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Plan Execution Based on Active Perception: Adding Hints to Plans

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Abstract

In this paper we present a complete control architecture for a mobile robot which enables it to achieve a set of proposed goals with a high degree of autonomy and to react to the changing environment in real time. Autonomy and robustness are achieved through careful selection and incremental implementation of a set of basic Motor-Behaviors that interpret the sensor readings (sonar, vision and odometric sensors) and actuate the motors. The plan is provided by a user, and is expressed as a sequence of goals and a series of hints on how to achieve them. These hints are based on the user's knowledge of the environment and of the robot's behavioral and perceptual abilities. A new set of behaviors, called Conductor-Behaviors, which inspect and modify Motor-Behaviors' attributes, have been introduced in order to link the robot's Motor-Behaviors to the user's plan. Finally, a canonical set of symbols, attached to the Motor-Behaviors, serves as well grounded symbols that the user can utilize to express the plans. We also report experimental results with a real robot that demonstrate how plans expressed as goals and hints to achieve them improve the robot's performance.

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References

  • Agre, P.E. and Chapman, D. 1990. What are plans for?. Robotics and Autonomous Systems, 6:17–34.

    Google Scholar 

  • Aloimonos, J. 1990. Purposive and qualitative active vision, DARPA Image Understanding Workshop, pp. 816–828.

  • Arkin, R.C. 1989a. Integrating behavioral, perceptual and world knowledge in reactive navigation. In Designing Autonomous Agents, P. Maes (Ed.).

  • Arkin, R.C. 1989b. Towards the unification of navigational planning and reactive control, AAAI Spring Symposium on Robot Navigation, pp. 1–5.

  • Ballard, D.H. 1991. Animate vision. Artificial Intelligence, 48(1):57–86.

    Google Scholar 

  • Bohrenstein, J. and Koren, Y. 1991. The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Transactions on Robotics and Automation, 7(3):278–288.

    Google Scholar 

  • Bonasso, R.P., Firby, R.J., Gat, E., Kortenkamp, D., Miller, D., and Slack, M. 1997. Experiences with an architecture for intelligent, reactive agents. Journal of Experimental and Theoretical Artificial Intelligence, 9(1):237–256.

    Google Scholar 

  • Brooks, M.J., de Agapito, L., Huynh, D.Q., and Baumela, L. 1996. Direct methods for self-calibration of a moving stereo head. In Proc. ECCV'96, Cambridge, UK, Vol. 2, pp. 415–426.

    Google Scholar 

  • Brooks, R.A. 1986. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automatition RA: 2(1):14–23.

    Google Scholar 

  • Brooks, R.A. 1989. A robot that walks; emergent behavior from a carefully evolved network. Neural Computation, 1(2):253–262.

    Google Scholar 

  • Brooks, R.A. 1990a. The behavior language; user's guide, A.I. Memo 1227, M.I.T.

  • Brooks, R.A. 1990b. Elephants don't play chess. Robotics and Autonomous Systems, 6:3–15.

    Google Scholar 

  • Brooks, R.A. 1991. Intelligence without representation. Artificial Intelligence, 47:139–159.

    Google Scholar 

  • Connell, J.H. 1989. A colony architecture for an artificial creature, M.I.T.A.I. Lab Tech Report 1151, M.I.T. Artificial Intelligence Laboratory.

  • Connell, J.H. 1991. SSS: A hybrid architecture applied to robot navigation, IEEE International Conference on Robotics and Automation, Nice, France, pp. 2719–2724.

  • Elfes, A. 1986. A distributed control architecture for an autonomous mobile robot. Artificial Intelligence, 1(2):135–144.

    Google Scholar 

  • Faugeras, O. 1993. Three-dimensional Computer Vision: A Geometric Viewpoint, MIT Press.

  • Fikes, R.E. and Nilsson, N.J. 1971. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2:189–208.

    Google Scholar 

  • Firby, R.J. 1987. An investigation into reactive planning in complex domains. In Proc. Sixth National Conference on Artificial Intelligence, Seattle, pp. 202–206.

  • Flynn, A.M., Brooks, R.A., Wells, W.M., and Barret, D.S. 1989. The world's largest one cubic inch robot. In Proc. IEEE Micro Electro Mechanical Systems, IEEE, pp. 98–101.

  • Haigh, K. and Veloso, M. 1997. High-level planning and low level-level execution: Towards a complete robotic agent. In Proc. of the International Conference on Autonomous Agents (AA).

  • Haralick, R.M. and Shapiro, L.G. 1992. Computer and Robot Vision, Vol. 1, Addison Wesley: Reading, MA.

    Google Scholar 

  • Harnard, S. 1995. Grounding symbolic capacity in robotic capacity. In The “Artificial Life” Route to “Artificial Intelligence”, L. Steels and R. Brooks (Eds.), Lawrence Erlbaum: New Haven, CT.

