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Unified Behavior Framework for Reactive Robot Control

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Abstract

Behavior-based systems form the basis of autonomous control for many robots. In this article, we demonstrate that a single software framework can be used to represent many existing behavior based approaches. The unified behavior framework presented, incorporates the critical ideas and concepts of the existing reactive controllers. Additionally, the modular design of the behavior framework: (1) simplifies development and testing; (2) promotes the reuse of code; (3) supports designs that scale easily into large hierarchies while restricting code complexity; and (4) allows the behavior based system developer the freedom to use the behavior system they feel will function the best. When a hybrid or three layer control architecture includes the unified behavior framework, a common interface is shared by all behaviors, leaving the higher order planning and sequencing elements free to interchange behaviors during execution to achieve high level goals and plans. The framework’s ability to compose structures from independent elements encourages experimentation and reuse while isolating the scope of troubleshooting to the behavior composition. The ability to use elemental components to build and evaluate behavior structures is demonstrated using the Robocode simulation environment. Additionally, the ability of a reactive controller to change its active behavior during execution is shown in a goal seeking robot implementation.

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References

  1. Arkin, R.C.: Behavior-based robot navigation for extended domains. Adapt. Behav. 1, 201–225 (1992). doi:10.1177/105971239200100204

    Article  Google Scholar 

  2. Arkin, R.C.: Behavior-Based Robotics. MIT Press, Cambridge, MA (1998)

    Google Scholar 

  3. Atrash, A., Koenig, S.: Probabilistic planning for behavior-based robots. In: Proceedings of the 14th International FLAIRS Conference (FLAIRS), pp. 531–535 (2001)

  4. Bischoff, R., Graefe, V.: Learning from nature to build intelligent autonomous robots. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3160–3165. Beijing, China (2006)

  5. Blank, D.S., Kumar, D., Meeden, L., Yanco, H.: Pyro: a python-based versatile programming environment for teaching robotics. J. Educ. Resour. Comput. (JERIC) 3(4), 1–15 (2003). doi:10.1145/1047568.1047569

    Article  Google Scholar 

  6. Braitenberg, V.: Vehicles: Experiments in Synthetic Psychology. MIT Press, Cambridge, MA (1984)

    Google Scholar 

  7. Brooks, R.A.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. RA-2, 14–23 (1986)

    MathSciNet  Google Scholar 

  8. Brooks, R.A.: Elephants don’t play chess. Robot. Auton. Syst. 6, 315 (1990). doi:10.1016/S0921-8890(05)80025-9

    Article  Google Scholar 

  9. Brooks, R.A.: New approaches to robotics. Science 253, 1227–1232 (1991). doi:10.1126/science.253.5025.1227

    Article  Google Scholar 

  10. Carreras, M., Ridao, P., Batlle, J., Nicosevici, T.: Efficient learning of reactive robot behaviors with a neural-Q-learning approach. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1020–1025 (2002)

  11. Caselli, S., Monica, F., Reggiani, M.: YARA: a software framework enhancing service robot dependability. In: Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference, pp. 1970–1976 (2005)

  12. Connell, J.: A behavior-based arm controller. IEEE Trans. Robot. Autom. 5, 784–791 (1989)

    Article  Google Scholar 

  13. Dong, M., Sun, Z.: A behavior-based architecture for unmanned aerial vehicles. In: Proceedings of the 2004 IEEE International Symposium on Intelligent Control, pp. 149–155. Taipei, Taiwan (2004)

  14. Firby, R.J.: Task networks for controlling continuous processes. In: Proceedings of the Second International conference on AI Planning Systems. Chicago, IL (1994)

  15. Gallagher, J.: An evolvable hardware layer for global and local learning of locomotion control in a hexapod robot. Int. J. Artif. Intell. Tools 14(6), 999–1017 (2005). doi:10.1142/S021821300500251X

    Article  Google Scholar 

  16. Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns. Addison-Wesley, Boston, MA (1994)

    Google Scholar 

  17. Gat, E.: On three-layer architectures. In: Artificial Intelligence and Mobile Robots: Case Studies of Successful Robot Systems, pp. 195–210. AAAI Press/MIT Press, Cambridge, MA (1998)

