-Autonomous adaptation in robots has become recognised as crucial for devices deployed in remote or inhospitable environments. The aim of this work is to investigate autonomous robot adaptation, focussing on damage recovery and adaptation to unknown environments. An embodied evolutionary algorithm is introduced and its capabilities demonstrated with experimental results. This algorithm is shown to be able to control the motion of a robot snake effectively; this same algorithm inherently recovers the snake's motion after damage. Another experiment shows that the algorithm is capable of contorting a shape-changing antenna in such a way as to minimise the affect of background noise on it, thus allowing the antenna to achieve a better signal.

Abstract. Robots that can recover from damage did not exist outside science fiction. Here we describe a self-adaptive snake robot that uses shape memory alloy as muscles and an evolutionary algorithm as a method of adaptive control. Experiments demonstrate that if some of the robot's muscles are deliberately damaged, evolution is able to find new sequences of muscle activations that compensate, thus enabling the robot to recover its ability to move.

Abstract. The objective of this work is to investigate how effective smart materials are for generating the motion of a robot. Because of the unique method of locomotion, an evolutionary algorithm is used to evolve the best combination of smart wire activations to move most efficiently. For this purpose, a robot snake was built that uses Nitinol wire as muscles in order to move. The most successful method of locomotion that was evolved, closely resembled the undulating motion of the cobra snake. During experimentation, one of the four Nitinol wires snapped, and the algorithm then enabled adaptive behaviour by the robot by evolving another sequence of muscle activations that more closely resembled the undulations exhibited by the earthworm.

The aim of this work is to utilize an evolutationary algorithm to evolve the microstructure of an object created by a stereolithography machine. This should be optimised to be able to withstand loads applied to it while at the same time minimizing its overall weight. A two part algorithm is proposed which evolves the topology of the structure with a genetic algorithm, while calculating the details of the shape with a separate, deterministic, iterative process derived from standard principles of structural engineering. The division of the method into two separate processes allows both flexibility to changed design parameters without the need for re-evolution, and scalability of the microstructure to manufacture objects of increasing size. The results show that a structure was evolved that was both light and stable. The overall shape of the evolved lattice resembled a honeycomb structure that also satisfied the restrictions imposed by the stereolithography machine.

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