Great interest has been shown in the application of the principles of artificial life to physically embedded systems such as mobile robots, computer networks, home devices able continuously and autonomously to adapt their behavior to changes of the environments. At the same time researchers have been working on the development of evolvable hardware, and new integrated circuits that are able to adapt their hardware autonomously and in real time in a changing environment. This article describes the navigation task for a real mobile robot and its implementation on evolvable hardware. The robot must track a colored ball, while avoiding obstacles in an environment that is unknown and dynamic. Although a model-free evolution method is not feasible for real-world applications due to the sheer number of possible interactions with the environment, we show that a model-based evolution can reduce these interactions by two orders of magnitude, even when some of the robot's sensors are blinded, thus allowing us to apply evolutionary processes online to obtain a self-adaptive tracking system in the real world, when the implementation is accelerated by the utilization of evolvable hardware.

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