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An adaptive modular recurrent cerebellum-inspired controller (2017)
Conference Proceeding
Maheri, K., Lenz, A., & Pearson, M. J. (2017). An adaptive modular recurrent cerebellum-inspired controller. In M. Mangan, C. Mark, A. Mura, P. Verschure, T. Prescott, & L. Nathan (Eds.), https://doi.org/10.1007/978-3-319-63537-8_23

Animals and robots face the common challenge of interacting with an unstructured environment. While animals excel and thrive in such environments, modern robotics struggles to effectively execute simple tasks. To help improve performance in the face... Read More about An adaptive modular recurrent cerebellum-inspired controller.

Feather-inspired sensor for stabilizing unmanned aerial vehicles in turbulent conditions (2017)
Book Chapter
Kouppas, C., Pearson, M. J., Dean, P., & Anderson, S. (2017). Feather-inspired sensor for stabilizing unmanned aerial vehicles in turbulent conditions. In M. Mangan, M. Cutkosky, A. Mura, P. Verschure, T. Prescott, & N. Lepora (Eds.), Biomimetic and Biohybrid Systems, 230-241. Springer link

Stabilizing unmanned aerial vehicles (UAVs) in turbulent conditions is a challenging problem. Typical methods of stabilization do not use feedforward information about the airflow disturbances but only UAV attitude feedback signals, e.g. from an iner... Read More about Feather-inspired sensor for stabilizing unmanned aerial vehicles in turbulent conditions.

Self-adaptive context aware audio localization (2017)
Book Chapter
Baxendale, M., Pearson, M. J., Nibouche, M., Secco, M., & Pipe, A. G. (2017). Self-adaptive context aware audio localization. In C. Lekakou, Y. Jin, S. Fallah, & G. Yang (Eds.), Towards Autonomous Robotic Systems, 66-78. Springer link

An audio sensor system is presented that uses multiple cere- bellar models to determine the acoustic environment in which a robot is operating, allowing the robot to select appropriate models to calibrate its audio-motor map for the detected environ... Read More about Self-adaptive context aware audio localization.