Online adaptation of robots controlled by nanowire networks

The use of current computational systems is sometimes problematic in robotics, as their cost and power consumption might be too high to find space in low-cost robots. Additionally, their computational capabilities are notable, but not always suited for real-time operations. These limits drastically reduce the amount of applications that can be envisioned. A basic example is the use of micro and nanobots, that even add the issue of size. The need to find different technologies is therefore strong. Here, one alternative computational system is proposed and used: Nanowire Networks. These are novel types of electrical circuits, able to show dynamical properties. Their low-cost and consumption, in addition to their high computational capabilities, make them a perfect candidate for robotic applications. Here, this possibility is assessed, evaluating their use as robots controllers. This research begins with preliminary studies on their behaviour, and considerations about their use. The initial analysis is then used to define an online, adaptive learning approach, allowing the robot to exploit the network to adapt to different tasks and environments. The tested capabilities are: a simple collision avoidance, with fault tolerance considerations; a fast, reactive behaviour to avoid illegal areas; a memory aware behaviour, that can navigate a maze according to an initial stimulus. The results support the promising capabilities of the robotic controller. Additionally, the power of the online adaptation is clearly shown. Therefore, this thesis paves the way for a new type of computation in the robotic area, allowing plastic, fault-tolerant, cheap and efficient systems to be developed.