Advertisement

Behavior-Based Systems

  • Maja J. MatarićEmail author
  • François MichaudEmail author

Abstract

Nature is filled with examples of autonomous creatures capable of dealing with the diversity, unpredictability, and rapidly changing conditions of the real world. Such creatures must make decisions and take actions based on incomplete perception, time constraints, limited knowledge about the world, cognition, reasoning and physical capabilities, in uncontrolled conditions and with very limited cues about the intent of others. Consequently, one way of evaluating intelligence is based on the creatureʼs ability to make the most of what it has available to handle the complexities of the real world. The main objective of this chapter is to clarify behavior-based systems and their use in single- and multi-robot autonomous control problems and applications. The chapter is organized as follows. Section 38.1 overviews robot control, introducing behavior-based systems in relation to other established approaches to robot control. Section 38.2 follows by outlining the basic principles of behavior-based systems that make them distinct from other types of robot control architectures. The concept of basis behaviors, the means of modularizing behavior-based systems, is presented in Sect. 38.3. Section 38.4 describes how behaviors are used as building blocks for creating representations for use by behavior-based systems, enabling the robot to reason about the world and about itself in that world. Section 38.5 presents several different classes of learning methods for behavior-based systems, validated on single-robot and multi-robot systems. Section 38.6 provides an overview of various robotics problems and application domains that have successfully been addressed with behavior-based control. Finally, Sect. 38.7 concludes the chapter.

Keywords

Mobile Robot Robot Control Motivational Module Basis Behavior Abstract Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

