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An auction behavior-based robotic architecture for service robotics

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Abstract

Service robots have the potential of improving the quality of life and assist with people’s daily activities. Such robots must be capable of operating over long periods of time, performing multiple tasks, and scheduling them appropriately for execution. In addition, service robots must be capable of dealing with tasks whose goals may be in conflict with each other and would need to determine, dynamically, which task to pursue in such a case. Adding to the complexity of the problem is the fact that some task requests may have time constraints—deadlines by which the task has to be completed. Given the dynamic nature of the environment, the robots must make decisions on what tasks to pursue in situations where there could be incomplete or missing information. The robots should also be capable of accepting requests for new tasks or services at runtime, while possibly working on another task. In order to achieve these requirements, this paper presents the Auction Behavior-Based Robotic Architecture that brings the following contributions: (1) it uses an auction mechanism to determine the relevance of a task to run at any given time, (2) it handles multiple user requests while dealing with potentially critical time constraints and incomplete information, (3) it enables long-term robot operation and (4) it allows for dynamic assignment of new tasks. The proposed system is validated on a physical robotic platform, the Segway RMP\(^{\circledR }\) and in simulation.

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References

  1. Arkin RC (1998) Behavior-based robotics, 1 edn. Massachusetts Institute of Technology, Massachusetts, pp 1–491

  2. Rosenblatt JK (1997) DAMN: a distributed architecture for mobile navigation. J Exp Theor Artif Intell 9(2–3):339–360

    Article  Google Scholar 

  3. Riekki J, Roning J (1997) Reactive task execution by combining action maps. In: Proceedings of the 1997 IEEE/RSJ international conference on intelligent robots and systems, IROS’97, vol 1. Grenoble, France, pp 224–230.

  4. Hoff J, Bekey G (1995) An architecture for behaviour coordination learning. In: Proceeding of the IEEE international conference on neural networks, vol 5. Perth, WA, Australia, pp 2375–2380

  5. Saffiotti A (1997) The uses of fuzzy logic in autonomous robot navigation. Soft Comput A Fusion Found Methodol Appl 1(4):180–197

    Google Scholar 

  6. Saffiotti A, Konolige K, Ruspini EH (1995) A multivalued logic approach to integrating planning and control. Artif Intell 76(1–2):481–526

    Google Scholar 

  7. Yen J, Pfluger N (1995) A fuzzy logic based extension to Payton and Rosenblatt’s command fusion method for mobile robot navigation. IEEE Trans Syst Man Cybern 25(6):971–978

    Article  Google Scholar 

  8. Arkin RC (1987) Motor schema based mobile robot navigation: An approach to programming by behavior. In: Proceedings of the IEEE conference on robotics and automation (ICRA’87), vol 4. University of Massachusetts, Amherst, Massachusetts, pp 264–271

  9. Maes P (1989) How to do the right thing. Connect Sci 1(3):291–323

    Article  Google Scholar 

  10. Brooks RA (1986) A robust layered control-system for a mobile robot. IEEE J Robotics Autom 2(1):14–23

    Article  Google Scholar 

  11. Koseck J, Bajcsy R (1994) Discrete event systems for autonomous mobile agents. Robotics Auton Syst 12(3–4):187–198

    Article  Google Scholar 

  12. Brooks R (1990) Elephants don’t play chess. Robotics Auton Syst 6(1–2):3–15

    Article  Google Scholar 

  13. Proetzsch M, Luksch T, Berns K (2010) Development of complex robotic systems using the behavior-based control architecture iB2C. Robotics Auton Syst 58(1):46–67

    Article  Google Scholar 

  14. Haazebroek P, van Dantzig S, Hommel B (2011) A computational model of perception and action for cognitive robotics. Cogn Process 12(4):355–365

    Article  Google Scholar 

  15. Lim GH, Suh IH (2012) Improvisational goal-oriented action recommendation under Incomplete Knowledge Base. In: Proceedings of IEEE international conference on robotics and automation (ICRA). Singapore, Southeast Asia, pp 896–903

  16. Davis R, Smith RG (1983) Negotiation as a metaphor for distributed problem solving. Artif Intell 20(1):63–109

    Article  Google Scholar 

  17. Brandt F, Brauer W, Weiss G (2000) Task assignment in multiagent systems based on vickrey-type auctioning and leveled commitment contracting. In: Cooperative Information Agents IV-The Future of Information Agents in Cyberspace vol 1860, pp 95–106

  18. Faratin P, Sierra C, Jennings NR (1998) Negotiation decision functions for autonomous agents. Robotics Auton Syst 24(3–4):159–182

    Article  Google Scholar 

  19. Gerkey BP, Mataric MJ (2002) Sold!: auction methods for multirobot coordination. IEEE Trans Robotics Autom 18(5):758–768

    Article  Google Scholar 

  20. Jennings B, Arvidsson Å (1999) Co-operating market/ant based multi-agent systems for intelligent network load Control. Intell Agents Telecommun Appl, pp 71–71

  21. Jung H, Tambe M, Kulkarni S (2001) Argumentation as distributed constraint satisfaction: applications and results. In: Proceedings of the 5th international conference on autonomous agents. ACM Press, New York, pp 324–331

  22. Krovi R, Graesser AC, Pracht WE (1999) Agent behaviors in virtual negotiation environments. IEEE Trans Syst Man Cybern Part C Appl Rev 29(1):15–25

    Article  Google Scholar 

  23. Matari MJ, Sukhatme GS, Østergaard EH (2003) Multi-robot task allocation in uncertain environments. Auton Robots 14(2):255–263

