Kinematic arrangement optimization of a quadruped robot with genetic algorithms

Official URL: http://risc01.sabanciuniv.edu/record=b1818057 (Table of Contents)

Quadruped robots are capable of performing a multitude of tasks like walking, running carrying and jumping. As research on quadruped robots grows, so does the variety of the designs available. These designs are often inspired by nature and finalized around technical constraints that are different for each project. A load carrying robot design will take its inspiration from a mule, while a running robot will use a cheetah-like design. However, this technique might be too broad when approaching a designing process for a quadruped robot aimed to accomplish certain tasks with varying degrees of importance. In order to reach an efficient design with precise link lengths and joint positions, for some specific task at hand, a complex series of problems have to be solved. This thesis proposes to use genetic algorithms to handle the designing process. An approach that mimics the evolutionary process of living beings, genetic algorithms can be used to reach quadruped designs which are optimized for a given task. The task-specific nature of this process is expected to result in more efficient designs than simply mimicking 4 animal structures, since animals are evolved to be efficient in a bigger variety of tasks. To explore this, genetic algorithms are used to optimize the kinematic structure of quadruped robots designed for the tasks of vertical jumping and trotting. The robots are optimized for these two tasks separately and then together. Algorithm results are compared to a relatively more conventional quadruped design.