AAAI

American Association for Artificial Intelligence

AuRA

autonomous robot architecture

BLE

broadcast of local eligibility

HRI

human–robot interaction

RL

reinforcement learning

References

  1. 38.1.
    J.S. Albus: Outline for a theory of intelligence, IEEE Trans. Syst. Man Cybernet. 21(3), 473–509 (1991)CrossRefMathSciNetGoogle Scholar
  2. 38.2.
    G. Girald, R. Chatila, M. Vaisset: An Integrated Navigation and Motion Control System for Autonomous Multisensory Mobile Robots, Proceedings First International Symposium on Robotics Research (MIT Press, Cambridge 1983)Google Scholar
  3. 38.3.
    H. Moravec, A. Elfes: High resolution maps from wide angle sonar, Proceedings IEEE International Conference on Robotics and Automation (1995)Google Scholar
  4. 38.4.
    J. Laird, P. Rosenbloom: An Investigation into Reactive Planning in Complex Domains, Proceedings Ninth National Conference of the American Association for Artificial Intelligence (MIT Press, Cambridge 1990) pp. 1022–1029Google Scholar
  5. 38.5.
    N. J. Nilsson: Shakey the Robot, Technical Report (325) (SRI International, 1984)Google Scholar
  6. 38.6.
    S.J. Rosenschein, L.P. Kaelbling: A situated view of representation and control, Artif. Intell. 73, 149–173 (1995)CrossRefGoogle Scholar
  7. 38.7.
    R.A. Brooks: Elephants donʼt play chess. In: Designing Autonomous Agents: Theory and Practive form Biology to Engineering and Back (The MIT Press, Bradford Book 1990) pp. 3–15Google Scholar
  8. 38.8.
    R. Brooks, J. Connell: Asynchrounous distributed control system for a mobile robot, Proceedings SPIE Intelligent Control and Adaptive Systems (1986) pp. 77–84Google Scholar
  9. 38.9.
    R.A. Brooks: A robust layered control system for a mobile robot, IEEE J. Robot. Autom. RA-2(1), 14–23 (1986)CrossRefGoogle Scholar
  10. 38.10.
    P.E. Agre, D. Chapman: Pengi: An implementation of a theory of activity, Proceedings Sixth National Conference of the American Association for Artificial Intelligence (1987) pp. 268–272Google Scholar
  11. 38.11.
    R.A. Brooks: Intelligence without representation, Artif. Intell. 47, 139–159 (1991)CrossRefGoogle Scholar
  12. 38.12.
    M. Schoppers: Universal plans for reactive robots in unpredictable domains, Proceedings International Joint Conference on Artificial Intelligence (1987) pp. 1039–1046Google Scholar
  13. 38.13.
    P.E. Agre, D. Chapman: What are plans for?. In: Designing Autonomous Agents: Theory and Practive form Biology to Engineering and Back, ed. by P. Maes (MIT Press, Bradford Book 1990) pp. 17–34Google Scholar
  14. 38.14.
    G.N. Saridis: Intelligent robotic control, IEEE Trans. Autom. Contr. AC-28(5), 547–557 (1983)CrossRefMathSciNetGoogle Scholar
  15. 38.15.
    R.J. Firby: An investigation into reactive planning in complex domains, Proceedings AAAI Conference (1987) pp. 202–206Google Scholar
  16. 38.16.
    R. Arkin: Towards the unification of navigational planning and reactive control, Proceedings American Association for Artificial Intelligence, Spring Symposium on Robot Navigation (1989) pp. 1–5Google Scholar
  17. 38.17.
    C. Malcolm, T. Smithers: Symbol grounding via a hybrid architecture in an autonomous assembly system. In: Designing Autonomous Agents: Theory and Practive form Biology to Engineering and Back, ed. by P. Maes (MIT Press, Bradford Book 1990) pp. 123–144Google Scholar
  18. 38.18.
    J.H. Connell: SSS: A hybrid architecture applied to robot navigation, Proceedings IEEE International Conference on Robotics and Automation (1992) pp. 2719–2724Google Scholar
  19. 38.19.
    E. Gat: Integrating planning and reacting in a heterogeneous asynchronous architecture for controlling real-world mobile robots, Proceedings National Conference on Artificial Intelligence (1992) pp. 809–815Google Scholar
  20. 38.20.
