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. 10508–10518 (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. 86–99 (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. 1–18 (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, 1195–1199 (SAGE Publications Sage CA: Los Angeles, CA, 2021).
Hentout, A., Aouache, M., Maoudj, A. & Akli, I. Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017. Advanced Robotics. 33, 764–799 (2019).
Google Scholar
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). 83–88 (IEEE, 2012).
Fernando, T., Denman, S., Sridharan, S. & Fookes, C. Soft+ hardwired attention: An lstm framework for human trajectory prediction and abnormal event detection. Neural networks. 108, 466–478 (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 Scholar
Chen, Z. et al. Autonomous social distancing in urban environments using a quadruped robot. IEEE Access. 9, 8392–8403 (2021).
Google Scholar
Di Lallo, A. et al. Medical robots for infectious diseases: Lessons and challenges from the covid-19 pandemic. IEEE Robotics & Automation Magazine 28, 18–27 (2021).
Google Scholar
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, 185–204 (1996).
Google Scholar
Pellegrini, S., Ess, A., Schindler, K. & Van Gool, L. You’ll never walk alone: Modeling social behavior for multi-target tracking. In 2009 IEEE 12th International Conference on Computer Vision. 261–268 (IEEE, 2009).
Lerner, A., Chrysanthou, Y. & Lischinski, D. Crowds by example. In Computer graphics forum. vol. 26, 655–664 (Wiley Online Library, 2007).
Majecka, B. Statistical models of pedestrian behaviour in the forum. Master’s thesis, School of Informatics, University of Edinburgh. (2009).
Benfold, B. & Reid, I. Stable multi-target tracking in real-time surveillance video. In CVPR 2011. 3457–3464 (IEEE, 2011).
Schneider, N. & Gavrila, D. M. Pedestrian path prediction with recursive bayesian filters: A comparative study. In German Conference on Pattern Recognition. 174–183 (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. 2871–2878 (IEEE, 2012).
Brščić, 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, 522–534 (2013).
Google Scholar
Kratzer, P. et al. Mogaze: A dataset of full-body motions that includes workspace geometry and eye-gaze. IEEE Robotics and Automation Letters. 6, 367–373 (2020).
Google Scholar
CMU. CMU graphics lab motion capture database. http://mocap.cs.cmu.edu (2003).
Ionescu, C., Papava, D., Olaru, V. & Sminchisescu, C. Human3. 6 m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence. 36, 1325–1339 (2013).
Google Scholar
Mandery, C., Terlemez, Ö., Do, M., Vahrenkamp, N. & Asfour, T. The kit whole-body human motion database. In 2015 International Conference on Advanced Robotics (ICAR). 329–336 (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, 228–237 (2014).
Google Scholar
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). 1–8 (IEEE, 2015).
Gadaleta, M. & Rossi, M. Idnet: Smartphone-based gait recognition with convolutional neural networks. Pattern Recognition. 74, 25–37 (2018).
Google Scholar
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, 624–635 (2022).
Google Scholar
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, 1691–1695 (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, 34–46 (2007).
Google Scholar
Fox, D., Burgard, W., Dellaert, F. & Thrun, S. Monte carlo localization: Efficient position estimation for mobile ro-bots. AAAI/IAAI. 343–349 (1999).
Dijkstra, E. W. A note on two problems in connexion with graphs. Numerische mathematik. 1, 269–271 (1959).
Google Scholar
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, 1723–1727 (2020).
Google Scholar
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, 110–116 (2021).
Google Scholar
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. Thör: Human-robot navigation data collection and accurate motion trajectories dataset. IEEE Robotics and Automation Letters. 5, 676–682 (2020).
Google Scholar
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. 779–788 (2016).
Thrun, S. Probabilistic robotics. Communications of the ACM. 45, 52–57 (2002).
Google Scholar
Obo, T. & Yasuda, E. Intelligent fuzzy controller for human-aware robot navigation. In 2018 12th France-Japan and 10th Europe-Asia Congress on Mechatronics. 392–397 (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). 6015–6022 (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. 549–565 (Springer, 2016).
Oh, S. et al. A large-scale benchmark dataset for event recognition in surveillance video. In CVPR 2011. 3153–3160 (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. 3354–3361 (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). 864–871 (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).