1932

Abstract

Modern robots are increasingly capable of performing “basic” activities such as localization, navigation, and motion planning. However, for a robot to be considered intelligent, we would like it to be able to automatically combine these capabilities in order to achieve a high-level goal. The field of automated planning (sometimes called AI planning) deals with automatically synthesizing plans that combine basic actions to achieve a high-level goal. In this article, we focus on the intersection of automated planning and robotics and discuss some of the challenges and tools available to employ automated planning in controlling robots. We review different types of planning formalisms and discuss their advantages and limitations, especially in the context of planning robot actions. We conclude with a brief guide aimed at helping roboticists choose the right planning model to endow a robot with planning capabilities.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-control-082619-100135
2020-05-03
2024-06-15
Loading full text...

Full text loading...

/deliver/fulltext/control/3/1/annurev-control-082619-100135.html?itemId=/content/journals/10.1146/annurev-control-082619-100135&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Ghallab M, Nau D, Traverso P 2016. Automated Planning and Acting New York: Cambridge Univ. Press
    [Google Scholar]
  2. 2. 
    Ingrand F, Ghallab M. 2017. Deliberation for autonomous robots: a survey. Artif. Intell. 247:10–44
    [Google Scholar]
  3. 3. 
    LaValle SM. 2006. Planning Algorithms Cambridge, UK: Cambridge Univ. Press http://planning.cs.uiuc.edu
    [Google Scholar]
  4. 4. 
    Stern R, Sturtevant N, Felner A, Koenig S, Ma H et al. 2019. Multi-agent pathfinding: definitions, variants, and benchmarks. arXiv:1906.08291 [cs.AI]
  5. 5. 
    Giftthaler M, Farshidian F, Sandy T, Stadelmann L, Buchli J 2017. Efficient kinematic planning for mobile manipulators with nonholonomic constraints using optimal control. 2017 IEEE International Conference on Robotics and Automation3411–17 Piscataway, NJ: IEEE
    [Google Scholar]
  6. 6. 
    Yang G, Chien S. 2017. Review on space robotics: toward top-level science through space exploration. Sci. Robot. 2:eaan5074
    [Google Scholar]
  7. 7. 
    Crosby M, Petrick RPA, Rovida F, Krüger V 2017. Integrating mission and task planning in an industrial robotics framework. Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling471–79 Palo Alto, CA: AAAI Press
    [Google Scholar]
  8. 8. 
    Long D. 2019. Planning a way into a deep hole Presentation at the Workshop on Scheduling and Planning Applications (SPARK), 29th International Conference on Automated Planning and Scheduling Berkeley, CA: July 10–15
    [Google Scholar]
  9. 9. 
    Niemueller T, Ewert D, Reuter S, Ferrein A, Jeschke S, Lakemeyer G 2013. RoboCup Logistics League sponsored by Festo: a competitive factory automation testbed. RoboCup 2013: Robot World Cup XVII S Behnke, M Veloso, A Visser, R Xiong 336–47 Berlin: Springer
    [Google Scholar]
  10. 10. 
    Geffner H. 2010. The model-based approach to autonomous behavior: a personal view. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence1709–12 Palo Alto, CA: AAAI Press
    [Google Scholar]
  11. 11. 
    Albee A, Battel S, Brace R, Burdick G, Casani J et al. 2000. Report on the loss of the Mars Polar Lander and Deep Space 2 missions Tech. Rep. 20000061966, Jet Propuls. Lab., Calif. Inst. Technol Pasadena, CA:
    [Google Scholar]
  12. 12. 
    Bylander T. 1994. The computational complexity of STRIPS planning. Artif. Intell. 69:165–204
    [Google Scholar]
  13. 13. 
    Fikes RE, Nilsson NJ. 1971. STRIPS: a new approach to the application of theorem proving to problem solving. Proceedings of the 2nd International Joint Conference on Artificial Intelligence608–20 San Francisco, CA: Morgan Kaufmann
    [Google Scholar]
  14. 14. 
    Bäckström C, Klein I. 1991. Planning in polynomial time: the SAS-PUBS class. Comput. Intell. 7:181–97
    [Google Scholar]
  15. 15. 
