Liang, J., Jiang, L., Murphy, K., Yu, T. & Hauptmann, A. The garden of forking paths: Towards multi-future trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10508ā10518 (2020).
Tang, Y. C. & Salakhutdinov, R. Multiple futures prediction. Advances in Neural Information Processing Systems 32 (2019).
Chai, Y., Sapp, B., Bansal, M. & Anguelov, D. Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. In Conference on Robot Learning. 86ā99 (2020).
Smith, T., Chen, Y., Hewitt, N., Hu, B. & Gu, Y. Socially aware robot obstacle avoidance considering human intention and preferences. International Journal of Social Robotics. 1ā18 (2021).
Chen, Y., Smith, T., Hewitt, N., Gu, Y. & Hu, B. Effects of human personal space on the robot obstacle avoidance behavior: A human-in-the-loop assessment. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 65, 1195ā1199 (SAGE Publications Sage CA: Los Angeles, CA, 2021).
Hentout, A., Aouache, M., Maoudj, A. & Akli, I. Humanārobot interaction in industrial collaborative robotics: a literature review of the decade 2008ā2017. Advanced Robotics. 33, 764ā799 (2019).
Google ScholarĀ
Kruse, T., Basili, P., Glasauer, S. & Kirsch, A. Legible robot navigation in the proximity of moving humans. In 2012 IEEE workshop on advanced robotics and its social impacts (ARSO). 83ā88 (IEEE, 2012).
Fernando, T., Denman, S., Sridharan, S. & Fookes, C. Soft+āhardwired attention: An lstm framework for human trajectory prediction and abnormal event detection. Neural networks. 108, 466ā478 (2018).
Kosaraju, V. et al. Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. Advances in Neural Information Processing Systems 32 (2019).
Helbing, D. & Molnar, P. Social force model for pedestrian dynamics. Physical review E. 51, 4282 (1995).
Google ScholarĀ
Chen, Z. et al. Autonomous social distancing in urban environments using a quadruped robot. IEEE Access. 9, 8392ā8403 (2021).
Google ScholarĀ
Di Lallo, A. et al. Medical robots for infectious diseases: Lessons and challenges from the covid-19 pandemic. IEEE Robotics & Automation Magazine 28, 18ā27 (2021).
Google ScholarĀ
Chen, Y., Luo, Y., & Hu, B. Towards Next Generation Cleaning Tools: Factors Affecting Cleaning Robot Usage and Proxemic Behaviors Design. Frontiers in Electronics 14 (2022).
Chau, P. Y. An empirical assessment of a modified technology acceptance model. Journal of management information systems. 13, 185ā204 (1996).
Google ScholarĀ
Pellegrini, S., Ess, A., Schindler, K. & Van Gool, L. Youāll never walk alone: Modeling social behavior for multi-target tracking. In 2009 IEEE 12th International Conference on Computer Vision. 261ā268 (IEEE, 2009).
Lerner, A., Chrysanthou, Y. & Lischinski, D. Crowds by example. In Computer graphics forum. vol. 26, 655ā664 (Wiley Online Library, 2007).
Majecka, B. Statistical models of pedestrian behaviour in the forum. Masterās thesis, School of Informatics, University of Edinburgh. (2009).
Benfold, B. & Reid, I. Stable multi-target tracking in real-time surveillance video. In CVPR 2011. 3457ā3464 (IEEE, 2011).
Schneider, N. & Gavrila, D. M. Pedestrian path prediction with recursive bayesian filters: A comparative study. In German Conference on Pattern Recognition. 174ā183 (Springer, 2013).
Martin-Martin, R. et al. Jrdb: A dataset and benchmark of egocentric robot visual perception of humans in built environments. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).
Zhou, B., Wang, X. & Tang, X. Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-agents. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2871ā2878 (IEEE, 2012).
BrÅ”ÄiÄ, D., Kanda, T., Ikeda, T. & Miyashita, T. Person tracking in large public spaces using 3-d range sensors. IEEE Transactions on Human-Machine Systems. 43, 522ā534 (2013).
Google ScholarĀ
Kratzer, P. et al. Mogaze: A dataset of full-body motions that includes workspace geometry and eye-gaze. IEEE Robotics and Automation Letters. 6, 367ā373 (2020).
Google ScholarĀ
CMU. CMU graphics lab motion capture database. http://mocap.cs.cmu.edu (2003).
