Generation and Organization of Behaviors for Autonomous Robots

THESIS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GENERATION AND ORGANIZATION OF BEHAVIORS FOR AUTONOMOUS ROBOTS JIMMY PETTERSSON Department of Applied Mechanics CHALMERS UNIVERSITY OF TECHNOLOGY Göteborg, Sweden 2006 Generation and organization of behaviors for autonomous robots JIMMY PETTERSSON ISBN 91-7291-833-0 c JIMMY PETTERSSON, 2006 Doktorsavhandlingar vid Chalmers tekniska högskola Ny serie nr 2515 ISSN 0346-718X Department of Applied Mechanics Chalmers University of Technology SE–412 96 Göteborg Sweden Telephone: +46 (0)31–772 1000 Chalmers Reproservice Göteborg, Sweden 2006 Till Anita och Nils-Åke Generation and organization of behaviors for autonomous robots JIMMY PETTERSSON Department of Applied Mechanics Chalmers University of Technology Abstract In this thesis, the generation and organization of behaviors for autonomous robots is studied within the framework of behavior-based robotics (BBR). Several different behavioral architectures have been considered in applications involving both bipedal and wheeled robots. In the case of bipedal robots, generalized finite-state machines (GFSMs) were used for generating a smooth gait for a (simulated) five- link bipedal model, constrained to move in the sagittal plane. In addition, robust balancing was achieved, even in the presence of perturbations. Furthermore, in simulations of a three-dimensional bipedal robot, gaits were generated using clus- ters of central pattern generators (CPGs) connected via a feedback network. A third architecture, namely a recurrent neural network (RNN), was used for generating several behaviors in a simulated, one-legged hopping robot. In all cases, evolutionary algorithms (EAs) were used for optimizing the behaviors. The important problem of behavioral organization has been studied using the utility function (UF) method, in which behavior selection is obtained through evolutionary optimization of utility functions that provide a common currency for the comparison of behaviors. In general, the UF method requires the use of simulations. Thus, an important part of this thesis has been the development of a general-purpose software library (UFLib) implementing the UF method. In order to study the properties of the UF method, several behavioral organization problems, mostly involving wheeled robots, have been considered. Most importantly, it was found that the UF method greatly simplifies the search for solutions to a wide variety of behavioral organization problems and requires a minimum of hand-coding. Furthermore, the results show that the use of multiple simulations (for the evaluation of a robot) significantly improves the ability of the robot to select appropriate behaviors. For the EA, it was found that the standard crossover procedure, which swaps entire utility functions between individuals, performed at least as well as several modified operators, and that the mutation rate should be set so as to generate around three parameter modifications per individual. Fi- nally, some early results are presented concerning the use of the UF method in connection with a robot intended for transportation and delivery. Keywords: autonomous robots, behavioral organization, action selection, utility function method, evolutionary robotics. i List of publications This thesis is based on the work contained in the following papers, referred to by Roman numerals in the text: I. Pettersson, J., Sandholt, H., and Wahde, M., A flexible evolutionary method for the generation and implementation of behaviors for humanoid robots, in: Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Humanoids 2001, Tokyo, Japan, November 2001, pp. 279–286. II. Pettersson, J., EvoDyn: A simulation library for behavior-based robotics, Technical Report, Chalmers University of Technology, September 2003. III. Pettersson, J. and Wahde, M., Application of the utility function method for behavioral organization in a locomotion task, IEEE Transactions on Evolutionary Computation, Volume 9, Issue 5, Oct. 2005, pp. 506–521. IV. Wolff, K., Pettersson, J., Heralic,´ A., and Wahde, M., Structural Evolu- tion of Central Pattern Generators for Bipedal Walking in 3D Simulation, to appear in: Proceedings of the 2006 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2006), Taipei, Taiwan, 2006. V. Pettersson, J. and Wahde, M., UFLibrary: A Simulation Library Imple- menting the Utility Function Method for Behavioral Organization in Au- tonomous Robots, submitted to: International Journal on Artificial Intelli- gence Tools, 2005. VI. Wahde, M., Pettersson, J., Sandholt, H., and Wolff, K., Behavioral Selection using the Utility Function Method: A Case Study Involving a Simple Guard Robot, in: Proceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE 2005), Fukui, Japan, 2005, pp. 261–266. iii iv List of publications VII. Pettersson, J., Sandberg, D., Wolff, K., and Wahde, M., Behavioral selection in domestic assistance robots: A comparison of different methods for optimization of utility functions, to appear in: Proceedings of the 2006 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2006), Taipei, Taiwan, 2006. VIII. Pettersson, J. and Wahde, M., Improving generalization in a behavioral selection problem using multiple simulations, in: Proceedings of the Joint 3rd International Conference on Soft Computing and Intelligent Systems and the 7th International Symposium on advanced Intelligent Systems (SCIS & ISIS 2006), Tokyo, Japan, 2006, pp. 989–994. IX. Wahde, M. and Pettersson, J., A General-purpose Transportation Robot: An Outline of Work in Progress, in: Proceedings of the 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 06), Hatfield, United Kingdom, 2006, pp. 722–726. X. Pettersson, J. and Wahde, M., Behavior selection for localization and nav- igation in a transportation robot, using evolvable internal state variables, manuscript to be submitted to: Autonomous Robots. In addition to the papers listed above, the author has also been involved in the work presented in references [57], [59], [69], [70], and [71]. Acknowledgments I would like to thank Chalmers Center for Mechatronics and System Engineering (CHASE) and the Carl Trygger Foundation for financial support for my research project. A special thanks goes to my thesis advisor Mattias Wahde for his never-ending enthusiasm and immense competence. Without his excellent guidance and super- vision during these years, this thesis would never have been. Thank you. I would also like to thank all colleagues (present and former) at the Depart- ment of Applied Mechanics, who have contributed to make Chalmers such an en- joyable working place. I am also grateful to my co-workers at Waseda University in Tokyo. Finally, a very special thank you to Marie for your patience and love, and for being by my side throughout this journey. Jimmy Pettersson Göteborg, 2006 v Technical terms used in the thesis The technical terms used in this thesis are listed below. For each term, the page number for its first occurrence is also given. A E activation networks . 28 embodied evolution . 22 arbitration methods . 28 evaluation ..................... 49 artificial intelligence . 27 evolution strategies . 15 artificial neural networks . 6 evolutionary algorithms . 2 autonomous robots. .1 explicit encoding . 18 auxiliary behaviors . 32 external variables . 33 B F behavior dithering . 35 feedforward networks . 10 behavior-based robotics . 1 fuzzy command fusion . 28 behavioral hierarchy . 36 G behavioral organization. .1 genes..........................16 behavioral repertoire . 1 genetic algorithms . 15 benefit.........................32 genetic programming . 15 binary encoding . 17 genome........................16 genotype.......................16 C gridmap.......................50 central pattern generators . 6 chromosomes..................16 I command fusion methods . 28 if-then-else-rules . 6 crossover......................16 incremental evolution . 29 individual......................15 D infraredsensor ................. 40 definitionfiles..................40 intelligent behavior . 32 degrees of freedom . 12 internal abstract variables. .33 vii viii Technical terms used in the thesis internal physical variables . 33 V intraspecies crossover . 19 validationset...................49 L W laserrangefinder...............40 waypoints......................52 M macromutations . 20 multiple simulations . 49 mutation.......................16 O overallfitness..................49 P pathfinding .................... 50 phenotype ..................... 16 population.....................15 potentialfield..................28 preferences .................... 32 R rational behavior . 32 reactivity......................27 real-number encoding . 17 S sagittalplane....................9 selection.......................16 single-neuron crossover . 20 statevariables..................33 subsumption method . 28 T taskbehaviors..................32 trainingset.....................49 U unstructured environments . 2 utility.........................31 utility function . 31 utility function method . 1 Table of contents 1 Introduction and motivation 1 1.1 Contributions ............................ 4 2 Behavior-based robotics 5 2.1 Architectures for behaviors . 6 2.1.1 If-then-else-rules . 7 2.1.2 Recurrent neural networks (RNNs) . 10 2.1.3 Central pattern generators (CPGs) . 11 3 Evolutionary robotics 15 3.1 Evolutionary algorithms . 15 3.2 Encodingschemes ........................

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