    M. Georgeoff, A. Lansky: Reactive reasoning and planning, Proceedings Sixth National Conference of the American Association for Artificial Intelligence (1987) pp. 677–682Google Scholar
  21. 38.21.
    B. Pell, D. Bernard, S. Chien, E. Gat, N. Muscettola, P. Nayak, M. Wagner, B. Williams: An autonomous spacecraft agent prototype, Autonom. Robot. 1-2(5), 1–27 (1998)Google Scholar
  22. 38.22.
    R.C. Arkin: Behavior-Based Robotics (MIT Press, Bradford Book 1998)Google Scholar
  23. 38.23.
    M.J. Matarić: Reinforcement learning in the multi-robot domain, Autonom. Robot. 4(1), 73–83 (1997)CrossRefGoogle Scholar
  24. 38.24.
    P. Bonasso, R.J. Firby, E. Gat, D. Kortenkamp, D.P. Miller, M.G. Slack: Experiences with an architecture for intelligent reactive agents, Proceedings International Joint Conference on Artificial Intelligence (1995)Google Scholar
  25. 38.25.
    P. Pirjanian: Multiple objective behavior-based control, Robot. Autonom. Syst. 31(1-2), 53–60 (2000)CrossRefGoogle Scholar
  26. 38.26.
    D. Payton, D. Keirsey, D. Kimble, J. Krozel, J. Rosenblatt: Do whatever works: A robust approach to fault-tolerant autonomous control, Appl. Intell. 2(3), 225–250 (1992)CrossRefGoogle Scholar
  27. 38.27.
    P. Maes: Situated agents can have goals. In: Designing Autonomous Agents: Theory and Practive form Biology to Engineering and Back, ed. by P. Maes (MIT Press, Bradford Book 1990) pp. 49–70Google Scholar
  28. 38.28.
    P. Maes: The dynamics of action selection, Proceedings International Joint Conference on Artificial Intelligence (1989) pp. 991–997Google Scholar
  29. 38.29.
    A. Saffiotti: The uses of fuzzy logic in autonomous robot navigation, Soft Comput. 1, 180–197 (1997)Google Scholar
  30. 38.30.
    F. Michaud: Selecting behaviors using fuzzy logic, Proceedings IEEE International Conference on Fuzzy Systems (1997) pp.Google Scholar
  31. 38.31.
    P. Pirjanian: Behavior coordination mechanisms–State-of-the-art, (Technical Report IRIS-99-375, University of Southern California, Institute of Robotics and Intelligent Systems) (1999)Google Scholar
  32. 38.32.
    E. Gat: On three-layer architectures. In: Artificial Intelligence and Mobile Robotics, ed. by D. Kortenkamp, R. Bonasso, R. Murphy (MIT/AAAI Press, Cambridge 1998)Google Scholar
  33. 38.33.
    M.J. Matarić: Integration of representation into goal-driven behavior-based robots, IEEE Trans. Robot. Autom. 8(3), 304–312 (1992)CrossRefGoogle Scholar
  34. 38.34.
    M.J. Matarić: Navigating with a Rat Brain: A Neurobiologically-Inspired Model for Robot Spatial Representation, From Animals to Animats. Proceedings First International Conference on Simulation of Adaptive Behaviors (MIT Press, Bradford Book 1990) pp. 169–175Google Scholar
  35. 38.35.
    M. Nicolescu, M.J. Matarić: Experience-based representation construction: Learning from human and robot teachers, Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (2001) pp. 740–745Google Scholar
  36. 38.36.
    M. Nicolescu, M.J. Matarić: A hierarchical architecture for behavior-based robots, Proceedings International Joint Conference on Autonomous Agents and Multiagent Systems (2002)Google Scholar
  37. 38.37.
    L.E. Parker: ALLIANCE: An architecture for fault tolerant multirobot cooperation, IEEE Trans. Robot. Autom. 14(2), 220–240 (1998)CrossRefGoogle Scholar
  38. 38.38.
    M.J. Matarić: Designing and understanding adaptive group behavior, Adapt. Behav. 4(1), 50–81 (1995)Google Scholar
  39. 38.39.
    O.C. Jenkins, M.J. Matarić: Deriving action and behavior primitives from human motion data, Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (2002) pp. 2551–2556Google Scholar
  40. 38.40.
    O.C. Jenkins, M.J. Matarić: Automated derivation of behavior vocabularies for autonomous humanoid motion, Proceedings Second International Joint Conference on Autonomous Agents and Multiagent Systems (2003)Google Scholar
  41. 38.41.
