A memetic evolutionary algorithm for real-time articulated kinematic motion

Abstract

Solving kinematic motion is a challenging field of research which is relevant for various applications in character animation and robotics. This paper presents a novel and fast hybrid evolutionary algorithm for inverse kinematics which can handle fully constrained and highly articulated geometries with multiple end effectors and individual objectives. Several experiments on the 42 DoF human body mannequin and other kinematic models demonstrate a robust multimodal and multi-objective optimisation, and the ability to evolve accurate solutions in real-time while offering maximum flexibility for the design of custom cost functions.