Browse > Article
http://dx.doi.org/10.5909/JBE.2020.25.5.734

Development of a New Pedestrian Avoidance Algorithm considering a Social Distance for Social Robots  

Yoo, Jooyoung (Data Technology, Dept. of Software Convergence, College of ICT Convergence, Myongji University)
Kim, Daewon (Data Technology, Dept. of Software Convergence, College of ICT Convergence, Myongji University)
Publication Information
Journal of Broadcast Engineering / v.25, no.5, 2020 , pp. 734-741 More about this Journal
Abstract
This article proposes a new pedestrian avoidance algorithm for social robots that coexist and communicate with humans and do not induce stress caused by invasion of psychological safety distance(Social Distance). To redefine the pedestrian model, pedestrians are clustered according to the pedestrian's gait characteristics(straightness, speed) and a social distance is defined for each pedestrian cluster. After modeling pedestrians(obstacles) with the social distances, integrated navigation algorithm is completed by applying the newly defined pedestrian model to commercial obstacle avoidance and path planning algorithms. To show the effectiveness of the proposed algorithm, two commercial obstacle avoidance & path planning algorithms(the Dynamic Window Approach (DWA) algorithm and the Timed Elastic Bands (TEB) algorithm) are used. Four cases were experimented in applying and non-applying the new pedestrian model, respectively. Simulation results show that the proposed algorithm can significantly reduce the stress index of pedestrians without loss of traveling time.
Keywords
Social Robot; Proxemics; Social Distance; HRI; Pedestrian Modeling;
Citations & Related Records
연도 인용수 순위
  • Reference
1 C. L. Breazeal, Designing sociable robots, A Bradford book, pp.1-60, 2002.
2 P. Fiorini, and Z. Shiller, "Motion planning in dynamic environments using velocity obstacles" The International Journal of Robotics Research , Vol.17, No.7, pp.760-772, 1998, DOI: 10.1177/027836499801700706   DOI
3 A, Bandura, "The self system in reciprocal determinism", American psychologist, Vol.33, No.4, pp.344-358, 1978, DOI: 10.1037/0003-066X.33.4.344   DOI
4 E. T. Hall, R. L. Birdwhistell, B. Bock, P. Bohannan, A. R. Diebold Jr, M. Durbin, ... and W. La Barre, "Proxemics [and comments and replies]", Current anthropology, Vol.9, No.2/3, pp.83-108, 1968.   DOI
5 E. T. Hall, The hidden dimension, Garden City, NY: Doubleday, Vol. 609, pp.121-129, 1966.
6 D. P. Kennedy, J. Gläscher, J. M. Tyszka, and R. Adolphs, "Personal space regulation by the human amygdala", Nature neuroscience, Vol.12, No.10, pp.1226-1227, 2009, DOI: 10.1038/nn.2381.   DOI
7 L. Takayama, and C. Pantofaru. "Influences on proxemic behaviors in human-robot interaction." 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.5495-5502, 2009, DOI: 10.1109/IROS.2009.5354145.
8 G.J. Stephen, S. Kim, M. C. Lin, and D. Manocha. "Simulating heterogeneous crowd behaviors using personality trait theory." Eurographics/ ACM SIGGRAPH Symposium on Computer Animation, pp.43-52, 2011, DOI: 10.1145/2019406.2019413.
9 A. Sorokowska, P. Sorokowski, P.Hilpert, K. Cantarero, T. Frackowiak, ... and S. Blumen, "Preferred interpersonal distances: a global comparison" Journal of Cross-Cultural Psychology, Vol.48, No.4, pp.577-592, 2017, DOI: 10.1177/0022022117698039.   DOI
10 G.G.Berntson, A. Bechara, H. Damasio, D.Tranel, and J.T. Cacioppo "Amygdala contribution to selective dimensions of emotion" Social cognitive and affective neuroscience, Vol.2, No.2, pp.123-129, 2007, DOI: 10.1093/scan/nsm008.   DOI
11 D. Fox, W. Burgard, and S. Thrun. "The dynamic window approach to collision avoidance" IEEE Robotics & Automation Magazine Vol.4, No.1, pp.23-33, 1997, DOI: 10.1109/100.580977   DOI
12 C. Rosmann, W. Feiten, T. Woesch, F. Hoffmann, and T.Bertram. "Tra- jectory modification considering dynamic constraints of autonomous robots", ROBOTIK 2012; 7th German Conference on Robotics, Munich, Germany, pp.1-6, 2012.
13 H. J. Eysenck, "Crime and personality", Medico-Legal Journal Vol.47, No.1 : pp.18-32, 1979, DOI: 10.1177/002581727904700104.   DOI
14 S. David, "Web-scale k-means clustering", Proceedings of the 19th international conference on World wide web, pp.1177-1178, 2010.
15 S. Ren, K. He, R. Girshick, J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks" Advances in neural information processing systems. 2015, arXiv:1506.01497.
16 OXFORD TOWN CENTRE, https://megapixels.cc/datasets/oxford_town_centre/ (accessed Jan. 12, 2020).
17 D. Elan, "Homography estimation" Master's Thesis of Vancouver: Univerzita Britske Kolumbie, 2009.