RT Journal Article
T1 Quad-RRT: a real-time GPU-based global path planner in large-scale real environments
A1 Hidalgo-Paniagua, Alejandro
A1 Bandera-Rubio, Juan Pedro
A1 Ruiz-de-Quintanilla, Manuel
A1 Bandera Rubio, Antonio
K1 Telecomunicación
K1 Tecnología
K1 Electrónica
AB During the last decade, sampling based methods for motion and path planning have gained more interest. Specifically, in the field of robotics, approaches based on the Rapidly-exploring Random Tree (RRT) algorithm have become the customary technique for solving the single-query motion planning problem. However, dynamic large maps still represent a challenging scenario for these methods to produce fast enough results. Taking advantage of an NVidia CUDA-enabled Graphic Processing Unit (GPU), we present quad-RRT, an extension of the bi-directional strategy to speed up the RRT when dealing with large-scale, bidimensional (2D) maps. Designed for modern GPUs, quad-RRT computes four trees instead of the two ones built by the bidirectional approaches. This modification aims balancing the direct searching ability of these methods with the parallel exploration of those parts of the map at both sides of the path joining the initial and goal poses. Experimental results demonstrate that the proposed algorithm provides a significant speedup dealing with large-scale maps densely populated by obstacles, when compared to other implementations of the RRT. Hence, the algorithm can have a high impact in the field of inspection path planning for distributed infrastructure. It is also a promising approach to allow new generation robots, designed to work in unconstrained environments, dynamically plan large-scale paths.
PB Elsevier
YR 2018
FD 2018-06-01
LK https://hdl.handle.net/10630/34023
UL https://hdl.handle.net/10630/34023
LA eng
NO Dronecaptor (ITC-20151141), funded by the Spanish Ministerio de Economía y Competitividad and FEDER funds.EU projects Surveiron (DRS-17-2014-1 and DRS-17-2014-2).TIN2015- 65686-C5, funded by the Spanish Ministerio de Economía y Competitividad.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 12 abr 2026