Evaluation functions synthesis for optimal design of hyper-redundant robotic systems

Simultaneous structure/control optimization in a robotic system design is addressed through Genetic Algorithms. Both aspects are here evolved in the same algorithm through simulations for task oriented evaluations. Moreover, a technique based on Genetic Programming is proposed to generate approximated evaluation functions. Its aim is to significantly speed the design process up, while leading to robust evaluation. A specific adaptation of these principles is investigated for the design of hyper-redundant robotic systems such as smart active endoscopes intended for minimally invasive surgery. The design of these micro-robots is based on a serial arrangement of articulated rings with associated antagonist SMA micro-actuators, whose configuration has to be adapted to the surgical operation constraints. The control strategies for an adaptation of the system geometry to the environment are based on a multi-agent approach to minimize the inter-module communication requirements. The results obtained for the particular application of colonoscopy show the consistency of the solutions.