Human inspired effort distribution during collision avoidance in human-robot motion

José Grimaldo da Silva Filho; Anne-Hélène Olivier; Armel Crétual; Julien Pettré; Thierry Fraichard

Human inspired effort distribution during collision avoidance in human-robot motion

José Grimaldo da Silva Filho ¹ Anne-Hélène Olivier ^{2, 3} Armel Crétual ^{2, 3} Julien Pettré ⁴ Thierry Fraichard ¹

1 PERVASIVE - Interaction située avec les objets et environnements intelligents

Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, UGA - Université Grenoble Alpes

2 MIMETIC - Analysis-Synthesis Approach for Virtual Human Simulation

UR2 - Université de Rennes 2, Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS

3 M2S - Laboratoire Mouvement Sport Santé

4 RAINBOW - Sensor-based and interactive robotics

Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE

Abstract : Recent works in the area of human robot motion showed that behaving in a human-like manner allows a robot to reduce global cognitive effort for people in the environment. Given that collision avoidance situations between people are solved cooperatively, this work models the manner in which this cooperation is done so that a robot can replicate their behavior. To that end, hundreds of situations where two walkers have crossing trajectories were analyzed. Based on these human trajectories involving a collision avoidance task, we determined how total effort is shared between each walker depending on several factors of the interaction such as crossing angle, time to collision and speed. To validate our approach, a proof of concept is integrated into ROS with Reciprocal Velocity Objects (RVO) in order to distribute collision avoidance effort in a human-like way.

Document type :

Conference papers

Domain :

Computer Science [cs] / Robotics [cs.RO]

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https://hal.inria.fr/hal-01831757
Contributor : Thierry Fraichard <>
Submitted on : Friday, July 6, 2018 - 11:00:51 AM
Last modification on : Thursday, October 24, 2019 - 10:35:59 AM
Long-term archiving on : Tuesday, October 2, 2018 - 5:32:39 AM

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