An Evolutionary Computational Approach to Humanoid Motion Planning

The theme of our work is centred on humanoid motion planning and balancing using evolutionary computational techniques. Evolutionary techniques, inspired by the Darwinian evolution of biological systems, make use of the concept of the iterative progress of a population of solutions with the aim of finding an optimally fit solution to a given problem. The problem we address here is that of asymmetric motion generation for humanoids, with the aim of automatically developing a series of motions to resemble certain predefined postures. An acceptable trajectory and stability is of the utmost concern in our work. In developing these motions, we are utilizing genetic algorithms coupled with heuristic knowledge of the problem domain. Unlike other types of robots, humanoids are complex in both construction and operation due to their myriad degrees of freedom and the difficulty of balancing on one or more limbs. The work presented in this paper includes the adopted methodology, experimental setup, results and an analysis of the outcome of a series of evolutionary experiments conducted for generating the said asymmetric motions.