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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tani, J. Fukumara, N. |
| Copyright Year | 1994 |
| Description | Author affiliation: Sony Comput. Sci. Lab. nc., Tokyo, Japan (Tani, J.) |
| Abstract | This study shows a novel approach for learning sensory- and task-based navigation of a mobile robot. The learning of task-based behavior is formulated as an embedding problem of dynamical systems: the desired trajectories in a task space should be embedded into an adequate sensory-based internal state space so that unique mapping from the internal state space to the motor command can be established. We discuss how such internal state space and its mapping can be self-organized by means of neural learning methodologies. Physical experiment by a mobile robot with laser range sensor was conducted, which showed that learning of homing and cyclic routing tasks were successfully realized on two different neural network (NN) architectures; the time delayed type and the recurrent type. The navigation was inherently robust against miscellaneous noise since those tasks were embedded in the global attractor dynamics of a fixed point and limit cycling. |
| Starting Page | 886 |
| Ending Page | 893 |
| File Size | 647573 |
| Page Count | 8 |
| File Format | |
| ISBN | 0780319338 |
| DOI | 10.1109/IROS.1994.407488 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-09-12 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | State-space methods Navigation Mobile robots Neural networks Trajectory Laser noise Laser theory Routing Recurrent neural networks Delay effects |
| Content Type | Text |
| Resource Type | Article |
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