Visual feedback for humans about robots' perception in collaborative environments

During the last years, major advances on artificial intelligence have successfully allowed robots to perceive their environment, which not only includes static but also dynamic objects such as humans. Indeed, robotic perception is a fundamental feature to achieve safe robots' autonomy in human-...

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Detalhes bibliográficos
Autores: Gassó Loncan, Juan Cruz, Olivares Alarcos, Alberto|||0000-0002-7733-7715, Alenyà Ribas, Guillem|||0000-0002-6018-154X
Tipo de documento: relatório científico
Data de publicação:2020
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositório:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglês
OAI Identifier:oai:upcommons.upc.edu:2117/328758
Acesso em linha:https://hdl.handle.net/2117/328758
Access Level:Acceso aberto
Palavra-chave:Safety
Collaborative robots
Human pose estimation
Human-robot closeness
Classificació INSPEC::Automation::Robots
Àrees temàtiques de la UPC::Informàtica::Robòtica
Descrição
Resumo:During the last years, major advances on artificial intelligence have successfully allowed robots to perceive their environment, which not only includes static but also dynamic objects such as humans. Indeed, robotic perception is a fundamental feature to achieve safe robots' autonomy in human-robot collaboration. However, in order to have true collaboration, both robots and humans should perceive each other’s intentions and interpret which actions they are performing. In this work, we developed a visual representation tool that illustrates the robot's perception of the space that is shared with a person. Specifically, we adapted an existent system to estimate the human pose, and we created a visualisation tool to represent the robot's perception about the human-robot closeness. We also performed a first evaluation of the system working in realistic conditions using the Tiago robot and a person as a test subject. This work is a first step towards allowing humans to have a better understanding about robots' perception in collaborative scenarios.