Application of arachnid prey localisation theory for a robot sensorimotor controller

We extend an existing spiking neural model of arachnid prey orientation sensing with a view to potentially using it in robotics applications. Firstly, we have added ‘motor’ behaviour by implementing a simulated arachnid in a physics simulation so that sensory signals from the neural model can be translated into movement to orient towards the prey. We have also created a spiking neural distance estimation model with a complementary motor model that enables walking towards the prey. Results from testing of the neural and motor aspects show that the neural models can represent actual prey angle and distance to a high degree of accuracy: an average error of approximately 7° in estimating prey angle and 1 cm in the estimation of distance to prey. The motor models consistently show the correct turning and walking responses but the overall accuracy is reduced with an average error of around 15° for angle and 1.25 cm for distance. In the case of orientation this is still in line with the error rate of between 12° and 15°, which has been observed in real arachnids.