Ionescu, C., Papava, D., Olaru, V. & Sminchisescu, C. Human3. 6ām: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence. 36, 1325ā1339 (2013).
Google ScholarĀ
Mandery, C., Terlemez, Ć., Do, M., Vahrenkamp, N. & Asfour, T. The kit whole-body human motion database. In 2015 International Conference on Advanced Robotics (ICAR). 329ā336 (IEEE, 2015).
Ngo, T. T., Makihara, Y., Nagahara, H., Mukaigawa, Y. & Yagi, Y. The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recognition 47, 228ā237 (2014).
Google ScholarĀ
Subramanian, R. et al. Orientation invariant gait matching algorithm based on the kabsch alignment. In IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015). 1ā8 (IEEE, 2015).
Gadaleta, M. & Rossi, M. Idnet: Smartphone-based gait recognition with convolutional neural networks. Pattern Recognition. 74, 25ā37 (2018).
Google ScholarĀ
Chen, Y., Yang, C., Gu, Y. & Hu, B. Influence of Mobile Robots on Human Safety Perception and System Productivity in Wholesale and Retail Trade Environments: A Pilot Study. IEEE Transactions on Human-Machine Systems 52, 624ā635 (2022).
Google ScholarĀ
Chen, Y. et al. Effects of autonomous mobile robots on human mental workload and system productivity in smart warehouses: A preliminary study. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 64, 1691ā1695 (SAGE Publications Sage CA: Los Angeles, CA, 2020).
Quigley, M. et al. ROS: an open-source Robot Operating System. ICRA workshop on open source software. 3, 5 (2009).
Grisetti, G., Stachniss, C. & Burgard, W. Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE transactions on Robotics. 23, 34ā46 (2007).
Google ScholarĀ
Fox, D., Burgard, W., Dellaert, F. & Thrun, S. Monte carlo localization: Efficient position estimation for mobile ro-bots. AAAI/IAAI. 343ā349 (1999).
Dijkstra, E. W. A note on two problems in connexion with graphs. Numerische mathematik. 1, 269ā271 (1959).
Google ScholarĀ
Gerkey, B. P. & Konolige, K. Planning and control in unstructured terrain. In ICRA Workshop on Path Planning on Costmaps. (2008).
Luo, Y., Zheng, H., Chen, Y., Giang, W. C. & Hu, B. Influences of smartphone operation on gait and posture during outdoor walking task. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, 1723ā1727 (2020).
Google ScholarĀ
Luo, Y., Lu, X., Ahrentzen, S. & Hu, B. Impact of destination-based visual cues on gait characteristics among adults over 75 years old: A pilot study. Gait & Posture 87, 110ā116 (2021).
Google ScholarĀ
Chen, Y. et al. Human Mobile Robot Interaction in the Retail Environment. Science Data Bank. https://doi.org/10.11922/sciencedb.01351 (2022).
Rudenko, A. et al. Thƶr: Human-robot navigation data collection and accurate motion trajectories dataset. IEEE Robotics and Automation Letters. 5, 676ā682 (2020).
Google ScholarĀ
Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 779ā788 (2016).
Thrun, S. Probabilistic robotics. Communications of the ACM. 45, 52ā57 (2002).
Google ScholarĀ
Obo, T. & Yasuda, E. Intelligent fuzzy controller for human-aware robot navigation. In 2018 12th France-Japan and 10th Europe-Asia Congress on Mechatronics. 392ā397 (IEEE, 2018).
Chen, C., Liu, Y., Kreiss, S. & Alahi, A. Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning. In 2019 International Conference on Robotics and Automation (ICRA). 6015ā6022 (IEEE, 2019).
Robicquet, A., Sadeghian, A., Alahi, A. & Savarese, S. Learning social etiquette: Human trajectory understanding in crowded scenes. In European conference on computer vision. 549ā565 (Springer, 2016).
Oh, S. et al. A large-scale benchmark dataset for event recognition in surveillance video. In CVPR 2011. 3153ā3160 (IEEE, 2011).
Geiger, A., Lenz, P. & Urtasun, R. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. 3354ā3361 (IEEE, 2012).
Yan, Z., Duckett, T. & Bellotto, N. Online learning for human classification in 3d lidar-based tracking. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 864ā871 (IEEE, 2017).
Dondrup, C., Bellotto, N., Jovan, F. & Hanheide, M. Real-time multisensor people tracking for human-robot spatial interaction In ICRA workshop on machine learning for social robotics. (2015).