Abstract
The research presented in this paper approaches the issue of robot team navigation using relative positioning. With this approach each robot is equipped with sensors that allow it to independently estimate the relative direction of an assigned leader. Acoustic sensor systems are used and were seen to work very effectively in environments where datum relative positioning systems (such as GPS or acoustic transponders) are typically ineffective. While acoustic sensors provide distinct advantages, the variability of the acoustic environment presents significant control challenges. To address this challenge, directional control of the robot was accomplished with a feed forward neural network trained using a genetic algorithm, and a new approach to training using recent memories was successfully implemented. The design of this controller is presented and its performance is compared with more traditional classic logic and behavior controllers.
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References
Arkin, R.C. 1998. Behavior Based Robotics., MIT Press, pp. 10–14.
Balch, T. and Arkin, R. 1998. Behavior-based formation control for multirobot teams. IEEE Trans. on Robotics and Automation, 14(6).
Bradley, M. 1996. Environmental Acoustics Handbook, 2nd edn. p. 23.
Desai, J., Kumar, V., and Ostrowski, J.P. 1999. Control of changes in formation for a team of mobile robots. In Proceedings of 1999 IEEE Int. Conf. on Robotics and Automation, pp. 1556–1561.
Desai, J., Ostrowski, J.P., and Kumar, V. 1998. Controlling formations of multiple mobile robots. In Proceedings of 1998 IEEE Int. Conf. on Robotics and Automation, pp. 2864–2869.
Desai, J., Ostrowski, J.P., and Kumar, V. 2001. Modeling and control of formations of nonholonomic mobile robots. IEEE Trans. on Robotics and Automation, 17(6).
Fierro, R., Das, A., Kumar, V., and Ostrowski, J.P. 2001. Hybrid control of formations of robots. IEEE Int. Conf. on Robotics and Automation, Seoul, Korea, pp. 157–162.
Hu, X. PSO Tutorial, http://www.swarmintelligence.org/tutorials.php.
Kushleyev, A. and Vohra, V. 2004. Sound Localization. University of Maryland.
Leonard, N.E. and Fiorelli, E. 2001. Virtual leaders, artificial potentials and coordinated control of groups. In Proceedings of the 40th IEEE Conf. on Decision and Control, pp. 2968–2973.
Lewis, M.A. and Tan, K.-H. 1997. High precision formation control of mobile robots using virtual structures. Autonomous Robots, 4:387–403.
McDowell, P. 2005. Biologically Inspired Learning System. Louisiana State University, Baton Rouge, La, p. 41.
McDowell, P., Chen, J., and Bourgeois, B. 2002. UUV teams, control from a biological perspective. In Proceedings of the Oceans 2002 MTS/IEEE Conference, Biloxi MS, pp. 331–337.
Mitchell, T. 1997a. Machine Learning. McGraw-Hill, p. 105.
Mitchell, T. 1997b. Machine Learning. McGraw-Hill, p. 86.
Reeve, R. and Webb, B. 2003. New neural circuits for robot phonotaxiss. Philosophical Transcations of the Royal Society, 2245–2266.
Rucci, M., Wray, J., and Edelman, G.M. 2000. Robust localization of auditory and visual targets in a robotic barn owl. Robots and Autonomous System, 30:181–193.
Shah, V. 2003. Practical implementation of the sound localization algorithm on a robot. Rensselaer Polytechnic Institute, Research Experience for Undergraduates.
Smith, T.R., Hanbmann, H., and Leonard, N.E. 2001. Orientation control of multiple underwater vehicles with symmetry-breaking potentials. In Proceedings of the 40th IEEE Conf. on Decision and Control, pp. 4598–4603.
Tan, A.-H., Karim, S., and Sonenberg, L. 2005. Reactive plan execution versus reactive learning: A case study on minefield navigaiton. IAT, France.
University of Reading. Flocking robots, http://www.cyber.rdg.ac.uk/CIRG/robots/flocking.htm.
Urick, R.J. 1983. Principles of Underwater Sound 3rd edn. McGraw-Hill, p. 100.
Werner, G. and Dyer, M. 1992. Evolution of herding behavior in artificial animals. In Proceedings of Int. Conf. on Simulation of Adaptive Behaviors.
Yamaguchi, H. and Arai, T. 1994. Distributed and autonomous control method for generating shape of multiple robot group. In Proceedings of IEEE Int. Conf. on Intelligent Robots and Systems, pp. 800–807.
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Patrick McDowell received his bachelor's degree in Computer Science in 1984 from the University of Idaho. He spent the next 15 years working as a computer scientist for a small defense contractor where he specialized in real time data acquisition, application development, and image processing. In 1999 he received his master's degree in computer science from the University of Southern Mississippi. In 2000 he began work at the Naval Research Lab where he has focused on application of machine learning techniques to autonomous underwater navigation. In 2005 he received his Ph.D. in Computer Science from Louisiana State University. His research interests include legged robotics, machine learning, and artificial intelligence. In Fall of 2006 he joined Southeastern Louisiana University as an assistant professor of Computer Science.
Brian S. Bourgeois received his Ph.D. in Electrical Engineering from Tulane University located in New Orleans, LA in 1991. Since then he has worked at the Stennis Space Center, MS detachment of the Naval Research Laboratory. He has worked on research projects spanning an array of technologies including airborne survey sytems, acoustic backscattering, bathymetry and imaging sonar systems, the ORCA unmanned underwater vehicle and the development of an autonomous survey system for hydrographic survey ships. He is presently the head of the Position, Navigation and Timing team at NRL with research interests including underwater positioning and communications and autonomous navigation.
Ms. McDowell received her M.S. in Applied Physics in 2002 from the University or New Orleans. She is presently a candidate for a Ph. D. in Engineering and Applied Science. She joined the Naval Research Laboratory in 1991 as a research engineer and has spent most of that time working in experimental and theoretical acoustic modeling. Ms. McDowell's specific research interest lie in the areas of sonar performance analysis.
Dr. S. S. Iyengar is the Chairman and Roy Paul Daniels Chaired Professor of Computer Science at Louisiana State University and is also Satish Dhawan Chaired Professor at Indian Institute of Science. He has been involved with research in high-performance algorithms, data structures, sensor fusion, data mining, and intelligent systems since receiving his Ph.D. degree (1974) and his M.S. from the Indian Institute of Science (1970). He has been a consultant to several industrial and government organizations (JPL, NASA etc.). In 1999, Professor Iyengar won the most prestigious research award titled Distinguished Research Award and a university medal for his research contributions in optimal algorithms for sensor fusion/image processing.
Dr. Jianhua Chen received her Ph.D. in computer science in 1988 from Jilin University, Chang Chun, China. In August 1988, She joined the Computer Science Department of Louisiana State University, Baton Rouge, USA, where she is currently an associate professor. Dr. Chen's research interests include Machine Learning and Data Mining, Fuzzy Sets and Systems, Knowledge Representation and Reasoning.
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McDowell, P., Bourgeois, B., McDowell, P.J. et al. Relative positioning for team robot navigation. Auton Robot 22, 133–148 (2007). https://doi.org/10.1007/s10514-006-9021-8
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DOI: https://doi.org/10.1007/s10514-006-9021-8