    McDermott D, Ghallab M, Howe A, Knoblock C, Ram A et al. 1998. PDDL – the Planning Domain Definition Language Tech. Rep. CVC TR-98-003/DCS TR-1165, Yale Cent. Comput. Vision Control New Haven, CT:
    [Google Scholar]
  16. 16. 
    Haslum P, Lipovetzky N, Magazzeni D, Muise C 2019. An introduction to the Planning Domain Definition Language. Synth. Lect. Artif. Intell. Mach. Learn. 13:21–187
    [Google Scholar]
  17. 17. 
    Helmert M. 2009. Concise finite-domain representations for PDDL planning tasks. Artif. Intell. 173:503–35
    [Google Scholar]
  18. 18. 
    Konidaris G, Kaelbling LP, Lozano-Pérez T 2018. From skills to symbols: learning symbolic representations for abstract high-level planning. J. Artif. Intell. Res. 61:215–89
    [Google Scholar]
  19. 19. 
    Niemueller T, Hofmann T, Lakemeyer G 2018. CLIPS-based execution for PDDL planners. Proceedings of the 28th International Conference on Automated Planning and Scheduling509–17 Palo Alto, CA: AAAI Press
    [Google Scholar]
  20. 20. 
    Yoon S, Fern A, Givan R 2007. FF-Replan: a baseline for probabilistic planning. Proceedings of the Seventeenth International Conference on Automated Planning and Scheduling352–59 Palo Alto, CA: AAAI Press
    [Google Scholar]
  21. 21. 
    Little I, Thiebaux S. 2007. Probabilistic planning versus replanning Paper presented in the Workshop on IPC: Past, Present and Future, 17th International Conference on Automated Planning and Scheduling Providence, RI: Sept 22–26
    [Google Scholar]
  22. 22. 
    Kolobov A. 2012. Planning with Markov decision processes: an AI perspective. Synth. Lect. Artif. Intell. Mach. Learn. 6:11–210
    [Google Scholar]
  23. 23. 
    Nilsson NJ. 1984. Shakey the robot Tech. Rep. 323, AI Cent., SRI Int Palo Alto, CA:
    [Google Scholar]
  24. 24. 
    Speck D, Dornhege C, Burgard W 2017. Shakey 2016 – how much does it take to redo Shakey the robot. IEEE Robot. Autom. Lett. 2:1203–9
    [Google Scholar]
  25. 25. 
    Fox M, Long D. 2003. PDDL2.1: an extension to PDDL for expressing temporal planning domains. J. Artif. Intell. Res. 20:61–124
    [Google Scholar]
  26. 26. 
    Ghallab M, Laruelle H. 1994. Representation and control in IxTeT, a temporal planner. Proceedings of the Second International Conference on Artificial Intelligence Planning Systems61–67 Palo Alto, CA: AAAI Press
    [Google Scholar]
  27. 27. 
    Muscettola N. 1994. HSTS: integrating planning and scheduling. Intelligent Scheduling M Zweben, MS Fox 169–212 San Francisco, CA: Morgan Kaufmann
    [Google Scholar]
  28. 28. 
    Gigante N, Montanari A, Mayer MC, Orlandini A 2017. Complexity of timeline-based planning. Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling116–24 Palo Alto, CA: AAAI Press
    [Google Scholar]
  29. 29. 
    Chien S, Rabideau G, Knight R, Sherwood R, Engelhardt B et al. 2000. ASPEN – automating space mission operations using automated planning and scheduling Paper presented at the International Conference on Space Operations Tolouse: Fr., June 19–23
    [Google Scholar]
  30. 30. 
    Barreiro J, Boyce M, Do M, Frank J, Iatauro M et al. 2012. EUROPA: a platform for AI planning, scheduling, constraint programming, and optimization Paper presented at the 22nd International Conference on Automated Planning and Scheduling Atibaia, Brazil: June 25–29
    [Google Scholar]
  31. 31. 
    Smith DE, Frank J, Cushing W 2008. The ANML language Paper presented at the 18th International Conference on Automated Planning and Scheduling Sydney: Sept 14–18
    [Google Scholar]
  32. 32. 
