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ISPRS Int. J. Geo-Inf. 2013, 2(3), 729-748; doi:10.3390/ijgi2030729

HCTNav: A Path Planning Algorithm for Low-Cost Autonomous Robot Navigation in Indoor Environments

* ,
Human Computer Technology Laboratory, EPS, Universidad Autónoma de Madrid. Francisco Tomás y Valiente 11, E-28049 Madrid, Spain
* Author to whom correspondence should be addressed.
Received: 25 June 2013 / Revised: 29 July 2013 / Accepted: 31 July 2013 / Published: 9 August 2013
(This article belongs to the Special Issue Indoor Positioning and Indoor Navigation)
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Low-cost robots are characterized by low computational resources and limited energy supply. Path planning algorithms aim to find the optimal path between two points so the robot consumes as little energy as possible. However, these algorithms were not developed considering computational limitations (i.e., processing and memory capacity). This paper presents the HCTNav path-planning algorithm (HCTLab research group’s navigation algorithm). This algorithm was designed to be run in low-cost robots for indoor navigation. The results of the comparison between HCTNav and the Dijkstra’s algorithms show that HCTNav’s memory peak is nine times lower than Dijkstra’s in maps with more than 150,000 cells.
Keywords: low-cost indoor navigation; path planning algorithm; autonomous robot low-cost indoor navigation; path planning algorithm; autonomous robot
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Pala, M.; Eraghi, N.O.; López-Colino, F.; Sanchez, A.; de Castro, A.; Garrido, J. HCTNav: A Path Planning Algorithm for Low-Cost Autonomous Robot Navigation in Indoor Environments. ISPRS Int. J. Geo-Inf. 2013, 2, 729-748.

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