Modeling a Social Placement Cost to Extend Navigation Among Movable Obstacles (NAMO) Algorithms

Benoit Renault (1, 2, 3) , Jacques Saraydaryan (1, 4, 3) , Olivier Simonin (1, 2, 3)

1 CITI - CITI Centre of Innovation in Telecommunications and Integration of services
2 INSA Lyon - Institut National des Sciences Appliquées de Lyon
3 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
4 CPE - Department of Computer Science [Lyon]

Current Navigation Among Movable Obstacles (NAMO) algorithms focus on finding a path for the robot that only optimizes the displacement cost of navigating and moving obstacles out of its way. However, in a human environment, this focus may lead the robot to leave the space in a socially inappropriate state that may hamper human activity (i.e. by blocking access to doors, corridors, rooms or objects of interest). In this paper, we tackle this problem of "Social Placement Choice" by building a social occupation costmap, built using only geometrical information. We present how existing NAMO algorithms can be extended by exploiting this new cost map. Then, we show the effectiveness of this approach with simulations, and provide additional evaluation criteria to assess the social acceptability of plans.

Mots clés: Navigation Among Movable Obstacles (NAMO); Socially- Aware Navigation (SAN); Path planning; Simulation

Domaines: Robotique [cs.RO]; Intelligence artificielle [cs.AI]

Benoit Renault, Jacques Saraydaryan, Olivier Simonin. Modeling a Social Placement Cost to Extend Navigation Among Movable Obstacles (NAMO) Algorithms. IROS 2020 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2020, Las Vegas, United States. pp.11345-11351, ⟨10.1109/IROS45743.2020.9340892⟩. ⟨hal-02912925⟩