    Article  Google Scholar 

  24. Smith RG (1980) The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Trans Comput 100(12):1104–1113

    Article  Google Scholar 

  25. Sycara K, Zeng D (1996) Coordination of multiple intelligent software agents. Int J Coop Inf Syst 5(2):181–212

    Article  Google Scholar 

  26. Wellman MP, Wurman PR (1998) Market-aware agents for a multiagent world. Robotics Auton Syst 24(3–4):115–125

    Article  Google Scholar 

  27. Sheng W et al (2006) Distributed multi-robot coordination in area exploration. Robotics Auton Syst 54(12):945–955

    Article  Google Scholar 

  28. Dias MB, Stentz A (2003) Traderbots: a market-based approach for resource, role, and task allocation in multirobot coordination. Carnegie Mellon University, Pittsburgh

  29. Sahota MK (1994) Action selection for robots in dynamic environments through inter-behaviour bidding. Anim Anim 3:138–142

    Google Scholar 

  30. Almeida A, Figueiredo L (2006) A product oriented approach to dynamic scheduling. In: IEEE international in industrial technology, ICIT. Mumbai, India, pp 523–528

  31. Chan FTS, Wong T, Chan L (2007) Lot splitting under different job shop conditions. In: IEEE congress on evolutionary computation. Singapore, Southeast Asia, pp 4722–4728

  32. Wang JB, Wang MZ (2011) Worst-case behavior of simple sequencing rules in flow shop scheduling with general position-dependent learning effects. Ann Oper Res, pp 1–15

  33. Younas M et al (2008) Priority scheduling service for E-commerce web servers. Inf Syst E Business Manag 6(1):69–82

    Article  Google Scholar 

  34. Liu H, Abraham A, Wang Z (2009) A multi-swarm approach to multi-objective flexible job-shop scheduling problems. Fundamenta Informaticae 95(4):465–489

    MathSciNet  Google Scholar 

  35. Lei D (2010) Solving fuzzy job shop scheduling problems using random key genetic algorithm. Int J Adv Manuf Technol 49(1):253–262

    Article  Google Scholar 

  36. Li J-Q, Pan Q-K, Gao K-Z (2011) Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int J Adv Manuf Technol 55:10

    Google Scholar 

  37. Miao X, Luh PB, Kleinman DL (1990) Dynamic job scheduling with strict deadline. In: Proceedings of the 29th IEEE conference on decision and control, vol 1. Hawaii, Honolulu, pp 116–121

  38. Anandaraman C (2011) An improved sheep flock heredity algorithm for job shop scheduling and flow shop scheduling problems. Int J Ind Eng 2:749–764

    Google Scholar 

  39. Hu Y, Yin M, Li X (2011) A novel objective function for job-shop scheduling problem with fuzzy processing time and fuzzy due date using differential evolution algorithm. Int J Adv Manuf Technol 56:1–14

    Article  Google Scholar 

  40. Kouider A, Bouzouia B (2011) Multi-agent job shop scheduling system based on co-operative approach of idle time minimisation. Int J Prod Res 50(2):409–424

    Article  Google Scholar 

  41. Watson JP, Beck J, Barbulescu L, Whitley L, Howe A (2001) Toward a descriptive model of local search cost in job-shop scheduling. In: Proceedings of the 6th European Conference on Planning (ECP’01), Toledo, Spain

  42. Yahyaoui A, Fnaiech F (2006) Recent trends in intelligent job shop scheduling. In: Proceedings of the 1st IEEE international conference on e-learning in industrial electronics. Hammamet, Tunisia, pp 191–195

  43. Towle BA, Nicolescu M (2010) Fusing multiple sensors through behaviors with the distributed architecture. In: 2010 IEEE international conference on multisensor fusion and integration for intelligent systems, Salt Lake, Utah, 2010, pp 115–120

  44. Towle Jr BA, Nicolescu M (2011) Applying dynamic conditions to an auction behavior-based robotic architecture. In: International conference on artificial intelligence (ICAI’11), July 18–21, vol 1, p 6

  45. Towle B, Nicolescu M (2012) Real-world implementation of an Auction Behavior-Based Robotic Architecture (ABBRA). In: IEEE international conference on technologies for practical robot applications (TePRA). Woburn, MA, pp 79–85

  46. Nicolescu MN, Mataric MJ (2002) A hierarchical architecture for behavior-based robots. In: Proceedings of the 1st international joint conference on Autonomous agents and multiagent systems: part 1. ACM Press, New York, USA, pp 227–233

  47. Stage P (2012) Player. http://playerstage.sourceforge.net/ Accessed 15 Oct 2012

  48. Garage W (2012) ROS \({\vert }\) Willow Garage. http://www.willowgarage.com/pages/software/ros-platform. Accessed 7 Mar 2012

  49. Fong T et al (2001) A personal user interface for collaborative human-robot exploration. In: 6th International symposium on artificial intelligence, robotics, and automation in space (iSAIRAS). Canada, Montreal, p 23

  50. Fong T, Thorpe C, Baur C (2001) Collaborative control: a robot-centric model for vehicle teleoperation. Carnegie Mellon University, The Robotics Institute

  51. Fong T, Thorpe C, Baur C (2003) Robot, asker of questions. Robotics Auton Syst 42(3–4):235–243

    Article  MATH  Google Scholar 

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Correspondence to Bradford A. Towle Jr.

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Towle, B.A., Nicolescu, M. An auction behavior-based robotic architecture for service robotics. Intel Serv Robotics 7, 157–174 (2014). https://doi.org/10.1007/s11370-013-0141-7

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  • DOI: https://doi.org/10.1007/s11370-013-0141-7

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