    M.J. Matarić: Designing Emergent Behaviors: From Local Interactions to Collective Intelligence, From Animals to Animats 2. Proceedings Second International Conference on Simulation of Adaptive Behaviors, ed. by J.-A. Meyer, H. Roitblat, S. Wilson (MIT Press, Cambridge 1992) pp. 432–441Google Scholar
  42. 38.42.
    S. Saripalli, D.J. Naffin, G.S. Sukhatme: Autonomous Flying Vehicle Research at the University of Southern California, Multi-Robot Systems: From Swarms to Intelligent Automata, Proceedings of the First International Workshop on Multi-Robot Systems, ed. by A. Schultz, L.E. Parker (Kluwer, Dordrecht 2002) pp. 73–82Google Scholar
  43. 38.43.
    M.J. Matarić: Behavior-based control: Examples from navigation, learning, and group behavior, J. Exp. Theor. Artif. Intell. 9(2-3), 323–336 (1997)CrossRefGoogle Scholar
  44. 38.44.
    P. Maes, R.A. Brooks: Learning to coordinate behaviors, Proceedings Eigth National Conference on Artificial Intelligence AAAI (1990) pp. 796–802Google Scholar
  45. 38.45.
    H. Yanco, L.A. Stein: An Adaptive Communication Protocol for Cooperating Mobile Robots, From Animals to Animats 3. Proceedings of the Third International Conference on Simulation of Adaptive Behaviors (MIT Press, Cambridge 1993) pp. 478–485Google Scholar
  46. 38.46.
    J.R. del Millàn: Learning Efficient Reactive Behavioral Sequences from Basic Reflexes in a Goal-Directed Autonomous Robot, From Animals to Animats 3. Proceedings Third International Conference on Simulation of Adaptive Behaviors (MIT Press, Cambrdige 1994) pp. 266–274Google Scholar
  47. 38.47.
    L. Parker: Learning in cooperative robot teams, Proceedings International Joint Conference on Artificial Intelligence (1993) pp. 12–23Google Scholar
  48. 38.48.
    M.J. Matarić: Learning to Behave Socially, From Animals to Animats 3. Proceedings Third International Conference on Simulation of Adaptive Behaviors (The MIT Press, Cambridge 1994) pp. 453–462Google Scholar
  49. 38.49.
    M. Asada, E. Uchibe, S. Noda, S. Tawaratsumida, K. Hosoda: Coordination of multiple behaviors acquired by a vision-based reinforcement learning, Proceedings IEEE/RSJ/GI International Conference on Intelligent Robots and Systems, Munich, Germany (1994)Google Scholar
  50. 38.50.
    J. McCarthy: Making robots conscious of their mental states, AAAI Spring Symposium (1995)Google Scholar
  51. 38.51.
    T. Smithers: On why Better Robots Make it Harder, From Animals to Animats: Proceedings 3rd International Conference on Simulation of Adaptive Behavior (MIT Press, Cambridge 1994) pp. 64–72Google Scholar
  52. 38.52.
    D. McFarland, T. Bösser: Intelligent Behavior in Animals and Robots (MIT Press, Bradford Book 1993)Google Scholar
  53. 38.53.
    P. Maes: A Bottom-Up Mechanism for Behavior Selection in an Artificial Creature, From Animals to Animats. Proceedings First International Conference on Simulation of Adaptive Behavior (MIT Press, Cambridge 1991) pp. 238–246Google Scholar
  54. 38.54.
    B.M. Blumberg, P.M. Todd, P. Maes: No bad dogs: Ethological lessons for learning in Hamsterdam, From Animals to Animats: Proceedings International Conference on Simulation of Adaptive Behavior, ed. by P. Maes, M.J. Matarić, J.-A. Meyer, J. Pollack, S.W. Wilson (1996) pp. 295–304Google Scholar
  55. 38.55.
    C. Breazeal, B. Scassellati: Infant-like social interactions between a robot and a human caregiver, Adapt. Behav. 8(1), 49–74 (2000)CrossRefGoogle Scholar
  56. 38.56.
    F. Michaud, M.T. Vu: Managing robot autonomy and interactivity using motives and visual communication, Proceedings International Conference on Autonomous Agents (1999) pp. 160–167Google Scholar
  57. 38.57.
    F. Michaud: EMIB – Computational architecture based on emotion and motivation for intentional selection and configuration of behaviour-producing modules, Cogn. Sci. Q., Special Issue on Desires, Goals, Intentions and Values: Comput. Arch. 3-4, 340–361 (2002)Google Scholar
