Bio-inspired adaptive sensing
Sensor array calibration is a major problem in engineering, to which a biological approach may provide alternative solutions. For animals, perception is relative. The aim of this thesis is to show that the relativity of perception in the animal kingdom could also be applied to robotics with promising results. This thesis explores through various behaviours and environments the properties of homeostatic mechanisms in sensory cells. It shows not only that the phenomenon can solve partial failure of sensors but also that it can be used by robots to adapt to their (changing) environment. Moreover the system shows emergent properties as well as adaptation to the robot body or its behaviour. The homeostatic mechanisms in biological neurons maintain fi ring activity between predefi ned ranges. Our model is designed to correct out of range neuron activity over a relatively long period of time (seconds or minutes). The system is implemented in a robot’s sensory neurons and is the only form of adaptability used in the central network. The robot was fi rst tested extensively with a mechanism implemented for obstacle avoidance and wall following behaviours. The robot was not only able to deal with sensor manufacture defects, but to adapt to changing environments (e.g. adapting to a narrow environment when it was originally in an open world). Emergence of non-implemented behaviours has also been observed. For example, during wall following behaviour, the robot seemed, at some point, bored. It changed the direction it was following the wall. Or we also noticed during obstacle avoidance an emerging exploratory behaviour. The model has also been tested on more complex behaviours such as skototaxis, an escape response, and phonotaxis. Again, especially with skototaxis, emergent behaviours appeared such as unpredictability on where and when the robot will be hiding. It appears that the adaptation is not only driven by the environment but by the behaviour of the robot too. It is by the complex feedback between these two things that non-implemented behaviours emerge. We showed that homeostasis can be used to improve sensory signal processing in robotics and we also found evidence that the phenomenon can be a necessary step towards better behavioural adaptation to the environment.