Memetic Evolution for Generic Full-Body Inverse Kinematics in Robotics and Animation

摘要

In this paper, a novel and fast memetic evolutionary algorithm is presented which can solve fully constrained generic inverse kinematics with multiple end effectors and goal objectives, leaving high flexibility for the design of custom cost functions. The algorithm utilizes a hybridization of evolutionary and swarm optimization, combined with the limited-memory-Broyden-Fletcher-Goldfarb-Shanno with bound constraints algorithm for gradient-based optimization. Accurate solutions can be found in real-time and suboptimal extrema are robustly avoided, scaling well even for greatly higher degree of freedom. The algorithm provides a general framework for hounded continuous optimization which only requires two parameters for the number of individuals and elites to be set, and supports adding additional goals and constraints for inverse kinematics, such as minimal displacement between solutions, collision avoidance, or functional joint relations. Experimental results on several industrial and anthropomorphic robots as well as on virtual characters demonstrate the algorithm to be applicable for solving complex kinematic postures for different challenging tasks in robotics, human-robot interaction and character animation, including dexterous object manipulation, collision-free full-body motion, as well as animation post-processing for video games and films. Implementations are made available for Unity3D and robot operating system.