Abstract
In this paper, we introduce a novel scheme for tracking moving person based on particle filter and social force model. The tracking process contains two parts: the predict model and the decision model. We adopt the particle filter algorithm to predict the position and velocity of human. According to the result of prediction, we adapt a sophisticated motion model to calculate the value of social force. Finally, we can control the velocity of robot dynamically through the value of social force.
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Acknowledgment
This work is supported by Shanghai University, and we would like to appreciate the senior engineer, Dr. Wanmi Chen for the support of our paper. We also would like to appreciate the equipment and experiment experience supported by Shanghai Robotics Society.
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Wang, Y., Chen, W., Luo, Y. (2017). People Tracking in Unknown Environment Based on Particle Filter and Social Force Model. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10464. Springer, Cham. https://doi.org/10.1007/978-3-319-65298-6_31
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DOI: https://doi.org/10.1007/978-3-319-65298-6_31
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