DocumentCode :
2019461
Title :
Can robots recognize common Marine gestures?
Author :
Ruttum, Mary ; Parikh, Sarangi P.
Author_Institution :
Weapons & Syst. Eng. Dept., US Naval Acad., Annapolis, MD, USA
fYear :
2010
fDate :
7-9 March 2010
Firstpage :
227
Lastpage :
231
Abstract :
This paper provides a different method for human-robot interaction and further encourages communication between human users and their robotic counterparts. The focus of our work is to develop a human-robot communication system that is not easily detectable and increases stealth when necessary. The human-robot interaction system we propose involves hand signals. Hand gestures are a common modality of communication humans use with each other. Likewise, hand commands are used by the Marine Corps to convey information to each other without speaking. We analyze common Marine gestures so that similar commands can be used to direct a robot out in the field. In this paper, we have selected important hand or body gestures used by the Marine Corps. We then identify distinguishable features for the different gestures. This includes position of joint variables as well as velocity and acceleration terms. Once ideal models of the gestures are designed, experimental data is gathered. Presently, we are comparing two different machine learning methods that can be used to identify a specific gesture. The two methods we are comparing are Bayesian networks and neural networks. This paper provides the background and structure of our experiments. Then, both models are discussed and experimental results are included. Finally, we make a comparison of the effectiveness of the two methods of interest.
Keywords :
belief networks; control engineering computing; gesture recognition; human-robot interaction; learning (artificial intelligence); military systems; mobile robots; neural nets; Bayesian networks; Marine Corps; body gestures; common marine gesture recognition; hand gestures; human-robot communication system; human-robot interaction; machine learning methods; neural networks; Acceleration; Bayesian methods; Communication systems; Human computer interaction; Human robot interaction; Learning systems; Neural networks; Reconnaissance; Systems engineering and theory; Weapons; comparison study; gesture recognition; human-robot interaction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Theory (SSST), 2010 42nd Southeastern Symposium on
Conference_Location :
Tyler, TX
ISSN :
0094-2898
Print_ISBN :
978-1-4244-5690-1
Type :
conf
DOI :
10.1109/SSST.2010.5442835
Filename :
5442835
Link To Document :
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