    Cashmore M, Cimatti A, Magazzeni D, Micheli A, Zehtabi P 2019. Robustness envelopes for temporal plans. Proceedings of the 2019 AAAI Conference on Artificial Intelligence7538–45 Palo Alto, CA: AAAI Press
    [Google Scholar]
  33. 33. 
    Verma V, Estlin T, Jónsson A, Pasareanu C, Simmons R, Tso K 2005. Plan Execution Interchange Language (PLEXIL) for executable plans and command sequences. Proceedings of i-SAIRAS 2005: The 8th International Symposium on Artificial Intelligence, Robotics and Automation in Space Paris: Eur. Space Agency. Available at https://www.esa.int/Our_Activities/Space_Engineering_Technology/Automation_and_Robotics/i-SAIRAS/(print)
    [Google Scholar]
  34. 34. 
    Rusu RB, Jones EG, Marder-Eppstein E, Pantofaru C, Wise M et al. 2011. Towards autonomous robotic butlers: lessons learned with the PR2. 2011 IEEE International Conference on Robotics and Automation5568–75 Piscataway, NJ: IEEE
    [Google Scholar]
  35. 35. 
    Williams B, Ingham M, Chung S, Elliott P 2003. Model-based programming of intelligent embedded systems and robotic space explorers. Proc. IEEE 91:212–37
    [Google Scholar]
  36. 36. 
    Dechter R, Meiri I, Pearl J 1991. Temporal constraint networks. Artif. Intell. 49:61–95
    [Google Scholar]
  37. 37. 
    Vidal T, Ghallab M. 1996. Dealing with uncertain durations in temporal constraint networks dedicated to planning. ECAI 96: 12th European Conference on Artificial Intelligence W Wahlster 48–54 Chichester, UK: Wiley & Sons
    [Google Scholar]
  38. 38. 
    Vidal T, Fargier H. 1997. Contingent durations in temporal CSPs: from consistency to controllabilities. Proceedings of TIME '97: 4th International Workshop on Temporal Representation and Reasoning78–85 Piscataway, NJ: IEEE
    [Google Scholar]
  39. 39. 
    Frank J, Jónsson A. 2003. Constraint-based attribute and interval planning. Constraints 8:339–64
    [Google Scholar]
  40. 40. 
    Fratini S, Cesta A, De Benedictis R, Orlandini A, Rasconi R 2011. APSI-based deliberation in goal oriented autonomous controllers. 11th Symposium on Advanced Space Technologies in Robotics and Automation Paris: Eur. Space Agency Available at https://www.esa.int/Our_Activities/Space_Engineering_Technology/Automation_and_Robotics/Proceedings_of_ASTRA
    [Google Scholar]
  41. 41. 
    Vidal T, Fargier H. 1999. Handling contingency in temporal constraint networks: from consistency to controllabilities. J. Exp. Theor. Artif. Intell. 11:23–45
    [Google Scholar]
  42. 42. 
    Micheli A. 2016. Planning and scheduling in temporally uncertain domains PhD Thesis, Univ. Trento, Trento Italy:
    [Google Scholar]
  43. 43. 
    Morris P, Muscettola N. 2000. Execution of temporal plans with uncertainty. Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence491–96 Palo Alto, CA: AAAI Press
    [Google Scholar]
  44. 44. 
    Barbulescu L, Rubinstein ZB, Smith SF, Zimmerman TL 2010. Distributed coordination of mobile agent teams: the advantage of planning ahead.. Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems Vol. 11331–38 Richland, SC: Int. Found. Auton. Agents Multiagent Syst.
    [Google Scholar]
  45. 45. 
    Coles AJ, Coles A, Fox M, Long D 2012. COLIN: planning with continuous linear numeric change. J. Artif. Intell. Res. 44:1–96
    [Google Scholar]
  46. 46. 
    Della Penna G, Magazzeni D, Mercorio F 2012. A universal planning system for hybrid domains. Appl. Intell. 36:932–59
    [Google Scholar]
  47. 47. 
