Abstract
A positioning controller based on Spiking Neural Networks for sensor fusion suitable to run on a neuromorphic computer is presented in this work. The proposed framework uses the paradigm of reservoir computing to control the collaborative robot BAXTER. The system was designed to work in parallel with Liquid State Machines that performs trajectories in 2D closed shapes. In order to keep a felt pen touching a drawing surface, data from sensors of force and distance are fed to the controller. The system was trained using data from a Proportional Integral Derivative controller, merging the data from both sensors. The results show that the LSM can learn the behavior of a PID controller on different situations.
DOI
10.1109/i2mtc.2017.7969728
Publication Date
2017-05-01
Event
2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
Publication Title
2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
Publisher
IEEE
Embargo Period
2024-11-22
Recommended Citation
Alberto Sala, D., Joao Brusamarello, V., de Azambuja, R., & Cangelosi, A. (2017) 'Positioning control on a collaborative robot by sensor fusion with liquid state machines', 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), . IEEE: Available at: https://doi.org/10.1109/i2mtc.2017.7969728
