RT journal article
T1 A Neuromorphic Vision and Feedback Sensor Fusion Based on Spiking Neural Networks for Real-Time Robot Adaption
A1 López Osorio, Pablo
A1 Domínguez Morales, Juan P.
A1 Pérez Peña, Fernando
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
A2 Ingeniería Informática
AB For some years now, the locomotion mechanisms used by vertebrate animals havebeen a major inspiration for the improvement of robotic systems. These mechanisms range from adapting their movements to move through the environment tothe ability to chase prey, all thanks to senses such as sight, hearing, and touch.Neuromorphic engineering is inspired by brain problem-solving techniques withthe goal of implementing models that take advantage of the characteristics ofbiological neural systems. While this is a well-defined and explored area in thisfield, there is no previous work that fuses analog and neuromorphic sensors tocontrol and modify robotic behavior in real time. Herein, a system is presentedbased on spiking neural networks implemented on the SpiNNaker hardwareplatform that receives information from both analog (force-sensing resistor) anddigital (neuromorphic retina) sensors and is able to adapt the speed and orientationof a hexapod robot depending on the stability of the terrain where it is located andthe position of the target. These sensors are used to modify the behavior of different spiking central pattern generators, which in turn will adapt the speed andorientation of the robotic platform, all in real time. In particular, experiments showthat the network is capable of correctly adapting to the stimuli received from thesensors, modifying the speed and heading of the robotic platform
PB Advanced Science News
SN 2640-4567
YR 2024
FD 2024
LK http://hdl.handle.net/10498/32686
UL http://hdl.handle.net/10498/32686
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 09-may-2026