Long-range navigation in complex and dynamic environments with Full-Stack S-DOVS

Martinez-Baselga, Diego (Universidad de Zaragoza) ; Riazuelo, Luis (Universidad de Zaragoza) ; Montano, Luis (Universidad de Zaragoza)

Resumen: Robotic autonomous navigation in dynamic environments is a complex problem, as traditional planners may fail to take dynamic obstacles and their variables into account. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner has been proposed as a solution to navigate in such scenarios. However, it has a number of limitations, such as inability to reach a goal in a large known map, avoid convex objects, or handle trap situations. In this article, we present a modified version of the S-DOVS planner that is integrated into a full navigation stack, which includes a localization system, obstacle tracker, and novel waypoint generator. The complete system takes into account robot kinodynamic constraints and is capable of navigating through large scenarios with known map information in the presence of dynamic obstacles. Extensive simulation and ground robot experiments demonstrate the effectiveness of our system even in environments with dynamic obstacles and replanning requirements, and show that our waypoint generator outperforms other approaches in terms of success rate and time to reach the goal when combined with the S-DOVS planner. Overall, our work represents a step forward in the development of robust and reliable autonomous navigation systems for real-world scenarios.

Idioma: Inglés

DOI: 10.3390/app13158925

Año: 2023

Publicado en: Applied Sciences (Switzerland) 13, 15 (2023), 8925 [15 pp.]
ISSN: 2076-3417

Factor impacto JCR: 2.5 (2023)

Categ. JCR: ENGINEERING, MULTIDISCIPLINARY rank: 44 / 181 = 0.243 (2023) - Q1 - T1
Categ. JCR: PHYSICS, APPLIED rank: 87 / 179 = 0.486 (2023) - Q2 - T2
Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 115 / 231 = 0.498 (2023) - Q2 - T2
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 258 / 439 = 0.588 (2023) - Q3 - T2

Factor impacto CITESCORE: 5.3 - Engineering (all) (Q1) - Computer Science Applications (Q2) - Materials Science (all) (Q2) - Fluid Flow and Transfer Processes (Q2) - Instrumentation (Q2) - Process Chemistry and Technology (Q3)

Factor impacto SCIMAGO: 0.508 - Engineering (miscellaneous) (Q2) - Fluid Flow and Transfer Processes (Q2) - Materials Science (miscellaneous) (Q2) - Instrumentation (Q2) - Process Chemistry and Technology (Q3) - Computer Science Applications (Q3)

Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/PID2019-105390RB-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)