  58. 38.58.
    F. Michaud, P. Prijanian, J. Audet, D. Létourneau: Artificial Emotion and Social Robotics, Distributed Autonomous Robotic Systems, ed. by L.E. Parker, G. Bekey, J. Barhen (Springer, Berlin, Heidelberg 2000) pp. 121–130Google Scholar
  59. 38.59.
    A. Stoytchev, R. Arkin: Incorporating motivation in a hybrid robot architecture, J. Adv. Comput. Intell. Intell. Inf. 8(3), 269–274 (2004)Google Scholar
  60. 38.60.
    S. Mahadevan, J. Connell: Automatic programming of behavior-based robots using reinforcement learning, Artif. Intell. 55, 311–365 (1992)CrossRefGoogle Scholar
  61. 38.61.
    M.J. Matarić: Reward functions for accelerated learning, Proceedings 11th International Conference on Machine Learning, New Brunswick, NJ, ed. by William W. Cohen, Haym Hirsh (Morgan Kauffman Publishers 1994) pp. 181–189Google Scholar
  62. 38.62.
    H. Gleitman: Psychology (NORTON, New York 1981)Google Scholar
  63. 38.63.
    M. Dorigo, M. Colombetti: Robot Shaping: An Experiment in Behavior Engineering (MIT Press, Cambridge 1997)Google Scholar
  64. 38.64.
    M. Nicolescu, M.J. Matarić: Learning and interacting in human-robot domains, IEEE Transactions on Systems, Man, Cybernetics, special issue on Socially Intelligent Agents - The Human in the Loop (2001)Google Scholar
  65. 38.65.
    A.K. McCallum: Hidden state and reinforcement learning with instance-based state identification, IEEE Trans. Syst. Man Cybernet. – Part B: Cybernetics 26(3), 464–473 (1996)CrossRefGoogle Scholar
  66. 38.66.
    F. Michaud, M.J. Matarić: Learning from history for behavior-based mobile robots in non-stationary environments, Spec. Iss. Learn. Autonom. Robot. Mach. Learn./Autonom. Robot. 31/5, 141–167/335–354 (1998)Google Scholar
  67. 38.67.
    F. Michaud, M.J. Matarić: Representation of behavioral history for learning in nonstationary conditions, Robot. Autonom. Syst. 29(2), 1–14 (1999)CrossRefGoogle Scholar
  68. 38.68.
    A. Agha, G. Bekey: Phylogenetic and ontogenetic learning in a colony of interacting robots, Autonom. Robot. 4(1), 85–100 (1997)CrossRefGoogle Scholar
  69. 38.69.
    R.A. Brooks, L. Stein: Building brains for bodies, Autonom. Robot. 1(1), 7–25 (1994)CrossRefGoogle Scholar
  70. 38.70.
    B. Webb: Robotic Experiments in Cricket Phonotaxis, From Animals to Animats 3. Proceedings Third International Conference on Simulation of Adaptive Behaviors (MIT Press, Cambridge 1994) pp. 45–54Google Scholar
  71. 38.71.
    J.L. Jones: Robots at the tipping point, IEEE Robot. Autom. Mag. 13(1), 76–78 (2006)CrossRefGoogle Scholar
  72. 38.72.
    P. Rusu, E.M. Petriu, T.E. Whalen, A. Cornell, H.J.W. Spoelder: Behavior-based neuro-fuzzy controller for mobile robot navigation, IEEE 52(4), 1335–1340 (2003)Google Scholar
  73. 38.73.
    R. Huq, G.K.I. Mann, R.G. Gosine: Behaviour modulation technique in mobile robotics using fuzzy discrete event system, IEEE Trans. Robot. 22, 903–916 (2006)CrossRefGoogle Scholar
  74. 38.74.
    L.E. Parker: Current research in multirobot systems, Artif. Life Robot. 7(1–2), 1–5 (2003)CrossRefGoogle Scholar
  75. 38.75.
    L.E. Parker, M. Chandra, F. Tang: Enabling Autonomous Sensor-Sharing for Tightly-Coupled Cooperative Tasks, Multi-Robot Systems. From Swarms to Intelligent Automata, Vol. III, ed. by L.E. Parker, F.E. Schneider, A.C. Schultz (Springer, Berlin, Heidelberg 2005) pp. 119–230Google Scholar
  76. 38.76.
    B.B. Werger, M.J. Matarić: Broadcast of Local Eligibility for Multi-Target Observation, Proceedings of the 5th International Conference on Distributed Autonomous Robotic Systems (2000) pp. 347–356Google Scholar
  77. 38.77.
    B.P. Gerkey, M.J. Matarić: Principled communication for dynamic multi-robot task allocation. In: Experimental Robotics VII, LNCIS 271, ed. by D. Rus, S. Singh (Springer, Berlin, Heidelberg 2001)Google Scholar