    Piotrowski WM, Fox M, Long D, Magazzeni D, Mercorio F 2016. Heuristic planning for PDDL+ domains. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence3213–19 Palo Alto, CA: AAAI Press
    [Google Scholar]
  48. 48. 
    Scala E, Haslum P, Thiébaux S 2016. Heuristics for numeric planning via subgoaling. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence3228–34 Palo Alto, CA: AAAI Press
    [Google Scholar]
  49. 49. 
    Cashmore M, Fox M, Long D, Magazzeni D 2016. A compilation of the full PDDL+ language into SMT. Proceedings of the Twenty-Sixth International Conference on Automated Planning and Scheduling79–87 Palo Alto, CA: AAAI Press
    [Google Scholar]
  50. 50. 
    Bryce D, Gao S, Musliner DJ, Goldman RP 2015. SMT-based nonlinear PDDL+ planning. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence3247–53 Palo Alto, CA: AAAI Press
    [Google Scholar]
  51. 51. 
    Fernández-González E, Williams BC, Karpas E 2018. ScottyActivity: mixed discrete-continuous planning with convex optimization. J. Artif. Intell. Res. 62:579–664
    [Google Scholar]
  52. 52. 
    Savas E, Fox M, Long D, Magazzeni D 2016. Planning using actions with control parameters. ECAI 2016: 22nd European Conference on Artificial Intelligence GA Kaminka, M Fox, P Boucquet, E Hüllermeier, V Dignum et al.1185–93 Amsterdam: IOS
    [Google Scholar]
  53. 53. 
    Sucan IA, Moll M, Kavraki LE 2012. The Open Motion Planning Library. IEEE Robot. Autom. Mag. 19:472–82
    [Google Scholar]
  54. 54. 
    Dantam NT, Chaudhuri S, Kavraki LE 2018. The Task-Motion Kit: an open source, general-purpose task and motion-planning framework. IEEE Robot. Autom. Mag. 25:361–70
    [Google Scholar]
  55. 55. 
    Dantam NT, Kingston ZK, Chaudhuri S, Kavraki LE 2016. Incremental task and motion planning: a constraint-based approach. Robotics: Science and Systems XII D Hsu, N Amato, S Berman, S Jacobs, pap. 2. N.p Robot. Sci. Syst. Found.
    [Google Scholar]
  56. 56. 
    McMahon J, Plaku E. 2017. Robot motion planning with task specifications via regular languages. Robotica 35:26–49
    [Google Scholar]
  57. 57. 
    Cambon S, Alami R, Gravot F 2009. A hybrid approach to intricate motion, manipulation and task planning. Int. J. Robot. Res. 28:104–26
    [Google Scholar]
  58. 58. 
    Waldhart J, Gharbi M, Alami R 2016. A novel software combining task and motion planning for human-robot interaction. 2016 AAAI Fall Symposium Series: Artificial Intelligence for Human-Robot Interaction100–2 Palo Alto, CA: AAAI Press
    [Google Scholar]
  59. 59. 
    Srivastava S, Fang E, Riano L, Chitnis R, Russell S, Abbeel P 2014. Combined task and motion planning through an extensible planner-independent interface layer. 2014 IEEE International Conference on Robotics and Automation639–46 Piscataway, NJ: IEEE
    [Google Scholar]
  60. 60. 
    Toussaint M. 2015. Logic-geometric programming: an optimization-based approach to combined task and motion planning. Proceedings of the 24th International Conference on Artificial Intelligence1930–36 Palo Alto, CA: AAAI Press
    [Google Scholar]
  61. 61. 
    Garrett CR, Lozano-Pérez T, Kaelbling LP 2018. FFRob: leveraging symbolic planning for efficient task and motion planning. Int. J. Robot. Res. 37:104–36
    [Google Scholar]
  62. 62. 
    Lagriffoul F, Dimitrov D, Bidot J, Saffiotti A, Karlsson L 2014. Efficiently combining task and motion planning using geometric constraints. Int. J. Robot. Res. 33:1726–47
    [Google Scholar]
  63. 63. 
