Evolutionary multi-objective inverse kinematics on highly articulated and humanoid robots

Abstract

While solving inverse kinematics on serial kinematic chains is well researched, many methods still seem rather limited in jointly handling more complex geometries, including dexterous multi-finger hands or humanoid robots. In particular, object manipulation and motion tasks would benefit from the ability to define intermediate goals along the kinematic chains, such as an elbow position or wrist orientation. In this paper, we propose a fast hybrid evolutionary approach that is capable of solving inverse kinematics for multiple end effectors simultaneously, leaving high flexibility for specifying full-body postures with different objectives. Accurate solutions can be found in real-time and suboptimal extrema are robustly avoided. Our experimental results on the NASA Valkyrie and Shadow Dexterous Hand demonstrate that the algorithm is fast and can be efficiently applied for different robotic tasks which require flexible control of fully-constrained geometries.