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Learning obstacle avoidance parameters from operator behavior
Author(s) -
Hamner Bradley,
Singh Sanjiv,
Scherer Sebastian
Publication year - 2006
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.20171
Subject(s) - obstacle avoidance , mobile robot , obstacle , computer science , collision avoidance , path (computing) , robot , artificial intelligence , operator (biology) , control (management) , control engineering , simulation , engineering , collision , geography , biochemistry , repressor , transcription factor , gene , chemistry , computer security , archaeology , programming language
This paper concerns 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 observing a human driver. We also present results from operation on an autonomous robot as well as in simulation, and compare the results from our method to another commonly used learning method. We find that the proposed method generalizes well and is capable of learning from a small number of samples. © 2007 Wiley Periodicals, Inc.