  78. 38.78.
    B.P. Gerkey, M.J. Matarić: Sold!: Auction methods for multi-robot coordination, IEEE Trans. Robot. Autom. 18(5), 758–768 (2002)CrossRefGoogle Scholar
  79. 38.79.
    B.P. Gerkey, M.J. Matarić: Pusher-watcher: An approach to fault-tolerant tightly-coupled robot coordination, Proceedings IEEE International Conference on Robotics and Automation (2002) pp. 464–469Google Scholar
  80. 38.80.
    L. Iocchi, D. Nardi, M. Piaggio, A. Sgorbissa: Distributed coordination in heterogeneous multi-robot systems, Autonom. Robot. 15(2), 155–168 (2004)CrossRefGoogle Scholar
  81. 38.81.
    M. Batalin, G. Sukhatme: Coverage, exploration and deployment by a mobile robot and communication network, Telecommun. Syst. 26(2–4), 181–196 (2004)CrossRefGoogle Scholar
  82. 38.82.
    A.W. Stroupe, T. Balch: Value-based action selection for observation with robot teams using probabilistic techniques, Robot. Autonom. Syst. 50(2–3), 85–97 (2005), special issue on Multi-Robots in Dynamic EnvironmentsCrossRefGoogle Scholar
  83. 38.83.
    R. Simmons, T. Smith, M.B. Dias, D. Goldberg, D. Hershberger, A. Stentz, R. Zlot: A Layered Architecture for Coordination of Mobile Robots, Proceedings from the NRL Workshop on Multi-Robot Systems Multi-Robot Systems: From Swarms to Intelligent Automata (2002)Google Scholar
  84. 38.84.
    J. Nembrini, A. Winfield, C. Melhuish: Minimalist Coherent Swarming of Wireless Networked Autonomous Mobile Robots, Proceedings of the 7th International Conference on Simulation of Adaptive Behavior (MIT Press, CAmbridge 2002) pp. 373–382Google Scholar
  85. 38.85.
    M. Egerstedt, X. Hu: Formation constrained multi-agent control, IEEE Trans. Robot. Autom. 17(6), 947–951 (2001)CrossRefGoogle Scholar
  86. 38.86.
    A. Olenderski, M. Nicolescu, S. Louis: A behavior-based architecture for realistic autonomous ship control, Proceesings, IEEE Symposium on Computational Intelligence and Games (2006)Google Scholar
  87. 38.87.
    M. Nicolescu, O.C. Jenkins, A. Olenderski: Learning behavior fusion estimation from demonstration, Proceedings, IEEE International Symposium on Robot and Human Interactive Communication (2006) pp. 340–345Google Scholar
  88. 38.88.
    K. Gold, B. Scassellati: Learning about the self and others through contingency, AAAI Spring Symposium on Developmental Robotics (2005)Google Scholar
  89. 38.89.
    M. Baker, H.A. Yanco: Automated street crossing for assistive robots, Proceedings of the International Conference on Rehabilitation Robotics (2005) pp. 187–192Google Scholar
  90. 38.90.
    M. Williamson: Postural Primitives: Interactive Behavior for a Humanoid Robot Arm, Proceedings of the International Conference on Simulation of Adaptive Behavior (MIT Press, Cambridge 1996)Google Scholar
  91. 38.91.
    M. Marjanovic, B. Scassellati, M. Williamson, R. Brooks, C. Breazeal: The Cog Project: Building a humanoid robot. In: Computation for Metaphors, Analogy and Agents, Vol. 1562 of Springer Lecture Notes in Artificial Intelligence, ed. by C. Nehaniv (Springer, Berlin, Heidelberg 1998)Google Scholar
  92. 38.92.
    A. Edsinger: Robot Manipulation in Human Environments. Ph.D. Thesis , Massachusettes Institute of Technology, Department of Electrical Engineering and Computer Science (2007)Google Scholar
  93. 38.93.
    C. Breazeal: Infant-like social interactions between a robot and a human caretaker, Adapt. Behav. 8(1), 49–74 (2000)CrossRefGoogle Scholar
  94. 38.94.
    H. Ishiguro, T. Kanda, K. Kimoto, T. Ishida: A robot architecture based on situated modules, Proceedings of the International Conference on Intelligent Robots and Systems (1999) pp. 1617–1623Google Scholar
  95. 38.95.
    T. Kanda, T. Hirano, D. Eaton, H. Ishiguro: Person identification and interaction of social robots by using wireless tags, Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (2003) pp. 1657–1664Google Scholar
