Abstract
In behaviour-based robotics (BBR), the artificial brain (or control system) of a robot is built from a repertoire of basic behaviours which are activated or deactivated through a process of behaviour selection that uses the state of the robot (and, possibly, its environment) as input [1–3].
Many behaviour-based systems are strongly reactive, i.e., there is a more or less direct connection between perception and action unlike the systems defined in classical artificial intelligence (AI) which are more deliberative, but typically operate quite slowly. In practice, it is common that the definition of a robotic brain involves a combination of the bottom-up approach defined in BBR and the topdown approach defined in classical AI [1].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arkin RC (1998) Behaviour-based robotics, The MIT Press
Pirjanian P (1999) Behaviour-coordination mechanisms – State-of-the-art, Institute for Robotics and Intelligent Systems, University of Southern California, Technical report IRIS-99-375
Wahde M (2003) A method for behavioral organizsation for autonomous robots based on evolutionary optimisation of utility functions, Journal of Systems and Control Engineering, 217 (4); 249–258
Wahde M, Pettersson J (2006) A general-purpose transportation robot: An outline of work in progress, Proc. 15th IEEE Int. Symposium on Robot and Human Interactive Communication (RO-MAN 06); 722–726.
Bryson JJ (2007) Mechanisms of action selection: Introduction to the special issue, Adaptive Behaviour, 15 (1); 5–8
Pettersson J, Wahde M (2007) Uflibrary: A simulation library implementing the utility function method for behavioural organization in autonomous robots, Int. J. on Artificial Intelligence Tools, 16; 507–536
Blumberg B (1994) Action-selection in Hamsterdam: Lessons from ethology, Proc. 3rd Int. Conf. on the Simulation of Adaptive Behaviour:108–117
Bergener T, Steinhage A (1998) An architecture for behavioural organization using dynamical systems, 3rd German Workshop on Artificial Life; 31–42
Brooks R (1986) A robust layered control system for a mobile robot, IEEE J. of Robotics and Automation, RA-2 (1); 14–23
Maes P (1989) How to do the right thing, Connection Science Journal, 1 (3); 291–323
Khatib O (1985) Real-time obstacle avoidance for manipulators and mobile robots, Proc. IEEE Int. Conf. on Robotics and Automation; 500–505
Gadanho SC, Hallam J (2001) Emotion-triggered learning in autonomous robot control, Cybernetics and Systems, 32; 531–559
McFarland D (1998) Animal Behaviour: Psychobiology, Ethology and Evolution, 3rd ed., Prentice Hall
McFarland D, Spier E (1997) Basic cycles, utility, and opportunism in self-sufficient robots, Robotics and Autonomous Systems, 20; 179–190
Bryson JJ, Prescott TJ, Seth AK (Eds.) (2007) Modelling Natural Action Selection, Proc. of an International Workshop, AISB Press
von Neumann J, Morgenstern O (1953) Theory of Games and Economic Behaviour, 3rd ed., Princeton University Press
Staddon JER (2001) Adaptive Dynamics: The Theoretical Analysis of Behaviour, MIT Press
Pettersson J, Wahde M (2006) Improving generalization in a behavioural selection problem using multiple simulations, Proc. SCIS & ISIS 2006, Tokyo; 989–994
Pettersson J (2006) Generation and organization of behaviors for autonomous robots, Ph.D. dissertation, Chalmers University of Technology
Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths in graphs, IEEE Trans. on Systems Science and Cybernetics, SSC-4 (2); 100–107
Jakobi N, Husbands P, Harvey I (1995) Noise and the reality gap: The use of simulation in evolutionary robotics, Lecture Notes in Computer Science, 929; 704–720
Holland J (1992) Adaptation in natural and artificial systems, MIT Press
Pettersson J, Hartono P, Wahde M (2007) A behaviour module of odometry recalibration in autonomous robots, Proc. 4th Int. Symp. on Autonomous Minirobots for Edutainment and Research, AMiRE2007
Damasio A (1994) Descartes’ error: Emotion, reason and the human brain, Avon Books
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag London Limited
About this chapter
Cite this chapter
Wahde, M. (2010). The Utility Function Method for Behaviour Selection in Autonomous Robots. In: Bradley, D., Russell, D. (eds) Mechatronics in Action. Springer, London. https://doi.org/10.1007/978-1-84996-080-9_9
Download citation
DOI: https://doi.org/10.1007/978-1-84996-080-9_9
Publisher Name: Springer, London
Print ISBN: 978-1-84996-079-3
Online ISBN: 978-1-84996-080-9
eBook Packages: EngineeringEngineering (R0)