    Google Scholar 

  • Hayes-Roth, B. 1993. Opportunistic control of action in intelligent agents. IEEE Transactions on Systems, Man, and Cybernetic, 23(6):1575–1587.

    Google Scholar 

  • Horswill, I.D. 1992. Characterizing adaptation by constraint, Toward a practice of autonomous systems. In Proc. of the First European Conference on Artificial Life, MIT Press/Bradford Books, pp. 58–63.

  • Horswill, I.D. 1993. Specialitation of perceptual processes. Ph.D. thesis, MIT Artificial Intelligence Laboratory.

  • Horswill, I.D. 1995. Analysis of adaptation and environment, Artificial Intelligence, 73(1–2):1–30.

    Google Scholar 

  • Horswill, I.D. and Brooks, R.A. 1988. Situated vision in a dynamic world: Chasing objects, AAAI-88, pp. 796–800.

  • Kaebling, L.P. and Rosenschein, S.J. 1990. Action and planning in embedded agents. Robotics and Autonomous Systems, 6:35–48.

    Google Scholar 

  • Košecká, J., Bajcsy, R., and Mintz, M. 1994. Control of visually guided behaviors, GRASP Lab. Technical Report 367, Department of Computer and Information Science, University of Pennsylvania.

  • Lozano-Pérez, T. 1983. Spatial planning: A configuration space approach. IEEE Transactions on Computers, C-32(2):395–407.

    Google Scholar 

  • Maes, P. 1989. How to do the right thing. Connection Science Journal, 1(3):291–323.

    Google Scholar 

  • Malcolm, C. and Smithers, T. 1990. Symbol grounding via a hybrid architecture in an autonomous assembly system. Robotics and Autonomous Systems, 6:123–144.

    Google Scholar 

  • Mataric, M.J. 1992. Integration of representation into goal-driven behavior-based robots. IEEE Transactions on Robotics and Automation, 8(3):304–312.

    Google Scholar 

  • Moravec, H.P. 1977. Towards automatic visual obstacle avoidance. In Proc. of the 5th International Joint Conference on Artificial Intelligence (IJCAI), p. 584.

  • Moravec, H.P. 1988. Sensor fusion in certainty grids for mobile robots, AI Mag. pp. 61–74.

  • Myers, K.L. 1996. Advisable planning systems. In Advanced Planning Technology, A. Tate (Ed.), AAAI Press: Menlo Park, CA.

    Google Scholar 

  • Noreils, F.R. and Chatila, R.G. 1995. Plan execution monitoring and control architecture for mobile robots. IEEE Journal of Robotics and Automatition, 11(2):255–266.

    Google Scholar 

  • Parker, L.E. 1992. Adaptive action selection for cooperative agent teams. From Animals to Animats: International Conference on Simulation of Adaptive Behavior, pp. 442–450.

  • Payton, D.W. 1990. Internalized plans: A representation for action resources. Robotics and Autonomous Systems, 6:89–103.

    Google Scholar 

  • Pfeifer, R. 1996. Symbols, patterns, and behavior: Towards a new understanding of intelligence. In Proc. Japanese Conference on Artificial Intelligence, Tokyo, pp. 1–15.

  • Saffiotti, A., Konolige, K., Myers, K., and Ruspini, E.H. 1997. The saphira architecture: A design for autonomy. Journal of Experimental and Theoretical Artificial Intelligence (JETAI). Special issue on Architecures for Physical Agents, 9(1):215–235.

    Google Scholar 

  • Saffioti, A., Ruspini, E.H., and Konolige, K. 1993. A fuzzy controller for flakey, an autonomous mobile robot, Technical Note 529, Artificial Intelligence Center. SRI International, 333 Ravenswood Ave. Menlo Park, CA 94025, USA.

    Google Scholar 

  • Schneider-Fontán, M. 1996. Planning based on active perception. Ph.D. Thesis, Universidad Complutense Madrid, Facultad CC. Fisicas.

    Google Scholar 

  • Simmons, R. 1994. Structured control for autonomous robots. IEEE Transactions on Robotics and Automation, 10(1):34–43.

    Google Scholar 

  • Simmons, R., Goodwin, R., Haigh, K., Koenig, S., and Sullivan, J. 1997. A layered architecture for office delivery robots. First International Conference on Autonomous Agents, Marina del Rey, CA, pp. 245–252.

  • Thomson, A.M. 1977. The navigation system of the JPL robot. In Proc. of the International Joint Conference on Artificial Inteligence, pp. 749–757.

  • Wilkins, D.E. and Myers, K.L. 1995. A common knowledge representation for plan generation and reactive execution. Journal of Logic and Computation, 5:731–761.

    Google Scholar 

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Schneider-Fontán, M. Plan Execution Based on Active Perception: Adding Hints to Plans. Autonomous Robots 6, 53–68 (1999). https://doi.org/10.1023/A:1008872509483

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