Sökning: onr:"swepub:oai:DiVA.org:oru-98588" >
Detecting Groups an...
Detecting Groups and Estimating F-Formations for Social Human-Robot Interactions
-
- Krishna Pathi, Sai, 1986- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Center for Applied Autonomous Sensor Systems (AASS)
-
- Kiselev, Andrey, 1982- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Center for Applied Autonomous Sensor Systems (AASS)
-
- Loutfi, Amy, 1978- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Center for Applied Autonomous Sensor Systems (AASS)
-
(creator_code:org_t)
- 2022-02-23
- 2022
- Engelska.
-
Ingår i: Multimodal Technologies and Interaction. - : MDPI. - 2414-4088. ; 6:3
- Relaterad länk:
-
https://doi.org/10.3...
-
visa fler...
-
https://www.mdpi.com...
-
https://urn.kb.se/re...
-
https://doi.org/10.3...
-
visa färre...
Abstract
Ämnesord
Stäng
- The ability of a robot to detect and join groups of people is of increasing importance in social contexts, and for the collaboration between teams of humans and robots. In this paper, we propose a framework, autonomous group interactions for robots (AGIR), that endows a robot with the ability to detect such groups while following the principles of F-formations. Using on-board sensors, this method accounts for a wide spectrum of different robot systems, ranging from autonomous service robots to telepresence robots. The presented framework detects individuals, estimates their position and orientation, detects groups, determines their F-formations, and is able to suggest a position for the robot to enter the social group. For evaluation, two simulation scenes were developed based on the standard real-world datasets. The 1st scene is built with 20 virtual agents (VAs) interacting in 7 different groups of varying sizes and 3 different formations. The 2nd scene is built with 36 VAs, positioned in 13 different groups of varying sizes and 6 different formations. A model of a Pepper robot is used in both simulated scenes in randomly generated different positions. The ability for the robot to estimate orientation, detect groups, and estimate F-formations at various locations is used to determine the validation of the approaches. The obtained results show a high accuracy within each of the simulated scenarios and demonstrates that the framework is able to work from an egocentric view with a robot in real time.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- human-robot interaction
- social robotics
- F-formations
- group interactions
- Kendon formations
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas