122. 122. 
    Lekien F, Leonard NE. 2009. Nonuniform coverage and cartograms. SIAM J. Control Optim. 48:351–72
     [Google Scholar]
  123. 123. 
    Palacios-Gasós JM, Montijano E, Sagüés C, Llorente S 2016. Distributed coverage estimation and control for multirobot persistent tasks. IEEE Trans. Robot. 32:1444–60
     [Google Scholar]
  124. 124. 
    Sharifi F, Chamseddine A, Mahboubi H, Zhang Y, Aghdam AG 2015. A distributed deployment strategy for a network of cooperative autonomous vehicles. IEEE Trans. Control Syst. Technol. 23:737–45
     [Google Scholar]
  125. 125. 
    Leonard NE, Olshevsky A. 2013. Nonuniform coverage control on the line. IEEE Trans. Autom. Control 58:2743–55
     [Google Scholar]
  126. 126. 
    Sydney N, Paley DA. 2014. Multivehicle coverage control for nonstationary spatiotemporal fields. Automatica 50:1381–90
     [Google Scholar]
  127. 127. 
    Manyika J, Durrant-Whyte H. 1994. Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach Upper Saddle River, NJ: Prentice Hall
  128. 128. 
    Grocholsky B, Keller J, Kumar V, Pappas G 2006. Cooperative air and ground surveillance: a scalable approach to the detection and localization of targets by a network of UAVs and UGVs. IEEE Robot. Autom. Mag. 13:16–26
     [Google Scholar]
  129. 129. 
    Vergassola M, Villermaux E, Shraiman BI 2007. 'Infotaxis' as a strategy for searching without gradients. Nature 445:406–9
     [Google Scholar]
  130. 130. 
    Martínez S, Bullo F. 2006. Optimal sensor placement and motion coordination for target tracking. Automatica 42:661–68
     [Google Scholar]
  131. 131. 
    Alvarez A, Mourre B. 2011. Optimum sampling designs for a glider–mooring observing network. J. Atmos. Ocean. Technol. 29:601–12
     [Google Scholar]
  132. 132. 
    Yilmaz NK, Evangelinos C, Lermusiaux PFJ, Patrikalakis NM 2008. Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming. IEEE J. Ocean. Eng. 33:522–37
     [Google Scholar]
  133. 133. 
    Xu Y, Choi J, Dass S, Maiti T 2012. Sequential Bayesian prediction and adaptive sampling algorithms for mobile sensor networks. IEEE Trans. Autom. Control 57:2078–84
     [Google Scholar]
  134. 134. 
    Julian BJ, Karaman S, Rus D 2014. On mutual information-based control of range sensing robots for mapping applications. Int. J. Robot. Res. 33:1375–92
     [Google Scholar]
  135. 135. 
    Ma K-C, Liu L, Heidarsson HK, Sukhatme GS 2017. Data-driven learning and planning for environmental sampling. J. Field Robot. 35:643–61
     [Google Scholar]
  136. 136. 
    Choi H-L, Lee S-J. 2015. A potential-game approach for information-maximizing cooperative planning of sensor networks. IEEE Trans. Control Syst. Technol. 23:2326–35
     [Google Scholar]
  137. 137. 
    Schwager M, Dames P, Rus D, Kumar V 2017. A multi-robot control policy for information gathering in the presence of unknown hazards. Robotics Research H Christensen, O Khatib 455–72 Cham, Switz: Springer
     [Google Scholar]
  138. 138. 
    Atanasov NA, Ny JL, Pappas GJ 2015. Distributed algorithms for stochastic source seeking with mobile robot networks. J. Dyn. Syst. Meas. Control 137:031004
     [Google Scholar]
  139. 139. 
    Carlone L, Du J, Ng MK, Bona B, Indri M 2014. Active SLAM and exploration with particle filters using Kullback-Leibler divergence. J. Intell. Robot. Syst. 75:291–311
     [Google Scholar]
  140. 140. 
    Lagor FD, Ide K, Paley DA 2016. Incorporating prior knowledge in observability-based path planning for ocean sampling. Syst. Control Lett. 97:169–75
     [Google Scholar]
  141. 141. 
    Julian BJ, Angermann M, Schwager M, Rus D 2012. Distributed robotic sensor networks: an information-theoretic approach. Int. J. Robot. Res. 31:1134–54
     [Google Scholar]
  142. 142. 
    Pavone M, Arsie A, Frazzoli E, Bullo F 2011. Distributed algorithms for environment partitioning in mobile robotic networks. IEEE Trans. Autom. Control 56:1834–48
     [Google Scholar]
  143. 143. 
    Caicedo-Núñez CH, efran M. 2011. Distributed task assignment in mobile sensor networks. IEEE Trans. Autom. Control 56:2485–89
     [Google Scholar]
  144. 144. 
    Cui R, Li Y, Yan W 2016. Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT*. IEEE Trans. Syst. Man Cybernet. Syst. 46:993–1004
     [Google Scholar]
  145. 145. 
    Levine D, Luders B, How JP 2013. Information-theoretic motion planning for constrained sensor networks. J. Aerosp. Inf. Syst. 10:476–96
     [Google Scholar]
  146. 146. 
    Smith SL, Bullo F. 2009. Monotonic target assignment for robotic networks. IEEE Trans. Autom. Control 54:2042–57
     [Google Scholar]
  147. 147. 
    Singh A, Krause A, Guestrin C, Kaiser WJ 2009. Efficient informative sensing using multiple robots. J. Artif. Intell. Res. 34:707–55
     [Google Scholar]
  148. 148. 
    Miller LM, Silverman Y, MacIver MA, Murphey TD 2016. Ergodic exploration of distributed information. IEEE Trans. Robot. 32:36–52
     [Google Scholar]
  149. 149. 
    Lermusiaux PFJ, Lolla T, Haley PJ Jr, Yigit K, Ueckermann MP et al. 2016. Science of autonomy: time-optimal path planning and adaptive sampling for swarms of ocean vehicles. Springer Handbook of Ocean Engineering MR Dhanak, NI Xiros481–98 Cham, Switz: Springer
     [Google Scholar]
  150. 150. 
    Bullo F, Frazzoli E, Pavone M, Savla K, Smith SL 2011. Dynamic vehicle routing for robotic systems. Proc. IEEE 99:1482–504
     [Google Scholar]
  151. 151. 
    Dickey TD. 1991. The emergence of concurrent high-resolution physical and bio-optical measurements in the upper ocean and their applications. Rev. Geophys. 29:383–413
     [Google Scholar]
/content/journals/10.1146/annurev-control-073119-090634
Loading
Mobile Sensor Networks and Control: Adaptive Sampling of Spatiotemporal Processes
Annual Review of Control, Robotics, and Autonomous Systems 3, 91 (2020); https://doi.org/10.1146/annurev-control-073119-090634
/content/journals/10.1146/annurev-control-073119-090634
/content/journals/10.1146/annurev-control-073119-090634
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error