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Carnegie Mellon Robotics Institute

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Abstract

Abstract-- This paper reports on an outdoor mobile robot that learns to avoid collisions by observing a human driver operate a vehicle equipped with sensors that continuously produce a map of the local environment. We have implemented steering control that models human behavior in trying to avoid obstacles while trying to follow a desired path. Here we present the formulation for this control system and its independent parameters, and then show how these parameters can be automatically estimated by observation of a human driver. We present results from experiments with a vehicle (both real and simulated) that avoids obstacles while following a prescribed path at speeds up to 4 m/sec. We compare the proposed method with another method based on Principal Component Analysis, a commonly used learning technique. We find that the proposed method generalizes well and is capable of learning from a small number of examples.

Notes

Associated Center(s) / Consortia: Field Robotics Center

Associated Project(s): Unmanned Ground Vehicle for Security

Number of pages: 7

Text Reference

Bradley Hamner, Sebastian Scherer, and Sanjiv Singh, "Learning to Drive Among Obstacles," 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, October, 2006, pp. 2663 - 2669.

BibTeX Reference

@inproceedings{Hamner_2006_5535,
   author = "Bradley Hamner and Sebastian Scherer and Sanjiv Singh",
   title = "Learning to Drive Among Obstacles",
   booktitle = "2006 IEEE/RSJ International Conference on Intelligent Robots and Systems",
   pages = "2663 - 2669",
   month = "October",
   year = "2006",
}

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