    Edelkamp S, Lahijanian M, Magazzeni D, Plaku E 2018. Integrating temporal reasoning and sampling-based motion planning for multigoal problems with dynamics and time windows. IEEE Robot. Autom. Lett. 3:3473–80
    [Google Scholar]
  64. 64. 
    Lagriffoul F, Dantam NT, Garrett C, Akbari A, Srivastava S, Kavraki LE 2018. Platform-independent benchmarks for task and motion planning. IEEE Robot. Autom. Lett. 3:3765–72
    [Google Scholar]
  65. 65. 
    Rintanen J. 2007. Complexity of concurrent temporal planning. Proceedings of the Seventeenth International Conference on International Conference on Automated Planning and Scheduling280–87 Palo Alto, CA: AAAI Press
    [Google Scholar]
  66. 66. 
    Helmert M. 2002. Decidability and undecidability results for planning with numerical state variables. Proceedings of the Sixth International Conference on Artificial Intelligence Planning Systems44–53 Palo Alto, CA: AAAI Press
    [Google Scholar]
  67. 67. 
    Celorrio SJ, Jonsson A, Palacios H 2015. Temporal planning with required concurrency using classical planning. Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling129–37 Palo Alto, CA: AAAI Press
    [Google Scholar]
  68. 68. 
    Howard RA. 1960. Dynamic Programming and Markov Processes Cambridge, MA: MIT Press
    [Google Scholar]
  69. 69. 
    Åström KJ. 1965. Optimal control of Markov processes with incomplete state information. J. Math. Anal. Appl. 10:174–205
    [Google Scholar]
  70. 70. 
    Cimatti A, Do M, Micheli A, Roveri M, Smith DE 2018. Strong temporal planning with uncontrollable durations. Artif. Intell. 256:1–34
    [Google Scholar]
  71. 71. 
    Domshlak C. 2013. Fault tolerant planning: complexity and compilation. Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling129–37 Palo Alto, CA: AAAI Press
    [Google Scholar]
  72. 72. 
    Younes HLS, Littman ML. 2004. PPDDL 1.0: an extension to PDDL for expressing planning domains with probabilistic effects Tech. Rep. CMU-CS-04-167, Carnegie Mellon Univ Pittsburgh, PA:
    [Google Scholar]
  73. 73. 
    Sanner S. 2010. Relational Dynamic Influence Diagram Language (RDDL): language description Unpubl. Manuscr., Aust. Natl. Univ Canberra: http://users.cecs.anu.edu.au/∼ssanner/IPPC_2011/RDDL.pdf
    [Google Scholar]
  74. 74. 
    Burns E, Benton J, Ruml W, Yoon SW, Do MB 2012. Anticipatory on-line planning. Proceedings of the Twenty-Second International Conference on Automated Planning and Scheduling333–37 Palo Alto, CA: AAAI Press
    [Google Scholar]
  75. 75. 
    Talamadupula K, Benton J, Kambhampati S, Schermerhorn P, Scheutz M 2010. Planning for human-robot teaming in open worlds. ACM Trans. Intell. Syst. Technol. 1:14
    [Google Scholar]
  76. 76. 
    Bonet B, Geffner H. 2014. Belief tracking for planning with sensing: width, complexity and approximations. J. Artif. Intell. Res. 50:923–70
    [Google Scholar]
  77. 77. 
    Fagin R, Halpern JY, Moses Y, Vardi MY 2003. Reasoning About Knowledge Cambridge, MA: MIT Press
    [Google Scholar]
  78. 78. 
    Palacios H, Geffner H. 2009. Compiling uncertainty away in conformant planning problems with bounded width. J. Artif. Intell. Res. 35:623–75
    [Google Scholar]
  79. 79. 
    Brafman RI, Shani G. 2012. A multi-path compilation approach to contingent planning. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence1868–74 Palo Alto, CA: AAAI Press
    [Google Scholar]
  80. 80. 
    Bonet B, Formica G, Ponte M 2017. Completeness of online planners for partially observable deterministic tasks. Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling38–46 Palo Alto, CA: AAAI Press
    [Google Scholar]
  81. 81. 
    Bernstein DS, Zilberstein S, Immerman N 2000. The complexity of decentralized control of Markov decision processes. Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence32–37 San Francisco, CA: Morgan Kaufmann
    [Google Scholar]
  82. 82. 
