Dr Sean Anderson

Senior Lecturer
The University of Sheffield

s.anderson@sheffield.ac.uk

Themes

Sean is a senior lecturer working in robotics, system identification and control engineering at the University of Sheffield. He led the robot mapping and localisation work for in-pipe robots as a Co-I on EPSRC project Assessing the Underworld (EP/K021699/1). He was recently a Co-I on EPSRC project Bioinspired control of electroactive polymers for next generation soft robots (EP/I032533/1). He currently leads themes on reinforcement learning for action discovery and internal models for forward emulation in driverless cars on EU H2020 project Dreams4Cars (www.dreams4cars.eu).

Related Publications

  1. Ma, K., Schirru, M. M., Zahraee, A. H., Dwyer-Joyce, R., Boxall, J., Dodd, T. J., Collins, R. & Anderson, S. R. (2017). Robot mapping and localisation in metal water pipes using hydrophone induced vibration and map alignment by dynamic time warping. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2548-2553). IEEE.
  2. Ma, K., Schirru, M., Zahraee, A. H., Dwyer-Joyce, R., Boxall, J., Dodd, T. J., Collins, R. & Anderson, S. R. (2017). PipeSLAM: Simultaneous localisation and mapping in feature sparse water pipes using the Rao-Blackwellised particle filter. In 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 1459-1464). IEEE.
  3. Georgiou, C., Anderson, S., & Dodd, T. (2017). Constructing informative Bayesian map priors: A multi-objective optimisation approach applied to indoor occupancy grid mapping. The International Journal of Robotics Research, 36(3), 274-291.
  4. Ma, K., Zhu, J., Dodd, T. J., Collins, R., & Anderson, S. R. (2015). Robot mapping and localisation for feature sparse water pipes using voids as landmarks. In Conference Towards Autonomous Robotic Systems (pp. 161-166). Springer, Cham.
  5. Aram, P., Kadirkamanathan, V., & Anderson, S. R. (2015). Spatiotemporal system identification with continuous spatial maps and sparse estimation. IEEE Transactions on Neural Networks and Learning Systems, 26(11), 2978-2983.