    Google Scholar 

  18. Gerkey, B., Vaughan, R.T., Howard, A.: The player/stage project: tools for multi-robot and distributed sensor systems. In: Proceedings of the 11th International Conference on Advanced Robotics, pp. 317–323 (2003)

  19. Gordon, E., Logan, B.: Managing goals and resources in dynamic environments. In: Davis, D.N., Hershey, P.A. (eds.) Visions of Mind: Architectures for Cognition and Affect, pp. 225–253. Information Science Publishing (2005)

  20. Hoffmann, F.: Evolutionary algorithms for fuzzy control system design. Proc. IEEE 89(9), 1318–1333 (2001). doi:10.1109/5.949487

    Article  Google Scholar 

  21. Huq, R., Mann, G.K.I., Gosine, R.G.: Behavior-based robot control using fuzzy discrete event system. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 1146–1153. Vancouver, BC, Canada (2006)

  22. Kaelbling, L.P.: An architecture for intelligent reactive systems. In: SRI International Technical Note No. 400. Menlo Park, CA (1986)

  23. Konolige, K.: The Saphira architecture: a design for autonomy. J. Exp. Theor. Artif. Intell. 9, 215–235 (1997). doi:10.1080/095281397147095

    Article  Google Scholar 

  24. LeCun, Y., Muller, W., Ben, J., Cosatto, E., Flepp, B.: Off-road obstacle avoidance through end-to-end learning. In: Advances in Neural Information Processing Systems (NIPS 2005). MIT Press (2005)

  25. Lussier, B., Gallien, M., Guiochet, J., Ingrand, F., Killijian, M.-O., Powell, D.: Planning with diversified models for fault-tolerant robots. In: ICAPS 2007 The International Conference on Automated Planning & Scheduling, pp. 2–9. Providence, RI (2007)

  26. Maes, P.: Situated agents can have goals. Robot. Auton. Syst. 6, 49–70 (1990)

    Article  Google Scholar 

  27. Magg, S., Philippides, A.: GasNets and CTRNNs—a comparison in terms of evolvability. From Anim. Animats 9, 461–472 (2006)

    Article  Google Scholar 

  28. Moravec, H.P.: Robot Rover Visual Navigation. UMI Research Press Ann Arbor, Mich (1981)

    Google Scholar 

  29. Nilsson, N.J.: Shakey the Robot. SRI International (1984)

  30. O’Kelly, J., Gibson, J.P.: RoboCode & Problem-based learning: a non-prescriptive approach to teaching programming. In: Proceedings of the 11th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, ITiCSE06, pp. 217–221. Bologna, Italy (2006)

  31. Peterson, G.L., Cook, D.J.: Integrating decision theoretic planning in a robot architecture. Robot. Auton. Syst. 42(2), 89–106 (2003). doi:10.1016/S0921-8890(02)00326-3

    Article  MATH  Google Scholar 

  32. Rosenblatt, J.: DAMN: a distributed architecture for mobile navigation. In: Presented at the AAAI Spring Symposium on Lessons Learned for Implemented Software Architectures for Physical Agents. Palo Alto, CA, 1995.

  33. Rosenblatt, J.: Utility fusion: map-based planning in a behavior-based system. In: Field and Service Robotics, pp. 411–418 (1998)

  34. Rusu, P., Petriu, E.M., Whalen, T.E., Cornell, A., Spoelder, H.J.W.: ANN arrangement of fuzzy controls. IEEE Trans. Instrum. Meas. 52(4), 1335–1340 (2003)

    Article  Google Scholar 

  35. Trujillo, L., Olague, G., Lutton, E., de Vega, F.F.: Discovering several robot behaviors through speciation. In: Applications of Evolutionary Computing, pp. 164–174. Springer, Berlin (2008)

    Chapter  Google Scholar 

  36. Utz, H., Kraetzschmar, G., Mayer, G., Palm, G.: Hierarchical behavior organization. In: 2005 International Conference on Intelligent Robots and Systems, pp. 2598–2605. IEEE/RSJ, Edmonton, Canada (2005)

    Google Scholar 

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Correspondence to Gilbert L. Peterson.

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Woolley, B.G., Peterson, G.L. Unified Behavior Framework for Reactive Robot Control. J Intell Robot Syst 55, 155–176 (2009). https://doi.org/10.1007/s10846-008-9299-1

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