    Amato C, Konidaris G, Cruz G, Maynor CA, How JP, Kaelbling LP 2015. Planning for decentralized control of multiple robots under uncertainty. 2015 IEEE International Conference on Robotics and Automation1241–48 Piscataway, NJ: IEEE
    [Google Scholar]
  83. 83. 
    Engesser T, Bolander T, Mattmüller R, Nebel B 2017. Cooperative epistemic multi-agent planning for implicit coordination. Proceedings of the Ninth Workshop on Methods for Modalities S Ghosh, R Ramanujam 75–90 Electr. Proc. Theor. Comput. Sci 243 Waterloo, Aust: Open Publ. Assoc.
    [Google Scholar]
  84. 84. 
    Kitano H, Asada M, Kuniyoshi Y, Noda I, Osawa E, Matsubara H 1997. RoboCup: a challenge problem for AI. AI Mag 18:173–85
    [Google Scholar]
  85. 85. 
    Hansen EA, Bernstein DS, Zilberstein S 2004. Dynamic programming for partially observable stochastic games. Proceedings of the 19th National Conference on Artificial Intelligence709–15 Palo Alto, CA: AAAI Press
    [Google Scholar]
  86. 86. 
    Muise C, Felli P, Miller T, Pearce AR, Sonenberg L 2016. Planning for a single agent in a multi-agent environment using FOND. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence3206–12 Palo Alto, CA: AAAI Press
    [Google Scholar]
  87. 87. 
    Ma H, Hönig W, Cohen L, Uras T, Xu H et al. 2017. Overview: a hierarchical framework for plan generation and execution in multirobot systems. IEEE Intell. Syst. 32:6–12
    [Google Scholar]
  88. 88. 
    Nir R, Karpas E. 2019. Automated verification of social laws for continuous time multi-robot systems. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence7683–90 Palo Alto, CA: AAAI Press
    [Google Scholar]
  89. 89. 
    Lacerda B, Faruq F, Parker D, Hawes N 2019. Probabilistic planning with formal performance guarantees for mobile service robots. Int. J. Robot. Res. 38:1098–123
    [Google Scholar]
  90. 90. 
    Hawes N, Burbridge C, Jovan F, Kunze L, Lacerda B et al. 2017. The STRANDS project: long-term autonomy in everyday environments. IEEE Robot. Autom. Mag. 24:3146–56
    [Google Scholar]
  91. 91. 
    McDermott D. 2000. The 1998 AI Planning Systems Competition. AI Mag 21:235–55
    [Google Scholar]
  92. 92. 
    Simmons R, Apfelbaum D, Fox D, Goldman RP, Haigh KZ et al. 2000. Coordinated deployment of multiple, heterogeneous robots. Proceedings: 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems Vol 32254–60 Piscataway, NJ: IEEE
    [Google Scholar]
  93. 93. 
    Erol K, Hendler J, Nau DS 1996. Complexity results for HTN planning. Ann. Math. Artif. Intell. 18:69–93
    [Google Scholar]
  94. 94. 
    Alford R, Kuter U, Nau D 2009. Translating HTNS to PDDL: a small amount of domain knowledge can go a long way. Proceedings of the 21st International Joint Conference on Artificial Intelligence1629–34 San Francisco, CA: Morgan Kaufmann
    [Google Scholar]
  95. 95. 
    Cashmore M, Fox M, Long D, Magazzeni D, Ridder B et al. 2015. ROSPlan: planning in the Robot Operating System. Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling333–41 Palo Alto, CA: AAAI Press
    [Google Scholar]
  96. 96. 
    Quigley M, Conley K, Gerkey BP, Faust J, Foote T et al. 2009. ROS: an open-source robot operating system Paper presented at the IEEE International Conference on Robotics and Automation Kobe, Japan: May 12–17
    [Google Scholar]
/content/journals/10.1146/annurev-control-082619-100135
Loading
/content/journals/10.1146/annurev-control-082619-100135
Loading

Data & Media loading...

  • Article Type: Review Article
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