Primate-inspired Autonomous Navigation Using Mental Rotation and Advice-Giving

Velayudhan, Lakshmi; Arkin, Ronald C.

Title:

Primate-inspired Autonomous Navigation Using Mental Rotation and Advice-Giving

dc.contributor.author	Velayudhan, Lakshmi
dc.contributor.author	Arkin, Ronald C.
dc.contributor.corporatename	Georgia Institute of Technology. College of Computing	en_US
dc.contributor.corporatename	Georgia Institute of Technology. School of Interactive Computing	en_US
dc.contributor.corporatename	Georgia Institute of Technology. Mobile Robot Laboratory	en_US
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Robotics and Intelligent Machines	en_US
dc.date.accessioned	2017-05-05T14:29:02Z
dc.date.available	2017-05-05T14:29:02Z
dc.date.issued	2015-09
dc.description	© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.	en_US
dc.description	DOI: 10.1109/MFI.2015.7295817	en_US
dc.description.abstract	The cognitive process that enables many primate species to efficiently traverse their environment has been a subject of numerous studies. Mental rotation is hypothesized to be one such process. The evolutionary causes for dominance in primates of mental rotation over its counterpart, rotational invariance, is still not conclusively understood. Advice-giving offers a possible explanation for this dominance in more evolved primate species such as humans. This project aims at exploring the relationship between advice-giving and mental rotation by designing a system that combines the two processes in order to achieve successful navigation to a goal location. Two approaches to visual advice-giving were explored namely, segment based and object based advice-giving. The results obtained upon execution of the navigation algorithm on a Pioneer 2-DX robotic platform offers evidence regarding a linkage between advice-giving and mental rotation. An overall navigational accuracy of 90.9% and 71.43% were obtained respectively for the segment-based and object-based methods. These results also indicate how the two processes can function together in order to accomplish a navigational task in the absence of any external aid, as is the case with primates.	en_US
dc.identifier.citation	Velayudhan, L. & Arkin, R.C. (2015). Primate-Inspired Autonomous Navigation Using Mental Rotation and Advice-Giving. 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 252-258, San Diego, CA.	en_US
dc.identifier.doi	10.1109/MFI.2015.7295817	en_US
dc.identifier.isbn	978-1-5090-0307-5 (Print)
dc.identifier.isbn	978-1-4799-7772-7 (Online)
dc.identifier.uri	http://hdl.handle.net/1853/56677
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.publisher.original	Institute of Electrical and Electronics Engineers
dc.relation.ispartofseries	Mobile Robot Laboratory	en_US
dc.subject	Advice-giving	en_US
dc.subject	Dominance	en_US
dc.subject	Humans	en_US
dc.subject	Mental rotation	en_US
dc.subject	Primate species	en_US
dc.subject	Rotational invariance	en_US
dc.title	Primate-inspired Autonomous Navigation Using Mental Rotation and Advice-Giving	en_US
dc.type	Text
dc.type.genre	Proceedings
dspace.entity.type	Publication
local.contributor.author	Arkin, Ronald C.
local.contributor.corporatename	College of Computing
local.contributor.corporatename	Mobile Robot Laboratory
local.contributor.corporatename	Institute for Robotics and Intelligent Machines (IRIM)
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