As a small team, we had to make choices that are reflected in our choice of hardware and algorithm. One of the most time intensive aspect of devising a robot is the development of an accurate motion model and then an adequate sensor/perceptual model [1].
We could not spend an entire team and the time to devise a motor model as well as a sensor model as what other teams have done. Some of it stems from the fact that we are not interested in getting M.S. theses written for the moment as it is the case here. Furthermore, our vehicle is far from being protected from the elements. And we considered that it would become difficult to rely on a model that could become less and less accurate as the car was getting older. Another aspect of our decision making came from the actuators we used. In the case of the steering wheel, there are moments when the steering motor can slip (especially in large deviations). This is owed to two factors: the laptop is busy doing other tasks can sometime send only the steering task with a delay or there is too much torque too be applied too fast. This results in a command control that would be difficult to model. Finally, we were interested in developing a robot that would learn it's own behavior while being supervised with very little labeled data. In other words, we want the learning to be based on data coming from an actual driving behavior. Not much data understanding should come from any subsequent data processing.
Our motor model relies on our ability to drive around while in the car through the keyboard of the command laptop. This supervised learning of the motor model was built by producing a probability distribution of the next state of the car as a function of the previous state and the commands sent by the "supervised" commands. We used a a variety of techniques to build this probability distribution including the simple but robust Experimental Probabilistic Hypersurface devised at SCM [2] as well as Subjective mapping representation techniques (by Michael Bowling, Ali Ghodsi and Dana Wilkinson, Subjective Localization with Action Respecting Embedding and [3], [4] [5] [6])
In the latter case, we decided against using the SDE approach but use Compressed Sensing instead for the dimensionality reduction aspect of the problem for building the sensor model. Some aspect of the reduction include some of the ideas in [7].
References:
[1] D. Fox, J. Hightower, L. Liao, D. Schulz, and G. Borriello., Bayesian Filtering for Location Estimation, IEEE Pervasive Computing, 2003.
[2] Robust Mathematical Modeling, SCM, Experimental Probabilistic Hypersurfaces
[3] [pdf] [ps.gz] Subjective mapping, Michael Bowling, Dana Wilkinson, and Ali Ghodsi.
In New Scientific and Technical Advances in Research (NECTAR) of the Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI) , pages 1569--1572, 2006.
[4] [pdf] [ps.gz] Subjective localization with action respecting embedding, Michael Bowling, Dana Wilkinson, Ali Ghodsi, and Adam Milstein. In Proceedings of the International Symposium of Robotics Research (ISRR) , 2005.
[5] [pdf] Action respecting embedding, Michael Bowling, Ali Ghodsi, and Dana Wilkinson.
In Proceedings of the Twenty-Second International Conference on Machine Learning (ICML) , pages 65--72, 2005.
[6] [pdf] [ps.gz] Learning subjective representations for planning, Dana Wilkinson, Michael Bowling, and Ali Ghodsi. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI) , pages 889--894, 2005.
[7] INRIA :: [inria-00117116, version 1] Real-time Vehicle Motion Estimation Using Texture Learning and Monocular Vision
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