Modeling of Personal/Group Dynamic Comfort Space Based on Asymmetric Gaussian Function
ZHOU Lei1,2, ZHANG Sen1,3, ZHAO Yingli1,2, HU Zhengxi1,2, LIU Jingtai1,2
1. Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin 300350, China; 2. Tianjin Key Laboratory of Intelligent Robotics, Tianjin 300350, China; 3. School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China
Abstract:A dynamic comfort space model based on asymmetric Gaussian function is designed. Firstly, the shapes of comfort spaces at different speeds are determined according to the motion state information of a human. Then a scalable fuzzy reasoning framework is proposed to define the size of personalized comfort space by considering the differences of individual social attributes such as gender and age. Based on the comfort space model of an individual person, the concept of common concern area is proposed, and the case of human group is analyzed through the minimum covering circle algorithm, so as to construct the comfort space for the group. Qualitative as well as quantitative experiments are carried out to analyze and evaluate the effectiveness of the proposed dynamic comfort space model, and the rationality of the model is verified.
[1] 韩建达,方勇纯,赵新,等.机器人的智能发育[J].人工智能, 2018(3): 28-35. Han J D, Fang Y C, Zhao X, et al. Intelligent development of robots[J]. Artificial Intelligence, 2018(3): 28-35. [2] 何玉庆,赵忆文,韩建达,等.与人共融——机器人技术发展的新趋势[J]. 机器人产业, 2015(5): 74-80. He Y Q, Zhao Y W, Han J D, et al. Co-existence with humans – The new trend of robot technology development[J]. Robot Industry, 2015(5): 74-80. [3] MuMMER[EB/OL]. [2020-07-01]. http://www.mummerproject.eu/. [4] Rios-Martinez J, Spalanzani A, Laugier C. From proxemics theory to socially-aware navigation: A survey[J]. International Journal of Social Robotics, 2015, 7(2): 137-153. [5] Helbing D, Molnar P. Social force model for pedestrian dynamics[J]. Physical Review E, 1995, 51(5): 4282-4286. [6] Hayduk L A. The shape of personal space: An experimental investigation[J]. Canadian Journal of Behavioural Science, 1981, 13(1): 87-93. [7] Lam C P, Chou C T, Chang C F, et al. Human-centered robot navigation – Toward a harmoniously coexisting multi-human and multi-robot environment[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2010: 1813-1818. [8] Huang K-C, Li J-Y, Fu L-C. Human-oriented navigation for service providing in home environment[C]//Proceedings of SICE Annual Conference. Piscataway, USA: IEEE, 2010: 1892-1897. [9] Higuchi T, Imanaka K, Patla A E. Action-oriented representation of peripersonal and extrapersonal space: Insights from manual and locomotor actions[J]. Japanese Psychological Research, 2006, 48(3): 126-140. [10] Svenstrup M, Bak T, Andersen H J. Trajectory planning for robots in dynamic human environments[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2010: 4293-4298. [11] Patompak P, Jeong S, Nilkhamhang I, et al. Learning social relations for culture aware interaction[C]//14th International Conference on Ubiquitous Robots and Ambient Intelligence. Piscataway, USA: IEEE, 2017: 26-31. [12] Patompak P, Jeong S, Nilkhamhang I, et al. Learning proxemics for personalized human-robot social interaction[J]. International Journal of Social Robotics, 2020, 12: 267-280. [13] Papadakis P, Spalanzani A, Laugier C. Social mapping of human-populated environments by implicit function learning [C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2013: 1701-1706. [14] Papadakis P, Rives P, Spalanzani A. Adaptive spacing in humanrobot interactions[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2014:2627-2632. [15] Aiello J R, Aiello T D C. The development of personal space: Proxemic behavior of children 6 through 16[J]. Human Ecology, 1974, 2(3): 177-189. [16] 张森,刘景泰. 基于多维度服务情景的人的舒适需求建模[J].机器人, 2019, 41(4): 493-506. Zhang S, Liu J T. Modeling of human’s comfort needs based on multi-dimensional service situations[J]. Robot, 2019, 41(4): 493-506. [17] Gérin-Lajoie M, Richards C L, McFadyen B J. The negotiation of stationary and moving obstructions during walking: Anticipatory locomotor adaptations and preservation of personal space[J]. Motor Control, 2005, 9(3): 242-269. [18] Vega A, Manso L J, Macharet D G, et al. Socially aware robot navigation system in human-populated and interactive environments based on an adaptive spatial density function and space affordances[J]. Pattern Recognition Letters, 2019, 118: 72-84. [19] Alessandra T. Charisma: Seven keys to developing the magnetism that leads to success[M]. USA: Business Plus, 2000. [20] Guy S J, Kim S, Lin M C, et al. Simulating heterogeneous crowd behaviors using personality trait theory[C]//ACM SIGGRAPH/Eurographics Symposium on Computer Animation. New York, USA: ACM, 2011: 43-52. [21] Bera A, Randhavane T, Prinja R, et al. SocioSense: Robot navigation amongst pedestrians with social and psychological constraints[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2017: 7018- 7025. [22] Moussaid M, Perozo N, Garnier S, et al. The walking behaviour of pedestrian social groups and its impact on crowd dynamics[J]. PLoS ONE, 2010, 5(3). DOI: 10.1371/journal.pone. 0010047. [23] Kendon A. Spacing and orientation in co-present interaction [M]//Development of Multimodal Interfaces: Active Listening and Synchrony. Berlin, Germany: Springer, 2010: 1-15. [24] Efran M G, Cheyne J A. Shared space: The co-operative control of spatial areas by two interacting individuals[J]. Canadian Journal of Behavioural Science, 1973, 5(3): 201-210. [25] Setti F, Russell C, Bassetti C, et al. F-formation detection: Individuating free-standing conversational groups in images[J]. PLoS ONE, 2015, 10(9). DOI: 10.1371/journal.pone.0139160. [26] Vega-Magro A, Manso L, Bustos P, et al. Socially acceptable robot navigation over groups of people[C]//IEEE International Symposium on Robot and Human Interactive Communication. Piscataway, USA: IEEE, 2017: 1182-1187. [27] Truong X T, Ngo T D. Dynamic social zone based mobile robot navigation for human comfortable safety in social environments[J]. International Journal of Social Robotics, 2016, 8(5): 663-684. [28] Chen Y F, Liu M, Everett M, et al. Decentralized noncommunicating multiagent collision avoidance with deep reinforcement learning[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2017: 285- 292. [29] Everett M, Chen Y F, How J P. Motion planning among dynamic, decision-making agents with deep reinforcement learning [C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2018: 3052-3059. [30] Chen C, Liu Y, Kreiss S, et al. Crowd-robot interaction: Crowdaware robot navigation with attention-based deep reinforcement learning[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2019: 6015-6022.