Context-Specific Navigation for ‘Gentle’ Approach Towards Objects Based on LiDAR and URF Sensors

[EN] Navigation skills are essential for most social and service robotics applications. The robots that are currently in practical use in various complex human environments are generally very limited in their autonomous navigational abilities; while they can reach the proximity of objects, they are...

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Detalles Bibliográficos
Autores: Álvarez Aparicio, Claudia, Korcsok, Beáta, Campazas Vega, Adrián, Miklósi, Ádám, Matellán Olivera, Vicente, Ferdinandy, Bence
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/25869
Acceso en línea:https://hdl.handle.net/10612/25869
Access Level:acceso abierto
Palabra clave:Informática
context specific
contextless
LiDAR
navigation
robotics
ROS
service robot
URF
Descripción
Sumario:[EN] Navigation skills are essential for most social and service robotics applications. The robots that are currently in practical use in various complex human environments are generally very limited in their autonomous navigational abilities; while they can reach the proximity of objects, they are not efficient in approaching them closely. The new solution described in this paper presents a system to solve this context-specific navigation problem. The system handles locations with differing contexts based on the use of LiDAR and URF sensors, allowing for the avoidance of people and obstacles with a wide margin, as well as for approaching target objects closely. To quantify the efficiency of our solution we compared it with the ROS contextless standard navigation (move_base) in two different robot platforms and environments, both with real-world tests and simulations. The metrics selected were (1) the time the robot needs to reach an object, (2) the Euclidean distance, and (3) the orientation between the final position of the robot and the defined goal position. We show that our context-specific solution is superior to the standard navigation both in time and Euclidean distance.