  96. 38.96.
    F. Michaud, Y. Brosseau, C. Cote, D. Letourneau, P. Moisan, A. Ponchon, C. Raievsky, J.-M. Valin, E. Beaudry, F. Kabanza: Modularity and integration in the design of a socially interactive robot, Proceedings IEEE International Workshop on Robot and Human Interactive Communication (2005) pp. 172–177Google Scholar
  97. 38.97.
    F. Michaud, G. Lachiver, C.T. Le Dinh: Architectural methodology based on intentional configuration of behaviors, Comput. Intell. 17(1), 132–156 (2001)CrossRefGoogle Scholar
  98. 38.98.
    D. Letourneau, F. Michaud, J.-M. Valin: Autonomous robot that can read, EURASIP J. Appl. Signal Process. 17, 1–14 (2004), Special Issue on Advances in Intelligent Vision Systems: Methods and ApplicationsGoogle Scholar
  99. 38.99.
    J.-M. Valin, F. Michaud, B. Hadjou, J. Rouat: Localization of simultaneous moving sound sources for mobile robot using a frequency-domaine steered beamformer approach, Proceedings IEEE International Conference on Robotics and Automation (2004) pp. 1033–1038Google Scholar
  100. 38.100.
    J.-M. Valin, F. Michaud, J. Rouat: Robust 3D localization and tracking of sound sources using beamforming and particle filtering, Proceedings International Conference on Audio, Speech and Signal Processing (2006) pp. 221–224Google Scholar
  101. 38.101.
    F. Michaud, C. Cote, D. Letourneau, Y. Brosseau, J.-M. Valin, E. Beaudry, C. Raievsky, A. Ponchon, P. Moisan, P. Lepage, Y. Morin, F. Gagnon, P. Giguere, M.-A. Roux, S. Caron, P. Frenette, F.Kabanza: Spartacus attending the 2005 AAAI Conference, Autonomous Robots, Special Issue on AAAI Mobile Robot Competition (2007)Google Scholar
  102. 38.102.
    F. Michaud, D. Letourneau, M. Frechette, E. Beaudry, F. Kabanza: Spartacus, scientific robot reporter, Proceedings of the Workshop on AAAI Mobile Robot Competition (2006)Google Scholar
  103. 38.103.
    E. Beaudry, Y. Brosseau, C. Cote, C. Raievsky, D. Letourneau, F. Kabanza, F. Michaud: Reactive planning in a motivated behavioral architecture, Proceedings American Association for Artificial Intelligence Conference (2005) pp. 1242–1247Google Scholar
  104. 38.104.
    K. Haigh, M. Veloso: Planning, execution and learning in a robotic agent, Proceedings Fourth International Conference on Artificial Intelligence Planning Systems (1998) pp. 120–127Google Scholar
  105. 38.105.
    S. Lemai, F. Ingrand: Interleaving temporeal planning and execution in robotics domains, Proceeedings National Conference on Artificial Intelligence (2004) pp. 617–622Google Scholar
  106. 38.106.
    F. Michaud, J.F. Laplante, H. Larouche, A. Duquette, S. Caron, D. Letourneau, P. Masson: Autonomous spherical mobile robotic to study child development, IEEE Trans. Syst. Man. Cybernet. 35(4), 1–10 (2005)Google Scholar
  107. 38.107.
    F. Michaud, S. Caron: Roball, the rolling robot, Autonom. Robot. 12(2), 211–222 (2002)CrossRefzbMATHGoogle Scholar
  108. 38.108.
    R.A. Brooks: Cambrian Intelligence – The Early History of the New AI (MIT Press, Cambridge 1999)zbMATHGoogle Scholar
  109. 38.109.
    R. Pfeifer, C. Scheier: Understanding Intelligence (MIT Press, Cambridge 2001)Google Scholar
  110. 38.110.
    R.R. Murphy: An Introduction to AI Robotics (MIT Press, Cambridge 2000)Google Scholar
  111. 38.111.
    M.J. Matarić: The Robotics Primer (MIT Press, Cambridge 2007)Google Scholar
  112. 38.112.
    F. Martin: Robotic Explorations: A Hands-On Introduction to Engineering (Prentice Hall, Upper Saddle River 2001)Google Scholar
  113. 38.113.
    J.L. Jones, A.M. Flynn: Mobile Robots - Inspiration to Implementation (Peters, Wellesley 1993)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of Electrical Engineering and Computer EngineeringUniversité de SherbrookeSherbrookeCanada

Personalised recommendations