Abstract
We present a system for visual robotic docking using an omnidirectional camera coupled with the actor critic reinforcement learning algorithm. The system enables a PeopleBot robot to locate and approach a table so that it can pick an object from it using the pan-tilt camera mounted on the robot. We use a staged approach to solve this problem as there are distinct sub tasks and different sensors used. Starting with random wandering of the robot until the table is located via a landmark, and then a network trained via reinforcement allows the robot to rum to and approach the table. Once at the table the robot is to pick the object from it. We argue that our approach has a lot of potential allowing the learning of robot control for navigation removing the need for internal maps of the environment. This is achieved by allowing the robot to learn couplings between motor actions and the position of a landmark.
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References
Filliat, D. & Meyer, J.A. Map-based navigation in mobile robots. I. A review of localization strategies. J. of Cognitive Systems Research 2003, 4(4):243–282
Filliat, D. & Meyer, J.A. Map-based navigation in mobile robots. II. A review of map-learning and path-planning strategies. J. of Cognitive Systems Research 2003,4(4):283–317
Dissanayake, M.W.M.G. Newman, P. Clark, S. Durrant-White, H.F. & Csorba, M. A solution to the simultaneous loacalization and map building (SLAM) problem. IEEE Transactions on Robotics and Automation 2001, 17(3):229–241
Tomatis, N. Nourbakhsh, I. & Siegwart, R. Hybrid simultaneous localization and map building; a natural integration of topological and metric. Robotics and Autonomous Systems 2003,44:3–14
Guivant, J.E. Masson, F.R. & Nebot E.M. Simultaneous localization and map building using natural features of absolute information. Robotics and Autonomous Systems 2002,40:79–90
Carelli, R. & Freire, E.O. Corridor navigation and wall-following stable control for sonar-based mobile robots. Robotics and Autonomous Systems 2003,45:235–247
Maaref, H. & Barret, C. Sensor-based navigation of a mobile robot in an indoor environment. Robotics and Autonomous systems 2002, 38:1–18
Delgado, E. & Barreiro, A. Sonar-based robot navigation using nonlinear robust observers. Automatica 2003, 39:1195–1203
Menegatti, E. Zoccarato, M. Pagello, E. & Ishiguro, H. Image-based Monte Carlo localisation with omnidirectional images. Robotics and Autonomous Systems 2004,48:17–30
Fiala, M. & Basu, A. Robot navigation using panoramic tracking. Partem Recognition 2004, 37:2195–2215
Jogan, M. & Leonardis, A. Robust localisation using an omnidirectional appearance-based subspace model of environment. Robotics and Autonomous Systems 2003,45:51–72
Gaussier, P. Joulain, C. Banquet, J.P. Lepretre, S. & Revel, A. The Visual Homing Problem: An Example of Robotics/Biology Cross Fertilization. Robotics and Autonomous Systems 2000, 30:155–180
Gaussier, P. Revel, A. Joulain, C. & Zrehen, S. Living in a Partially Structured Environment: How to Bypass the Limitations of Classical Reinforcement Techniques. Robotics and Autonomous Systems 1997, 20:225–250
Sutton, R.S. & Barto, A.G. Reinforcement Learning An Introduction. MIT Press 1998
Wörgötter, F. Actor-Critic models of animal control — a critique of reinforcement learning. Proceeding of Fourth International ICSC Symposium on Engineering of Intelligent Systems 2004
Weber, C. Wermter, S. & Zochios, A. Robot docking with neural vision and reinforcement. Knowledge Based Systems 2004, 12:165–172
Foster, D.J. Morris, R.G.N. & Dayan, P. A model of hippocampally dependent navigation, using the temporal learning rule. Hippocampus 2000, 10:1–16
Kondo, T. & Ito, K. A reinforcement learning with evolutionary state recruitment strategy for autonomous mobile robot control. Robotics and Autonomous Systems 2004,46:11–124
Lee, I.S.K. & Lau, A.Y.K. Adaptive state space partitioning for reinforcement learning. Engineering Applications of Artificial Intelligence 2004, 17:577–588
Weber, C. Muse, D. Elshaw, M. & Wermter, S. Neural robot docking involving a camera-direction dependent visual-motor coordinate transformation. AI 2005 (submitted)
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© 2006 Springer-Verlag London Limited
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Muse, D., Weber, C., Wermter, S. (2006). Robot Docking Based on Omnidirectional Vision and Reinforcement Learning. In: Bramer, M., Coenen, F., Allen, T. (eds) Research and Development in Intelligent Systems XXII. SGAI 2005. Springer, London. https://doi.org/10.1007/978-1-84628-226-3_3
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DOI: https://doi.org/10.1007/978-1-84628-226-3_3
Publisher Name: Springer, London
Print ISBN: 978-1-84628-225-6
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