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Learning to Drive Among Obstacles

Bradley Hamner, Sebastian Scherer and Sanjiv Singh
Conference Paper, Carnegie Mellon University, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2663 - 2669, October, 2006

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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.

BibTeX Reference
title = {Learning to Drive Among Obstacles},
author = {Bradley Hamner and Sebastian Scherer and Sanjiv Singh},
booktitle = {2006 IEEE/RSJ International Conference on Intelligent Robots and Systems},
school = {Robotics Institute , Carnegie Mellon University},
month = {October},
year = {2006},
pages = {2663 - 2669},
address = {Pittsburgh, PA},