Social-aware navigation in crowds with static and dynamic groups

Serious games with virtual characters heavily depend on character properties related to social interactions and awareness. This paper presents social-aware navigation for crowds in serious games. It enables a virtual character to join a group or navigate to a goal position through a crowd with static and dynamic group formations in a safe and socially acceptable way. The social-aware space is modeled in order to produce a speed map used by a fast marching method to find the fastest path to the goal position. We evaluate the method on a simulated scenario and public crowd datasets. Initial results show that social-aware navigation is capable of driving a virtual character to join a group or navigate to the goal in a realistic way while considering social norms and interactions.

[1]  Antonios Gasteratos,et al.  Recent trends in social aware robot navigation: A survey , 2017, Robotics Auton. Syst..

[2]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[3]  Xuan-Tung Truong,et al.  “To Approach Humans?”: A Unified Framework for Approaching Pose Prediction and Socially Aware Robot Navigation , 2018, IEEE Transactions on Cognitive and Developmental Systems.

[4]  Lorenza Manenti,et al.  Data Collection for Modeling and Simulation: Case Study at the University of Milan-Bicocca , 2012, ACRI.

[5]  Hannes Högni Vilhjálmsson,et al.  Study of Nine People in a Hallway: Some Simulation Challenges , 2018, IVA.

[6]  Francesco Setti,et al.  F-Formation Detection: Individuating Free-Standing Conversational Groups in Images , 2015, PloS one.

[7]  Francesco Solera,et al.  Socially Constrained Structural Learning for Groups Detection in Crowd , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Masayuki Nakajima,et al.  Personal Space Modeling for Human-Computer Interaction , 2009, ICEC.

[9]  Stefania Bandini,et al.  Towards an Integrated Approach to Crowd Analysis and Crowd Synthesis: a Case Study and First Results , 2013, Pattern Recognit. Lett..

[10]  E. Hall,et al.  The Hidden Dimension , 1970 .

[11]  Javier V. Gómez,et al.  Fast marching solution for the social path planning problem , 2014, ICRA.

[12]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[13]  Rachid Alami,et al.  A Human Aware Mobile Robot Motion Planner , 2007, IEEE Transactions on Robotics.

[14]  David Traum,et al.  Dynamic Movement and Positioning of Embodied Agents in Multiparty Conversations , 2007, ACL 2007.

[15]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[16]  Fangkai Yang,et al.  Expressive virtual characters for social demonstration games , 2017, 2017 9th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games).

[17]  Christian Laugier,et al.  Using social cues to estimate possible destinations when driving a robotic wheelchair , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  A. Kendon Conducting Interaction: Patterns of Behavior in Focused Encounters , 1990 .

[19]  Stéphane Sanchez,et al.  A Level of Interaction Framework for Exploratory Learning with Characters in Virtual Environments , 2010 .

[20]  Fangkai Yang,et al.  Investigating Social Distances between Humans, Virtual Humans and Virtual Robots in Mixed Reality , 2018, AAMAS.

[21]  Nadia Magnenat-Thalmann,et al.  Virtual Humans in Serious Games , 2009, 2009 International Conference on CyberWorlds.

[22]  Catherine Pelachaud,et al.  The Effects of Interpersonal Attitude of a Group of Agents on User’s Presence and Proxemics Behavior , 2016, TIIS.

[23]  Xiaogang Wang,et al.  Understanding pedestrian behaviors from stationary crowd groups , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Takayuki Kanda,et al.  A Robot that Approaches Pedestrians , 2013, IEEE Transactions on Robotics.

[25]  Subramanian Ramanathan,et al.  SALSA: A Novel Dataset for Multimodal Group Behavior Analysis , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Dani Lischinski,et al.  Crowds by Example , 2007, Comput. Graph. Forum.

[27]  Ming-Ching Chang,et al.  Probabilistic group-level motion analysis and scenario recognition , 2011, 2011 International Conference on Computer Vision.