ABSTRACT
A target tracking approach is proposed for mobile robots in this paper to address the human-robot coexistence and collaboration problem. The improved social force model (SFM) is applied to improve the tracking performance of the robot in crowded environments. When the robot approaches the pedestrians or obstacles, the tracking strategy is adaptively adjusted to avoid collision. The inverse reinforcement learning (IRL) is used to learn the parameters of the improved SFM, where the training data for the IRL is collected in real-world scenes. An effective criterion is designed to evaluate the tracking performance, which fully considers the relationship between the robot and surrounding environments. The experimental results demonstrate the effectiveness of the proposed target tracking method.
- J. Mainprice, R. Hayne, and D. Berenson. 2016. Goal Set Inverse Optimal Control and Iterative Replanning for Predicting Human Reaching Motions in Shared Workspaces. IEEE Transactions on Robotics 32, 4 (2016), 897–908. https://doi.org/10.1109/TRO.2016.2581216Google ScholarDigital Library
- M. Ewerton, G. Neumann, R. Lioutikov, H. Ben Amor, J. Peters, and G. Maeda. 2015. Learning multiple collaborative tasks with a mixture of Interaction Primitives. In 2015 IEEE International Conference on Robotics and Automation (ICRA). 1535–1542. https://doi.org/10.1109/ICRA.2015.7139393Google ScholarCross Ref
- A. D. Wilson and A. F. Bobick. 1999. Parametric hidden Markov models for gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 9(1999), 884–900. https://doi.org/10.1109/34.790429Google ScholarDigital Library
- J. Mainprice and D. Berenson. 2013. Human-robot collaborative manipulation planning using early prediction of human motion. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. 299–306. https://doi.org/10.1109/IROS.2013.6696368Google ScholarCross Ref
- L. Bretzner, I. Laptev, and T. Lindeberg. 2002. Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering. In Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition. 423–428. https://doi.org/10.1109/AFGR.2002.1004190Google ScholarCross Ref
- Dana Kulić, Christian Ott, Dongheui Lee, Junichi Ishikawa, and Yoshihiko Nakamura. 2012. Incremental learning of full body motion primitives and their sequencing through human motion observation. The International Journal of Robotics Research 31, 3 (2012), 330–345. https://doi.org/10.1177/0278364911426178Google ScholarDigital Library
- H. S. Koppula and A. Saxena. 2016. Anticipating Human Activities Using Object Affordances for Reactive Robotic Response. IEEE Transactions on Pattern Analysis and Machine Intelligence 38, 1(2016), 14–29. https://doi.org/10.1109/TPAMI.2015.2430335Google ScholarDigital Library
- Yun Jiang and Ashutosh Saxena. 2014. Modeling High-Dimensional Humans for Activity Anticipation using Gaussian Process Latent CRFs, In Robotics: Science and Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence.Google Scholar
- C. Huang and B. Mutlu. 2016. Anticipatory robot control for efficient human-robot collaboration. In 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). 83–90. https://doi.org/10.1109/HRI.2016.7451737Google Scholar
- Hema S. Koppula, Ashesh Jain, and Ashutosh Saxena. 2016. Anticipatory Planning for Human-Robot Teams. Springer International Publishing, Cham, 453–470. https://doi.org/10.1007/978-3-319-23778-7_30Google Scholar
- Gonzalo Ferrer and Alberto Sanfeliu. 2014. Bayesian Human Motion Intentionality Prediction in urban environments. Pattern Recognition Letters 44 (2014), 134 – 140. https://doi.org/10.1016/j.patrec.2013.08.013 Pattern Recognition and Crowd Analysis.Google ScholarDigital Library
- Dirk Helbing and Peter Molnar. 1998. Social Force Model for Pedestrian Dynamics. Physical Review E 51 (05 1998). https://doi.org/10.1103/PhysRevE.51.4282Google Scholar
- Dirk Helbing, Illés Farkas, and Tamás Vicsek. 2000. Simulating dynamical features of escape panic. Nature 407, 6803 (01 Sep 2000), 487–490. https://doi.org/10.1038/35035023Google Scholar
- M. Luber, J. A. Stork, G. D. Tipaldi, and K. O. Arras. 2010. People tracking with human motion predictions from social forces. In 2010 IEEE International Conference on Robotics and Automation. 464–469. https://doi.org/10.1109/ROBOT.2010.5509779Google ScholarCross Ref
- F. Zanlungo, T. Ikeda, and T. Kanda. 2011. Social force model with explicit collision prediction. EPL (Europhysics Letters) 93, 6 (mar 2011), 68005. https://doi.org/10.1209/0295-5075/93/68005Google ScholarCross Ref
- Tetsushi Ikeda, Yoshihiro Chigodo, Daniel Rea, Francesco Zanlungo, Masahiro Shiomi, and Takayuki Kanda. 2012. Modeling and Prediction of Pedestrian Behavior based on the Sub-goal Concept. In Proceedings of Robotics: Science and Systems. Sydney, Australia. https://doi.org/10.15607/RSS.2012.VIII.018Google ScholarCross Ref
- R. Mehran, A. Oyama, and M. Shah. 2009. Abnormal crowd behavior detection using social force model. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. 935–942. https://doi.org/10.1109/CVPR.2009.5206641Google ScholarCross Ref
- C. Wang, Y. Li, S. S. Ge, and T. H. Lee. 2016. Adaptive control for robot navigation in human environments based on social force model. In 2016 IEEE International Conference on Robotics and Automation (ICRA). 5690–5695. https://doi.org/10.1109/ICRA.2016.7487791Google ScholarDigital Library
- Edward Twitchell Hall. 1990. The hidden dimension. Anchor Books.Google Scholar
- J. Yuan, H. Chen, F. Sun, and Y. Huang. 2015. Multisensor Information Fusion for People Tracking With a Mobile Robot: A Particle Filtering Approach. IEEE Transactions on Instrumentation and Measurement 64, 9(2015), 2427–2442. https://doi.org/10.1109/TIM.2015.2407512Google ScholarCross Ref
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